RETRACTED ARTICLE: Deep learning approaches to detect breast cancer: a comprehensive review

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474

Very Deep Convolutional Networks for Large-Scale Image Recognition

Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148

ImageNet: A large-scale hierarchical image database

Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333

Gradient-based learning applied to document recognition

Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805

Going deeper with convolutions

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562

Rethinking the Inception Architecture for Computer Vision

Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138

MobileNetV2: Inverted Residuals and Linear Bottlenecks

Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L.-C. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00474 · Cited by 28,755

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017) · arXiv · doi:10.48550/arxiv.1704.04861 · Cited by 19,015

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

Tan, M., & Le, Q. V. (2019) · arXiv · doi:10.48550/arxiv.1905.11946 · Cited by 17,072

Ror2 signaling regulates Golgi structure and transport through IFT20 for tumor invasiveness

Nishita, M., Park, S.-Y., Nishio, T., Kamizaki, K., Wang, Z., Tamada, K., Takumi, T., Hashimoto, R., Otani, H., Pazour, G. J., et al. (2017) · Scientific Reports · doi:10.1038/s41598-016-0028-x · Cited by 15,947

A survey on Image Data Augmentation for Deep Learning

Shorten, C., & Khoshgoftaar, T. M. (2019) · Journal of Big Data · doi:10.1186/s40537-019-0197-0 · Cited by 13,203

High-performance medicine: the convergence of human and artificial intelligence

Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450

Searching for MobileNetV3

Howard, A., Sandler, M., Chen, B., Wang, W., Chen, L.-C., Tan, M., Chu, G., Vasudevan, V., Zhu, Y., Pang, R., et al. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00140 · Cited by 9,708

Swarm Intelligence

Bonabeau, E. W., Dorigo, M., & Théraulaz, G. (1999) · Oxford University Press eBooks · doi:10.1093/oso/9780195131581.001.0001 · Cited by 6,653

The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups

METABRIC Group, Curtis, C., Shah, S. P., Chin, S.-F., Turashvili, G., Rueda, O. M., Dunning, M. J., Speed, D., Lynch, A. G., Samarajiwa, S., et al. (2012) · Nature · doi:10.1038/nature10983 · Cited by 6,097

A guide to deep learning in healthcare

Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., & Dean, J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0316-z · Cited by 5,312

Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11231 · Cited by 4,636

Dataset of breast ultrasound images

Al-Dhabyani, W., Gomaa, M., Khaled, H., & Fahmy, A. (2020) · Data in Brief · doi:10.1016/j.dib.2019.104863 · Cited by 2,405

MobileNetV2: Inverted Residuals and Linear BottlenecksPreprint

Sandler, M., Howard, A., Zhu, M., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.04381 · Cited by 2,255

A Dataset for Breast Cancer Histopathological Image Classification

Spanhol, F. A., Oliveira, L. S., Petitjean, C., & Heutte, L. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2496264 · Cited by 1,944

Moore's law: past, present and future

Schaller, R. R. (1997) · IEEE Spectrum · doi:10.1109/6.591665 · Cited by 1,931

Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes

Tu, J. V. (1996) · Journal of Clinical Epidemiology · doi:10.1016/s0895-4356(96)00002-9 · Cited by 1,818

INbreast

Moreira, I. C., Amaral, I., Domingues, I., Cardoso, A. N. L., Cardoso, M. J., & Cardoso, J. S. (2011) · Academic Radiology · doi:10.1016/j.acra.2011.09.014 · Cited by 1,256

Ian Goodfellow, Yoshua Bengio, and Aaron Courville: Deep learning

Heaton, J. (2017) · Genetic Programming and Evolvable Machines · doi:10.1007/s10710-017-9314-z · Cited by 1,062

A curated mammography data set for use in computer-aided detection and diagnosis research

Lee, R. S., Gimenez, F., Hoogi, A., Miyake, K. K., Gorovoy, M., & Rubin, D. L. (2017) · Scientific Data · doi:10.1038/sdata.2017.177 · Cited by 859

Breast Cancer Screening With Imaging: Recommendations From the Society of Breast Imaging and the ACR on the Use of Mammography, Breast MRI, Breast Ultrasound, and Other Technologies for the Detection of Clinically Occult Breast Cancer

Lee, C., Dershaw, D. D., Kopans, D. B., Evans, P., Monsees, B., Monticciolo, D. L., Brenner, R. J., Bassett, L. W., Berg, W. A., Feig, S. A., et al. (2010) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2009.09.022 · Cited by 812

BACH: Grand challenge on breast cancer histology images

Aresta, G., Araújo, T., Kwok, S., Chennamsetty, S. S., Safwan, M., Alex, V., Marami, B., Prastawa, M. W., Chan, M. S. M., Donovan, M. J., et al. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.05.010 · Cited by 755

Diagnostic Accuracy of Digital Screening Mammography With and Without Computer-Aided Detection

