RETRACTED ARTICLE: Deep learning approaches to detect breast cancer: a comprehensive review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

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

Physics-informed machine learning for advancing computational medical imaging: integrating data-driven approaches with fundamental physical principles

Ahmadi, M., Biswas, D., Lin, M., Vrionis, F. D., Hashemi, J., & Tang, Y. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11303-w · Cited by 57

The role of explainable AI in enhancing breast cancer diagnosis using machine learning and deep learning models

Ansari, Z. A., Tripathi, M. M., & Ahmed, R. (2025) · Discover Artificial Intelligence · doi:10.1007/s44163-025-00307-8 · Cited by 53

Super perfect polarization-insensitive graphene disk terahertz absorber for breast cancer detection using deep learning

Zamzam, P., Rezaei, P., Khatami, S. A., & Appasani, B. (2024) · Optics & Laser Technology · doi:10.1016/j.optlastec.2024.112246 · Cited by 42

Advancements in Breast Cancer Detection: A Review of Global Trends, Risk Factors, Imaging Modalities, Machine Learning, and Deep Learning Approaches

Rahman, M. A., Khan, M., Watanobe, Y., Prioty, J. T., Annita, T. T., Rahman, S., Hossain, M. S., Aitijjo, S. A., Taskin, R. I., Dhrubo, V., et al. (2025) · BioMedInformatics · doi:10.3390/biomedinformatics5030046 · Cited by 40

Adaptive deep Q-GAN framework for enhanced breast cancer detection in medical imaging

Basith, M. D., Praveen, P., & Reddy, P. C. S. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108638 · Cited by 29

Enhancing Early Breast Cancer Detection with Infrared Thermography: A Comparative Evaluation of Deep Learning and Machine Learning Models

Jalloul, R., Krishnappa, C. H., Ikechukwu, A. V., & Alkhatib, R. (2024) · Technologies · doi:10.3390/technologies13010007 · Cited by 28

Advancing explainable AI in healthcare: Necessity, progress, and future directions

Mohapatra, R., Jolly, L., & Dakua, S. (2025) · Comput. Biol. Chem. · doi:10.1016/j.compbiolchem.2025.108599 · Cited by 23

Detecting and classifying breast masses via YOLO-based deep learning

Karaca, B. K., Telatar, Z., Güney, S., & Dengiz, B. (2025) · Neural Computing and Applications · doi:10.1007/s00521-025-11153-1 · Cited by 22

Enhanced Interpretability in Breast Cancer Detection: Combining Grad‐ CAM With Selective Layer Freezing in Deep Learning

Nesheli, S. J., Sabet, M., Firoozi, V., Heydarheydari, S., & Rezaeijo, S. M. (2025) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.70151 · Cited by 18

Enhanced machine learning models for accurate breast cancer mammogram classification

Puttegowda, K., Veeraprathap, V., Kumar, H. S. R., Sudheesh, K. V., Prabhavathi, K., Ravi, V., & Tabianan, K. (2025) · Global Transitions · doi:10.1016/j.glt.2025.04.007 · Cited by 16

A review of breast cancer histopathology image analysis with deep learning: Challenges, innovations, and clinical integration

Haq, I., Gong, Z., Liang, H., Zhang, W., Khan, R., Gu, L., Eils, R., Kang, Y., & Huang, B. (2025) · Image and Vision Computing · doi:10.1016/j.imavis.2025.105708 · Cited by 16

Analytical strategies in early breast cancer diagnostic biomarker discovery by machine learning methods: Promises, advances and outlooks

Naghib, S. M., Khorasani, M. A., Sharifianjazi, F., & Tavamaishvili, K. (2025) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2025.118412 · Cited by 11

Efficient Attentive U-Net for Fault Diagnosis and Predictive Maintenance of Photovoltaic Panels Through Infrared Thermography

Pratticò, D., Laganà, F., Versaci, M., Franković, D., Jakoplić, A., & Foresta, F. L. (2025) · Energies · doi:10.3390/en18246472 · Cited by 12