Lehman, C. D., Wellman, R. D., Buist, D. S. M., Kerlikowske, K., Tosteson, A. N. A., & Miglioretti, D. L. (2015) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2015.5231 · Cited by 678

Computer-Aided Detection and Diagnosis of Breast Cancer With Mammography: Recent Advances

Tang, J., Rangayyan, R. M., Xu, J., Naqa, I. E., & Yang, Y. (2009) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2008.2009441 · Cited by 619

Explainable AI for Healthcare 5.0: Opportunities and Challenges

Saraswat, D., Bhattacharya, P., Verma, A., Prasad, V. K., Tanwar, S., Sharma, G., Bokoro, P. N., & Sharma, R. (2022) · IEEE Access · doi:10.1109/access.2022.3197671 · Cited by 609

Breast cancer detection using automated whole breast ultrasound and mammography in radiographically dense breasts

Kelly, K. M., Dean, J. C., Comulada, W. S., & Lee, S. (2009) · European Radiology · doi:10.1007/s00330-009-1588-y · Cited by 497

Convolutional neural networks for medical image analysis: State-of-the-art, comparisons, improvement and perspectives

Yu, H., Yang, L. T., Zhang, Q., Armstrong, D., & Deen, M. J. (2021) · Neurocomputing · doi:10.1016/j.neucom.2020.04.157 · Cited by 463

Breast cancer detection using deep learning: Datasets, methods, and challenges ahead

Din, N. M. U., Dar, R. A., Rasool, M., & Assad, A. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106073 · Cited by 385

Computer vision for behaviour-based safety in construction: A review and future directions

Fang, W., Love, P. E. D., Luo, H., & Ding, L. (2020) · Advanced Engineering Informatics · doi:10.1016/j.aei.2019.100980 · Cited by 346

A New Database for Breast Research with Infrared Image

Silva, L. F., Saade, D. C. M., Sequeiros, G. O., Silva, A. C., de Paiva, A. C., Bravo, R., & Conci, A. (2013) · Journal of Medical Imaging and Health Informatics · doi:10.1166/jmihi.2014.1226 · Cited by 336

Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis

Li, Y., Xin, W., Yang, P., Jiang, G., & Luo, Y. (2022) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2022.11.003 · Cited by 287

Radiomic versus Convolutional Neural Networks Analysis for Classification of Contrast-enhancing Lesions at Multiparametric Breast MRI

Truhn, D., Schrading, S., Haarburger, C., Schneider, H., Merhof, D., & Kuhl, C. (2019) · Radiology · doi:10.1148/radiol.2018181352 · Cited by 273

Prediction of HER2-positive breast cancer recurrence and metastasis risk from histopathological images and clinical information via multimodal deep learning

Yang, J., Ju, J., Guo, L., Ji, B., Shi, S., Yang, Z., Gao, S., Yuan, X., Tian, G., Liang, Y., et al. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.12.028 · Cited by 233

A GA based hierarchical feature selection approach for handwritten word recognition

Malakar, S., Ghosh, M., Bhowmik, S., Sarkar, R., & Nasipuri, M. (2018) · Neural Computing and Applications · doi:10.1007/s00521-018-3937-8 · Cited by 203

A Deep Learning Method for Breast Cancer Classification in the Pathology Images

Liu, M., Hu, L., Tang, Y. G., Wang, C., He, Y., Zeng, C., Lin, K., He, Z., & Huo, W. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3187765 · Cited by 190

An optimized deep learning architecture for breast cancer diagnosis based on improved marine predators algorithm

Houssein, E. H., Emam, M. M., & Ali, A. A. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07445-5 · Cited by 187

Performance of a Novel Chaotic Firefly Algorithm with Enhanced Exploration for Tackling Global Optimization Problems: Application for Dropout Regularization

Bačanin, N., Stoean, R., Zivkovic, M. Z., Petrović, A., Rashid, T. A., & Bezdan, T. (2021) · Mathematics · doi:10.3390/math9212705 · Cited by 178

Mammographic Image Analysis Society (MIAS) database v1.21

John Suckling, A., Parker, J., Dance, D. A. B., Astley, S., Hutt, I., Boggis, C. R. M., Ricketts, I. W., Stamatakis, E. A., Cerneaz, N., Kok, S. Y., et al. (2015) · Apollo (University of Cambridge) · doi:10.17863/cam.105113 · Cited by 176

Deep Learning Based Capsule Neural Network Model for Breast Cancer Diagnosis Using Mammogram Images

Kavitha, T., Mathai, P. P., Karthikeyan, C., Ashok, M., Kohar, R., Avanija, J., & Neelakandan, S. (2021) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-021-00467-y · Cited by 176

Survey on Machine Learning and Deep Learning Applications in Breast Cancer Diagnosis

Chugh, G., Kumar, S., & Singh, N. (2021) · Cognitive Computation · doi:10.1007/s12559-020-09813-6 · Cited by 172

Explainable AI-driven IoMT fusion: Unravelling techniques, opportunities, and challenges with Explainable AI in healthcare

Wani, N. A., Kumar, R., Mamta, ­., Bedi, J., & Rida, I. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102472 · Cited by 172

End-to-end acoustic modeling using convolutional neural networks for HMM-based automatic speech recognition

Palaz, D., Magimai-Doss, M., & Collobert, R. (2019) · Speech Communication · doi:10.1016/j.specom.2019.01.004 · Cited by 168

Computer-aided breast cancer detection and classification in mammography: A comprehensive review

Loizidou, K., Elia, R., & Pitris, C. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106554 · Cited by 166

Contrast-enhanced digital mammography

Dromain, C., Balleyguier, C., Adler, G., Garbay, J. R., & Delaloge, S. (2008) · European Journal of Radiology · doi:10.1016/j.ejrad.2008.07.035 · Cited by 149

A Hybrid Deep Learning Model for Predicting Molecular Subtypes of Human Breast Cancer Using Multimodal Data

Liu, T., Huang, J., Liao, T., Pu, R., Liu, S., & Peng, Y. (2022) · IRBM · doi:10.1016/j.irbm.2020.12.002 · Cited by 137

Thermal-based early breast cancer detection using inception V3, inception V4 and modified inception MV4

Husaini, M. A. S. A., Habaebi, M. H., Gunawan, T. S., Islam, M. R., Elsheikh, E. A. A., & Suliman, F. M. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06372-1 · Cited by 133

Federated learning aided breast cancer detection with intelligent Heuristic-based deep learning framework

Kumbhare, S., Kathole, A. B., & Shinde, S. V. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105080 · Cited by 118

Multi-Modal Classification for Human Breast Cancer Prognosis Prediction: Proposal of Deep-Learning Based Stacked Ensemble Model

Arya, N., & Saha, S. (2020) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2020.3018467 · Cited by 117

Deep learning-based breast cancer grading and survival analysis on whole-slide histopathology images

Wetstein, S. C., de Jong, V. M. T., Stathonikos, N., Opdam, M., Dackus, G. M. H. E., Pluim, J. P. W., van Diest, P. J., & Veta, M. (2022) · Scientific Reports · doi:10.1038/s41598-022-19112-9 · Cited by 110

Application of Convolutional Neural Network in Natural Language Processing

Wang, W., & Gang, J. (2018) · IEEE International Conference on Information Systems and Computer Aided Education (ICISCAE) · doi:10.1109/iciscae.2018.8666928 · Cited by 97

Machine learning and deep learning techniques for breast cancer diagnosis and classification: a comprehensive review of medical imaging studies

Radak, M., Lafta, H. Y., & Fallahi, H. (2023) · Journal of Cancer Research and Clinical Oncology · doi:10.1007/s00432-023-04956-z · Cited by 97

Recent advancements in machine learning and deep learning-based breast cancer detection using mammograms

Sahu, A., Das, P. K., & Meher, S. (2023) · Physica Medica · doi:10.1016/j.ejmp.2023.103138 · Cited by 97

Deep learning radiopathomics based on preoperative US images and biopsy whole slide images can distinguish between luminal and non-luminal tumors in early-stage breast cancers

Huang, Y., Yao, Z., Li, L., Mao, R., Huang, W., Hu, Z., Hu, Y., Wang, Y., Guo, R., Tang, X., et al. (2023) · EBioMedicine · doi:10.1016/j.ebiom.2023.104706 · Cited by 92

Mammography and ultrasound based dual modality classification of breast cancer using a hybrid deep learning approach

Atrey, K., Singh, B. K., Bodhey, N. K., & Pachori, R. B. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.104919 · Cited by 89

MSRNet: Multiclass Skin Lesion Recognition Using Additional Residual Block Based Fine-Tuned Deep Models Information Fusion and Best Feature Selection

Bibi, S., Khan, M. A., Shah, J. H., Damaševičius, R., Alasiry, A., Marzougui, M., Alhaisoni, M., & Masood, A. (2023) · Diagnostics · doi:10.3390/diagnostics13193063 · Cited by 88

Breast cancer detection and diagnosis using hybrid deep learning architecture

Raaj, R. S. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104558 · Cited by 84

Hybrid deep learning and genetic algorithms approach (HMB-DLGAHA) for the early ultrasound diagnoses of breast cancer

Balaha, H. M., Saif, M., Tamer, A., & Abdelhay, E. H. (2022) · Neural Computing and Applications · doi:10.1007/s00521-021-06851-5 · Cited by 79

Breast cancer detection from thermal images using a Grunwald-Letnikov-aided Dragonfly algorithm-based deep feature selection method

Chatterjee, S., Biswas, S., Majee, A., Sen, S., Oliva, D. A., & Sarkar, R. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.105027 · Cited by 74

Deep Learning-Based Metaheuristic Weighted K-Nearest Neighbor Algorithm for the Severity Classification of Breast Cancer