Examination of Breast Cancer Histology Images with MobileNet and NASNet Models

Rajinikanth, V. (2025) · International Conference on Recent Innovation in Science Engineering and Technology (ICRISET) · doi:10.1109/icriset64803.2025.11252414 · Cited by 10

Deep Learning Framework for Breast Cancer Detection and Segmentation Using EfficientNet and U-Net with Hyperparameter Optimization

Abdel-Wahab, A., Pulipati, V., Joshi, S., Srikanteswara, R., Menon, S. P., & Dutta, P. (2025) · SN Computer Science · doi:10.1007/s42979-025-03900-x · Cited by 10

Sensitivity enhanced terahertz plasmonic metasurface biosensor based on symmetrical graphene–gold–silver resonators for breast cancer biomarker detection

Usha, S., Jalaja, A. A., Anbazhagan, S., & Kumar, U. S. (2026) · Results in Physics · doi:10.1016/j.rinp.2026.108607 · Cited by 9

Artificial intelligence in COVID-19 research: A comprehensive survey of innovations, challenges, and future directions

Annan, R., & Qingge, L. (2025) · Computer Science Review · doi:10.1016/j.cosrev.2025.100751 · Cited by 8

LBNet: an optimized lightweight CNN for mammographic breast cancer classification with XAI-based interpretability

Ahmmed, J., Ahmed, F., Kabir, M. A., Ahad, M. T., Jadoon, M. A., Rehman, A. U., & Bermak, A. (2025) · Scientific Reports · doi:10.1038/s41598-025-31642-6 · Cited by 8

Review on artificial intelligence supported breast cancer diagnosis for effective treatment planning

Narayanan, M., Prabhu, N., & Rajinikanth, V. (2025) · Discover Applied Sciences · doi:10.1007/s42452-025-07920-3 · Cited by 6

Deep learning for multi-modal data fusion in IoT applications

Saghir, A., Akbar, A., Zafar, A., & Hassan, A. (2025) · Mehran University Research Journal of Engineering and Technology · doi:10.22581/muet1982.3171 · Cited by 6

Cold plasma triggered cell death with a curcumin and capecitabine loaded magnetic nanocluster-based multifunctional system on the MCF-7 cell line: a smart therapy platform

Erdag, D., Garrido, M. D., Başoğlu, H., Yazgan, İ., Amorós, P., Yalcintepe, L., & Toprak, M. S. (2025) · Journal of Materials Chemistry B · doi:10.1039/d5tb01738f · Cited by 5

Novel intelligent exogenous neuro-architecture–driven machine learning approach for nonlinear fractional breast cancer risk system

Fida, A., Raja, M. A. Z., Chang, C., Raja, M. J. A. A., Khan, Z. A., & Shoaib, M. (2025) · Communications in Nonlinear Science and Numerical Simulation · doi:10.1016/j.cnsns.2025.108955 · Cited by 5

Artificial Intelligence (AI)-Assisted Treatment of Breast Cancer

Mubasshira, Rahman, M. M., Mondal, J., Parvez, M. M. H., Uddin, M. N., & Akter, L. (2026) · Springer eBooks · doi:10.1007/978-981-95-3682-5_18 · Cited by 5

The Role of Machine Learning and Deep Learning in Revolutionizing Healthcare: A Review

Ahmed, W. (2025) · Premier Journal of Artificial Intelligence · doi:10.70389/pjai.100010 · Cited by 3

ResMobileNet: A Deep Ensemble Approach for Classification of Breast Cancer Using Transfer Learning

Khattar, S., & Kumar, V. (2025) · International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT) · doi:10.1109/idciot64235.2025.10915153 · Cited by 3

TinyVit-LightGBM: A lightweight and smart feature fusion framework for IoMT-based cancer diagnosis

Wang, H., Dai, X., Ning, S., Ye, J., Srivastava, G., Khan, F., Shah, S. T. U., & You, P. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103180 · Cited by 3

Employing transfer learning for breast cancer detection using deep learning models