Chakravarthy, S. R. S., Bharanidharan, N., & Rajaguru, H. (2023) · IRBM · doi:10.1016/j.irbm.2022.100749 · Cited by 73

A comprehensive overview of feature representation for biometric recognition

Rida, I., Almaadeed, N., Al-Maadeed, S. A., & Bakshi, S. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6808-5 · Cited by 72

A comparative study of breast cancer tumor classification by classical machine learning methods and deep learning method

Yadav, Y., & Chand, S. (2020) · Machine Vision and Applications · doi:10.1007/s00138-020-01094-1 · Cited by 72

Breast cancer diagnosis: A systematic review

Wen, X., Guo, X., Wang, S., Lu, Z., & Zhang, Y. (2024) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2024.01.002 · Cited by 72

Computerized Breast Cancer Diagnosis and Prognosis From Fine-Needle Aspirates

Wolberg, W. H. (1995) · Archives of Surgery · doi:10.1001/archsurg.1995.01430050061010 · Cited by 71

Evaluation of deep learning models for detecting breast cancer using histopathological mammograms Images

Mohapatra, S., Muduly, S., Mohanty, S., Ravindra, J. V. R., & Mohanty, S. N. (2022) · Sustainable Operations and Computers · doi:10.1016/j.susoc.2022.06.001 · Cited by 70

Fuzzy ensemble of deep learning models using choquet fuzzy integral, coalition game and information theory for breast cancer histology classification

Bhowal, P., Sen, S., Velásquez, J. D., & Sarkar, R. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.116167 · Cited by 68

Segmentation and classification of breast cancer using novel deep learning architecture

Ramesh, S., Sasikala, S., Gomathi, S., Geetha, V., & Anbumani, V. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07230-4 · Cited by 68

Multi-layer Perceptron Training Using Hybridized Bat Algorithm

Gajic, L., Cvetnić, D., Zivkovic, M. Z., Bezdan, T., Bačanin, N., & Milosevic, S. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-981-33-6862-0_54 · Cited by 66

MTRRE-Net: A deep learning model for detection of breast cancer from histopathological images

Chattopadhyay, S., Dey, A., Singh, P. K., Oliva, D. A., Cuevas, E., & Sarkar, R. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106155 · Cited by 66

Deep learning of longitudinal mammogram examinations for breast cancer risk prediction

Dadsetan, S., Arefan, D., Berg, W. A., Zuley, M. L., Sumkin, J. H., & Wu, S. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108919 · Cited by 62

Deep Learning Signature Based on Staging CT for Preoperative Prediction of Sentinel Lymph Node Metastasis in Breast Cancer

Yang, X., Wu, L., Ye, W., Zhao, K., Wang, Y., Liu, W., Li, J., Li, H., Liu, Z., & Liang, C. (2019) · Academic Radiology · doi:10.1016/j.acra.2019.11.007 · Cited by 61

An extended approach to the diagnosis of tumour location in breast cancer using deep learning

Ghoushchi, S. J., Ranjbarzadeh, R., Najafabadi, S. A., Osgooei, E., & Tırkolaee, E. B. (2021) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-03613-y · Cited by 60

Breast cancer prediction using gated attentive multimodal deep learning

Kayıkçı, Ş., & Khoshgoftaar, T. M. (2023) · Journal Of Big Data · doi:10.1186/s40537-023-00749-w · Cited by 59

Breast cancer detection and classification in mammogram using a three-stage deep learning framework based on PAA algorithm

Jiang, J., Peng, J., Hu, C., Jian, W., Wang, X., & Liu, W. (2022) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102419 · Cited by 57

Authenticating and securing healthcare records: A deep learning-based zero watermarking approach

Anand, A., Bedi, J., Aggarwal, A., Khan, M. A., & Rida, I. (2024) · Image and Vision Computing · doi:10.1016/j.imavis.2024.104975 · Cited by 56

Infrared Imaging for Human Thermography and Breast Tumor Classification using Thermal Images

Farooq, M. A., & Corcoran, P. (2020) · Irish Signals and Systems Conference · doi:10.1109/issc49989.2020.9180164 · Cited by 55

Lightweight and Resource-Constrained Learning Network for Face Recognition with Performance Optimization

Li, H.-C., Deng, Z.-Y., & Chiang, H.-H. (2020) · Sensors · doi:10.3390/s20216114 · Cited by 54

Enhanced firefly algorithm for constrained numerical optimization

Strumberger, I., Bačanin, N., & Tuba, M. (2017) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2017.7969561 · Cited by 53

Prediction of pathologic complete response to neoadjuvant systemic therapy in triple negative breast cancer using deep learning on multiparametric MRI

Zhou, Z., Adrada, B. E., Candelaria, R. P., Elshafeey, N. A., Böge, M., Mohamed, R. M. M., Pashapoor, S., Sun, J., Xu, Z., Panthi, B., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-27518-2 · Cited by 53

Deep learning algorithms for the early detection of breast cancer: A comparative study with traditional machine learning

Martinez, R. M. G., & van Dongen, D.-M. (2023) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2023.101317 · Cited by 53

Deep Learning-Based Multi-Modal Ensemble Classification Approach for Human Breast Cancer Prognosis

Jadoon, E. K., Khan, F. G., Shah, S., Khan, A., & ElAffendi, M. (2023) · IEEE Access · doi:10.1109/access.2023.3304242 · Cited by 52

Multi-Input Deep Learning Approach for Breast Cancer Screening Using Thermal Infrared Imaging and Clinical Data

Tsietso, D., Yahya, A., Samikannu, R., Tariq, M. U., Babar, M. A., Qureshi, B., & Koubâa, A. (2023) · IEEE Access · doi:10.1109/access.2023.3280422 · Cited by 51

Deep Learning-based Multi-stage segmentation method using ultrasound images for breast cancer diagnosis

Cho, S. W., Baek, N. R., & Park, K. R. (2022) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2022.10.020 · Cited by 50

Noninvasive identification of HER2-low-positive status by MRI-based deep learning radiomics predicts the disease-free survival of patients with breast cancer

Guo, Y., Xie, X., Tang, W., Chen, S., Wang, M., Fan, Y., Lin, C., Hu, W., Yang, J., Xiang, J., et al. (2023) · European Radiology · doi:10.1007/s00330-023-09990-6 · Cited by 50

Pretreatment ultrasound-based deep learning radiomics model for the early prediction of pathologic response to neoadjuvant chemotherapy in breast cancer

Yu, F., Miao, S., Li, C., Hang, J., Deng, J., Ye, X., & Liu, Y. (2023) · European Radiology · doi:10.1007/s00330-023-09555-7 · Cited by 49

Deep learning of quantitative ultrasound multi-parametric images at pre-treatment to predict breast cancer response to chemotherapy

Taleghamar, H., Jalalifar, S. A., Czarnota, G. J., & Naini, A. S. (2022) · Scientific Reports · doi:10.1038/s41598-022-06100-2 · Cited by 46

XAI-MethylMarker: Explainable AI approach for biomarker discovery for breast cancer subtype classification using methylation data

Rajpal, S., Rajpal, A., Saggar, A., Vaid, A. K., Kumar, V., Agarwal, M., & Kumar, N., V (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120130 · Cited by 46

A systematic review of machine and deep learning techniques for the identification and classification of breast cancer through medical image modalities

Thakur, N., Kumar, P., & Kumar, A. P. S. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16634-w · Cited by 46

Deep learning-based system for automatic prediction of triple-negative breast cancer from ultrasound images

Boulenger, A., Luo, Y., Zhang, C., Zhao, C., Gao, Y., Xiao, M., Zhu, Q., & Tang, J. (2022) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-022-02728-4 · Cited by 45

Breast cancer classification using snapshot ensemble deep learning model and t-distributed stochastic neighbor embedding

Sharma, N., Sharma, K., Mangla, M., & Rani, R. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13419-5 · Cited by 44

Computer-aided diagnosis of breast cancer in ultrasonography images by deep learning

Qi, X., Yi, F., Zhang, L., Chen, Y., Pi, Y., Chen, Y., Guo, J., Wang, J., Guo, Q., Li, J., et al. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.11.047 · Cited by 43

A novel fusion framework of deep bottleneck residual convolutional neural network for breast cancer classification from mammogram images

Jabeen, K., Khan, M. A., Hameed, M. A., Alqahtani, O., Alouane, M. T.-H., & Masood, A. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1347856 · Cited by 41

B 2 C 3 NetF 2 : Breast cancer classification using an end‐to‐end deep learning feature fusion and satin bowerbird optimization controlled Newton Raphson feature selection

Fatima, M., Khan, M. A., Shaheen, S., Almujally, N. A., & Wang‎, S. (2023) · CAAI Transactions on Intelligence Technology · doi:10.1049/cit2.12219 · Cited by 40

Computer-Aided Diagnosis Scheme for Distinguishing Between Benign and Malignant Masses on Breast DCE-MRI Images Using Deep Convolutional Neural Network with Bayesian Optimization

Hizukuri, A., Nakayama, R., Nara, M., Suzuki, M., & Namba, K. (2020) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-020-00394-2 · Cited by 39

Deep Learning for the Detection of Breast Cancers on Chest Computed Tomography

Koh, J., Yoon, Y., Kim, S., Han, K., & Kim, E. (2021) · Clinical Breast Cancer · doi:10.1016/j.clbc.2021.04.015 · Cited by 36

MITNET: a novel dataset and a two-stage deep learning approach for mitosis recognition in whole slide images of breast cancer tissue

Çayır, S., Solmaz, G., Kusetoğulları, H., Tokat, F., Bozaba, E., Karakaya, S., Iheme, L. O., Tekin, E., Yazıcı, Ç., Özsoy, G., et al. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07441-9 · Cited by 36