Twum, F., Ahiable, C. C. E., Oppong, S. O., Amoako-Banning, L., & Owusu-Agyemang, K. (2025) · PLOS Digital Health · doi:10.1371/journal.pdig.0000907 · Cited by 3

Multimodal Machine Learning Approaches for Early Detection and Classification of Breast Cancer Using Imaging and Genomic Data

Babu, T., Nair, R. R., Manjula, M., Soans, V. O., & Shree, C. (2025) · International Conference on Biomedical Engineering and Sustainable Healthcare (ICBMESH) · doi:10.1109/icbmesh66209.2025.11182194 · Cited by 4

Deep learning advances in breast medical imaging with a focus on clinical readiness and radiologists’ perspective

Oladimeji, O. O., Imran, A.-A.-Z., Wang, X., & Unnikrishnan, S. (2025) · Image and Vision Computing · doi:10.1016/j.imavis.2025.105601 · Cited by 5

Enhanced Multi-Layer Graphene-Metal Terahertz Biosensor with Machine Learning Optimization for Early-Stage Breast Cancer Detection

Elsayed, H. A. E. A., Mehaney, A., Rajakannu, A., Wekalao, J., Alqhtani, H. A., Bin-Jumah, M. N., & Kabarokole, P. (2025) · Journal of Electronic Materials · doi:10.1007/s11664-025-12489-z · Cited by 2

HiGATE: hierarchical graph attention for multi-scale tissue encoder in computational pathology

Dad, I., He, J., & Shen, T. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1798040 · Cited by 2

Hybrid Framework for Breast Cancer Classification Using Deep Feature Extraction and Chebyshev Fossa Optimization Based Feature Selection

Elumalai, S., & Surendran, R. (2025) · International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) · doi:10.1109/i-smac65734.2025.11597847 · Cited by 2

Decoding the Role of CDCA Genes in Breast Cancer Progression: Insights From in Silico and Functional Assay

Zhao, Y., Ma, X., & Zhou, J. (2025) · Asia-Pacific Journal of Clinical Oncology · doi:10.1111/ajco.14173 · Cited by 2

A Comprehensive Review on Breast Cancer Detection and Using Machine Learning Techniques: Methods, and Challenges Ahead

Parhizkari, M., Shabanzadeh, M., Abbasi, F., & Saremi, S. (2025) · Advances in Applied NanoBio-Technologies · doi:10.18502/aanbt.v6i1.18227 · Cited by 2

Deep Learning for Predicting Invasive Ductal Carcinoma in Histopathological Tissue Images

Appavu, N. (2025) · International Conference on Bio Signals, Images, and Instrumentation (ICBSII) · doi:10.1109/icbsii65145.2025.11013109 · Cited by 2

Smart Iridology: Deep Learning for Predictive Health Insights

Avhad, V. V., & Bakal, J. W. (2024) · International Conference on Recent Advances in Science and Engineering Technology (ICRASET) · doi:10.1109/icraset63057.2024.10895403 · Cited by 1

Comprehensive Performance Evaluation of Deep Learning Models for Optimizing Breast Cancer Classification on Mammogram Datasets

Akhter, S., & Liu, J. (2025) · IEEE World AI IoT Congress (AIIoT) · doi:10.1109/aiiot65859.2025.11105244 · Cited by 1

Multimodal Vision-Language Transformer for Thermography Breast Cancer Classification

Pinto, G., León, J., Quintero, B., Villamizar, D., & Rueda-Chacón, H. (2025) · IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI) · doi:10.1109/colcaci67437.2025.11230909 · Cited by 1

BreastHistoNet: A Efficient Breast Cancer Histopathological Image Classification Using Multiscale Features and Channel Recalibration

Deb, D., Dash, R., & Mohapatra, D. P. (2025) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc58623.2025.11253992 · Cited by 1

Implementation of Deep Learning for Breast Cancer Detection: A Systematic Literature Review