Anomaly Detection of Breast Cancer Using Deep Learning

Alloqmani, A., Abushark, Y. B., & Khan, A. I. (2023) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-023-07945-z · Cited by 36

Breast cancer diagnosis based on hybrid rule-based feature selection with deep learning algorithm

Awotunde, J. B., Panigrahi, R., Khandelwal, B., Garg, A., & Bhoi, A. K. (2023) · Research on Biomedical Engineering · doi:10.1007/s42600-022-00255-7 · Cited by 35

Performance of novel deep learning network with the incorporation of the automatic segmentation network for diagnosis of breast cancer in automated breast ultrasound

Wang, Q., Chen, H. H., Luo, G., Li, B., Shang, H., Shao, H., Sun, S., Wang, Z., Wang, K., & Cheng, W. (2022) · European Radiology · doi:10.1007/s00330-022-08836-x · Cited by 32

Ensemble deep learning system for early breast cancer detection

Hekal, A. A., Moustafa, H. E.-D., & Elnakib, A. A. (2022) · Evolutionary Intelligence · doi:10.1007/s12065-022-00719-w · Cited by 32

Deep-learning based breast cancer detection for cross-staining histopathology images

Huang, P., Ouyang, H., Hsu, B.-Y., Chang, Y.-R., Lin, Y.-C., Chen, Y.-A., Hsieh, Y.-H., Fu, C., Li, C., Lin, C., et al. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e13171 · Cited by 32

A New method for promote the performance of deep learning paradigm in diagnosing breast cancer: improving role of fusing multiple views of thermography images

Ensafi, M., Keyvanpour, M. R., & Shojaedini, S. V. (2022) · Health and Technology · doi:10.1007/s12553-022-00702-6 · Cited by 29

Processing of thermal images to detect breast cancer: comparison with previous work

Frize, M., Herry, C. L., & Roberge, R. (2002) · doi:10.1109/iembs.2002.1106325 · Cited by 27

Deep learning-based ensemble model for classification of breast cancer

Nemade, V., Pathak, S. K., & Dubey, A. K. (2023) · Microsystem Technologies · doi:10.1007/s00542-023-05469-y · Cited by 27

BRMI-Net: Deep Learning Features and Flower Pollination-Controlled Regula Falsi-Based Feature Selection Framework for Breast Cancer Recognition in Mammography Images

Rehman, S. U., Khan, M. A., Masood, A., Almujally, N. A., Baili, J., Alhaisoni, M. M., Tariq, U., & Zhang, Y. (2023) · Diagnostics · doi:10.3390/diagnostics13091618 · Cited by 26

Deep learning framework for comprehensive molecular and prognostic stratifications of triple-negative breast cancer

Zhao, S., Yan, C., Lv, H., Yang, J., You, C., Li, Z., Ma, D., Xiao, Y., Hu, J., Yang, W., et al. (2022) · Fundamental Research · doi:10.1016/j.fmre.2022.06.008 · Cited by 25

Microscopic image analysis in breast cancer detection using ensemble deep learning architectures integrated with web of things

Sheeba, A., Kumar, P. S., Ramamoorthy, M., & Sasikala, S. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104048 · Cited by 25

Magnification-specific and magnification-independent classification of breast cancer histopathological image using deep learning approaches

Taheri, S., & Golrizkhatami, Z. (2022) · Signal Image and Video Processing · doi:10.1007/s11760-022-02263-7 · Cited by 24

Medical imaging in rheumatoid arthritis: A review on deep learning approach

Parashar, A., Rishi, R., Parashar, A., & Rida, I. (2023) · Open Life Sciences · doi:10.1515/biol-2022-0611 · Cited by 22

AMIAC: adaptive medical image analyzes and classification, a robust self-learning framework

Iqbal, S., Qureshi, A. N., Aurangzeb, K., Alhussein, M. A., Haider, S. I., & Rida, I. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-09209-1 · Cited by 22

Multimodal microscopic imaging with deep learning for highly effective diagnosis of breast cancer

Wu, J., Xu, Z., Shang, L., Wang, Z., Zhou, S.-W., Shang, H., Wang, H., & Yin, J. (2023) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2023.107667 · Cited by 21

Combining radiomics and deep learning features of intra-tumoral and peri-tumoral regions for the classification of breast cancer lung metastasis and primary lung cancer with low-dose CT

Li, L., Zhou, X., Cui, W., Li, Y., Liu, T., Yuan, G., Peng, Y., & Zheng, J. (2023) · Journal of Cancer Research and Clinical Oncology · doi:10.1007/s00432-023-05329-2 · Cited by 20

CG‐Net : A novel CNN framework for gastrointestinal tract diseases classification

Siddiqui, S., Akram, T., Ashraf, I., Raza, M., Khan, M. A., & Damaševičius, R. (2024) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.23081 · Cited by 20

Application of a Deep-Learning Technique to Non-Linear Images From Human Tissue Biopsies for Shedding New Light on Breast Cancer Diagnosis

Tsafas, V., Oikonomidis, I., Gavgiotaki, E., Tzamali, E., Tzedakis, G., Fotakis, C., Athanassakis, I., & Filippidis, G. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3104002 · Cited by 19

Using deep learning to assist readers during the arbitration process: a lesion-based retrospective evaluation of breast cancer screening performance

Kerschke, L., Weigel, S., Rodríguez‐Ruiz, A., Karssemeijer, N., & Heindel, W. L. (2021) · European Radiology · doi:10.1007/s00330-021-08217-w · Cited by 19

Deep learning model for breast cancer diagnosis based on bilateral asymmetrical detection (BilAD) in digital breast tomosynthesis images

Shimokawa, D., Takahashi, K., Kurosawa, D., Takaya, E., Oba, K., Yagishita, K., Fukuda, T., Tsunoda, H. S., & Ueda, T. (2022) · Radiological Physics and Technology · doi:10.1007/s12194-022-00686-y · Cited by 19

Smart IoT in Breast Cancer Detection Using Optimal Deep Learning

Majji, R., Prakash, P. G. O., Rajeswari, R., & Cristin, R. (2023) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-023-00834-9 · Cited by 19

Deep residual learning with attention mechanism for breast cancer classification

Toa, C. K., Elsayed, M., & Sim, K. S. (2023) · Soft Computing · doi:10.1007/s00500-023-09152-2 · Cited by 18

Breast cancer patient characterisation and visualisation using deep learning and fisher information networks

Ortega‐Martorell, S., Riley, P., Olier, I., Raidou, R. G., Casaña-Eslava, R. V., Rea, M., Shen, L., Lisboa, P. J. G., & Palmieri, C. (2022) · Scientific Reports · doi:10.1038/s41598-022-17894-6 · Cited by 16

Developing a weakly supervised deep learning framework for breast cancer diagnosis with HR status based on mammography images

Zhang, M., Wang, C., Cai, L., Zhao, J., Xu, Y., Xing, J., Sun, J., & Zhang, Y. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.08.012 · Cited by 16

Estimating the effect of an intensive surveillance program on stage of breast carcinoma at diagnosis

Mitra, N., Schnabel, F. R., Neugut, A. I., & Heitjan, D. F. (2001) · Cancer · doi:10.1002/1097-0142(20010501)91:9 · Cited by 15

Breast Cancer Detection from Histopathological Images using Deep Learning and Transfer Learning

Muntean, C. H., & Chowkkar, M. (2022) · International Conference on Machine Learning Technologies (ICMLT) · doi:10.1145/3529399.3529426 · Cited by 15

Composite materials – modelling, prediction and optimization

Aleksendrić, D., & Carlone, P. (2015) · Elsevier eBooks · doi:10.1533/9781782421801.61 · Cited by 13

Deep learning on digital mammography for expert-level diagnosis accuracy in breast cancer detection

Qu, J., Zhao, X., Chen, P., Wang, Z., Liu, Z., Yang, B., & Li, H. (2021) · Multimedia Systems · doi:10.1007/s00530-021-00823-4 · Cited by 13

Outcome and biomarker supervised deep learning for survival prediction in two multicenter breast cancer series

Bychkov, D., Joensuu, H., Nordling, S., Tiulpin, A., Kücükel, H., Lundin, M., Sihto, H. J., Isola, J. J., Lehtimäki, T., Kellokumpu‐Lehtinen, P., et al. (2022) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_29_21 · Cited by 12

CNN-based deep learning method for predicting the disease response to the Neoadjuvant Chemotherapy (NAC) treatment in breast cancer

Kırelli, Y., Arslankaya, S., Koçer, B., & Harmantepe, A. T. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e16812 · Cited by 12

Real-time deployment of BI-RADS breast cancer classifier using deep-learning and FPGA techniques

Maria, H. H., Kayalvizhi, R., Malarvizhi, S., Venkatraman, R., Patil, S. S., & Kumar, A. (2023) · Journal of Real-Time Image Processing · doi:10.1007/s11554-023-01335-2 · Cited by 10

Deep learning model for predicting the presence of stromal invasion of breast cancer on digital breast tomosynthesis

Shimokawa, D., Takahashi, K., Oba, K., Takaya, E., Usuzaki, T., Kadowaki, M., Kawaguchi, K., Adachi, M., Kaneno, T., Fukuda, T., et al. (2023) · Radiological Physics and Technology · doi:10.1007/s12194-023-00731-4 · Cited by 10

Examining the effectiveness of a deep learning-based computer-aided breast cancer detection system for breast ultrasound

Fujioka, T., Kubota, K., Hsu, J. F., Chang, R. F., Sawada, T., Ide, Y., Taruno, K., Hankyo, M., Kurita, T., Nakamura, S., et al. (2023) · Journal of Medical Ultrasonics · doi:10.1007/s10396-023-01332-9 · Cited by 9