Riyadi, S., Chaerani, I. T., Hariadi, T. K., Andriyani, A. D., & Ghifari, N. T. A. (2025) · International Conference on Electronic and Electrical Engineering and Intelligent System (ICE3IS) · doi:10.1109/ice3is66769.2025.11280895 · Cited by 1

Establishing an Evidence-based Modern Breast MRI Program

Aru, M. G., Rahbar, H., Biswas, D., Sujichantararat, S., Dontchos, B. N., Partridge, S. C., & Kazerouni, A. S. (2025) · Journal of Breast Imaging · doi:10.1093/jbi/wbaf082 · Cited by 1

Application of YOLO-v7 and YOLO-v8 Transfer Learning Models in Breast Lesion Classification and Diagnosis

Jiang, K., Wang, Y., He, H., Chen, H., & Shen, H. (2026) · Current Medical Imaging Formerly Current Medical Imaging Reviews · doi:10.2174/0115734056422406251128144702 · Cited by 1

A multi-scale attention-based parallel feature calibration framework for segmentation of multi-modality breast cancer images

Zafar, M., Sharif, M., & Majid, A. (2026) · Egyptian Informatics Journal · doi:10.1016/j.eij.2026.100984 · Cited by 2

Hybrid machine learning fractional-order framework for mammographic tumor characterization integrating growth dynamics and malignancy prediction

Amilo, D., Sadri, K., Nwekwo, C. W., Farman, M. T., Hafez, M. M., Nisar, K. S., & Bayram, M. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01310-3 · Cited by 1

XAI-CAD: towards trustworthy explainable CAD for breast ultrasound diagnosis

Ahmed, I. B., & Amar, C. B. (2026) · Journal of Information and Telecommunication · doi:10.1080/24751839.2026.2666970 · Cited by 1

Benign/Malignant Breast Cancer Detection with Efficient Deep Learning Scheme

Janarthanan, S., Sabri, M. O., Vijayakumar, K., Prabha, Chhab, S., & Nagini, Y. R. V. S. S. (2026) · International Conference on Intelligent Computing (ICONIC) · doi:10.1109/iconic67661.2026.11517711 · Cited by 1

Advance ANN Algorithm and Research Challenges for Breast Cancer Detection and Classification with Evaluation Ni LabVIEW

Haider, N., Mazhar, I., Haider, S., Ghazanfar, J., Zohaib, M. M., & Haider, Z. (2025) · International Conference on Artificial Intelligence, Robotics and Control (AIRC) · doi:10.1109/airc64931.2025.11077485 · Cited by 1

FIRE-Breast: A Firefly Optimization-Driven EfficientNetB0 CNN Model for Optimized Breast Cancer Detection

Ogundokun, R. O., Owolawi, P. A., Adekunle, T. S., & Awoniyi, C. (2024) · IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) · doi:10.1109/iemcon62851.2024.11093134 · Cited by 1

Enhancing breast cancer detection: Integrating deep learning with evolutionary algorithms

Bhavya, G., & Manjunath, T. N. (2025) · International Journal of Systems Assurance Engineering and Management · doi:10.1007/s13198-025-02981-4 · Cited by 1

Thermal Imaging and Infrared Physics in Early Disease Detection

Karam, L. S., Fakhir, A. S., Hadeed, J. I., Manharan, A. A., & Shahid, A. D. (2025) · Bright Sky Publications eBooks · doi:10.62906/bs.book.376

Revolutionizing breast cancer care: the synergy of AI-powered diagnostics, haptic-based biopsy simulators, and advanced surgical techniques

Tichiwangana, T. R., Ji, Q., Fan, X., Ying, T., & Chen, X. (2025) · Expert Review of Medical Devices · doi:10.1080/17434440.2025.2514007

Advancing Breast Cancer Prediction with Machine Learning and Explainable AI Techniques

Amodi, T. M. A. A., & Bajaj, K. (2025) · International Conference on Knowledge Engineering and Communication Systems (ICKECS) · doi:10.1109/ickecs65700.2025.11035930