Deep Learning Radiomics Nomogram Based on Multiphase Computed Tomography for Predicting Axillary Lymph Node Metastasis in Breast Cancer

Zhang, J., Yin, W. H., Lü, Y., & Yao, X. (2023) · Molecular Imaging and Biology · doi:10.1007/s11307-023-01839-0 · Cited by 9

Deep Learning-Based Computer-Aided Diagnosis Model for the Identification and Classification of Mammography Images

Kumar, S., Bhupati, B., Bhambu, P., Pachar, S., Cotrina-Aliaga, J. C., & Arias‐Gonzáles, J. L. (2023) · SN Computer Science · doi:10.1007/s42979-023-01863-5 · Cited by 5

Deep learning based breast cancer detection system using fog computing

Welhenge, A. M. (2022) · Journal of Discrete Mathematical Sciences and Cryptography · doi:10.1080/09720529.2021.2014130 · Cited by 4

Deep learning-based transcription factor activity for stratification of breast cancer patients

Xiong, Y., Wang, S., Wei, H., Li, H., Lv, Y., Chi, M., Su, D., Lu, Q., Yao, Y., Zuo, Y., et al. (2022) · Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms · doi:10.1016/j.bbagrm.2022.194838 · Cited by 3

Classification of laterality and mastectomy/lumpectomy for breast cancer patients for improved performance of deep learning auto segmentation

Vivancos, H., Stick, L. B., Korreman, S. S., Kronborg, C. J. S., Nielsen, M. M., Borgen, A. C., Offersen, B. V., Nørrevang, O., & Kallehauge, J. F. (2023) · Acta Oncologica · doi:10.1080/0284186x.2023.2245965 · Cited by 2

Potential targets of endocrine-disrupting chemicals related to breast cancer identified by ToxCast and deep learning models

Guan, M., Qi, G., Li, Z., & Hou, X. (2023) · Toxicological & Environmental Chemistry Reviews · doi:10.1080/02772248.2023.2212827 · Cited by 1

Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration

Abbas, S. R., Abbas, Z., Zahir, A., & Lee, S. W. (2024) · Healthcare · doi:10.3390/healthcare12242587 · Cited by 228

An explainable AI-driven deep neural network for accurate breast cancer detection from histopathological and ultrasound images

Alom, M. R., Farid, F. A., Rahaman, M. A., Rahman, A., Debnath, T., Miah, A. S. M., & Mansor, S. B. (2025) · Scientific Reports · doi:10.1038/s41598-025-97718-5 · Cited by 73

A scoping review of the governance of federated learning in healthcare

Eden, R., Chukwudi, I., Bain, C., Barbieri, S., Callaway, L. K., de Jersey, S., George, Y., Gorse, A., Lawley, M., Marendy, P., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01836-3 · Cited by 88

Histopathology in focus: a review on explainable multi-modal approaches for breast cancer diagnosis

Abdullakutty, F. C., Akbari, Y., Al-Maadeed, S. A., Bouridane, A., Talaat, I. M., & Hamoudi, R. (2024) · Frontiers in Medicine · doi:10.3389/fmed.2024.1450103 · Cited by 65

Hierarchical Swin Transformer Ensemble with Explainable AI for Robust and Decentralized Breast Cancer Diagnosis

Ahmed, M. R., Rahman, H., Limon, Z. H., Siddiqui, M. I. H., Khan, M. A., Pranta, A. S. U. K., Haque, R., Swapno, S. M. M. R., Cho, Y. I., & Abdallah, M. S. (2025) · Bioengineering · doi:10.3390/bioengineering12060651 · Cited by 57

Mammogram Analysis for Breast Cancer Detection Using Deep Learning: A Review

Hazheen Sarbast Mahmood, S. F. K. (2025) · Journal of Information Systems Engineering & Management · doi:10.52783/jisem.v10i37s.6372 · Cited by 1

A Comprehensive Survey on Deep Learning Techniques for Breast Cancer Detection

Manusha, C., Ravi, S., P, S. P. T., & Harsha Latha, P. (2025) · International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT) · doi:10.1109/iconscept66142.2025.11436314

Application Progress of Deep Learning in Imaging Examination of Breast Cancer

WANG Yifan, L. J. (2024) · DOAJ (DOAJ: Directory of Open Access Journals) · doi:10.3778/j.issn.1673-9418.2309033

Artificial Intelligence and Deep Learning Models in Breast Cancer Detection: A Decade Trend and Performance Analysis

Sarvasiddi, K., Monica, K. L., Gardi, A., Parveen, M. N., Muthabathula, R. J., Das, P. N., & Surekha, P. V. (2026) · International Journal of Drug Delivery Technology · doi:10.25258/ijddt.16.52s.58

Deep Learning Based Methods for Breast Cancer Diagnosis: A Systematic Review and Future Direction

Nasser, M., & Yusof, U. K. (2023) · Diagnostics · doi:10.3390/diagnostics13010161 · Cited by 249