Breast Cancer Classification via a High-Precision Hybrid IGWO–SOA Optimized Deep Learning Framework

Deka, A., Misra, D. D., Das, A., & Saikia, M. J. (2025) · AI · doi:10.3390/ai6080167

Revolutionizing MRI Tumor Classification: A Fusion of DCNN and Salp Swarm Algorithms

Sen, M. K., Kumari, N., & Chandan, T. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-6718-5_2

Exploring the Role of Deep Learning in Image Analysis for Breast Cancer Diagnosis

Aitouhanni, I., Bakkali, M. E., Choukri, S., Ouardi, B. E., Mouniane, Y., Masmoudi, S., Rektouti, M., Elorch, I., Belomaria, M., & Aouane, M. (2025) · IGI Global eBooks · doi:10.4018/979-8-3693-8789-4.ch015 · Cited by 1

Explainable Deep Learning System for Custom Report Generation in Breast Cancer Histology

Anguita-Molina, M. Á., Civit-Masot, J., Muñoz-Saavedra, L., Polo-Rodríguez, A., & Domínguez-Morales, M. (2025) · Cognitive Computation · doi:10.1007/s12559-025-10510-5

FARKLI KANSER TÜRLERİNDE DERİN ÖĞRENMEYE DAYALI GÖRÜNTÜ ANALİZİ: BİR LİTERATÜR TARAMASI

Ertürkmen, E., Çam, H., & Öter, A. (2025) · Uluslararası Stratejik Boyut Dergisi · doi:10.63742/ustbd.1744186

Label-Efficient Breast Ultrasound Lesion Detection using a Tiny Transformer Encoder (TEBCS-Net)

Azar, N. A. N. (2025) · International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) · doi:10.1109/ismsit67332.2025.11268178

Enhanced Breast Cancer Detection Through Conven-tional and Modern Algorithms

Kumar, G., Manoharan, G., Arora, K., & Kumar, S. (2025) · International Conference on Sustainability, Innovation & Technology (ICSIT) · doi:10.1109/icsit65336.2025.11294920

Decentralized Tumor Classification with Federated Learning: A Privacy-Preserving Approach

Mir, M. N. H., Nagib, M. N., Nova, A. A., Pervez, R., & Hasan, M. N. (2025) · Studies in computational intelligence · doi:10.1007/978-981-96-8353-6_2

Deep Learning Approach Integration with Attention Mechanism for Early Diagnosis of Invasive Ductal Carcinoma

Mogha, S., & Kumari, R. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-10670-4_4

Fracture Finder: Computer-aided real-time diagnosis of vertebral fractures in thoracic spine X-rays using YOLOv8 with Weighted Box Fusion and augmentation strategies

Islam, M. S., Anik, M. A., Wasi, A. T., & Bappy, M. M. (2026) · IISE Transactions on Healthcare Systems Engineering · doi:10.1080/24725579.2025.2598578

Automated Classification of Store‐Operated Calcium Entry Activity and Disease Conditions in Murine Skeletal Muscle Images Using Machine Learning

Binesh, F., Pasham, K. V. R., Villani, K. R., Krekora, U., Wei‐LaPierre, L., & Barton, E. R. (2026) · Muscle & Nerve · doi:10.1002/mus.70157

Localized waves and interaction solution for the fifth-order nonlinear Schrödinger equation from Heisenberg ferromagnetism

Liu, H., Wang, Y., & Qi, F.-H. (2026) · Physica Scripta · doi:10.1088/1402-4896/ae3f61

Fuzzy Gradient-Based Assessment for Robust Forecasting in Uncertain Environments

Ergenç, C., & Aktaş, R. (2026) · Journal of Intelligent & Fuzzy Systems · doi:10.1177/18758967251413997

Picture Media as a Solution for Mathematics Learning: A Classroom Action Study on Fourth Grade Elementary School Students

Winarni, A. T., & Kisyanto, K. (2025) · Interval Indonesian Journal of Mathematical Education · doi:10.37251/ijome.v3i2.2511

AI-Driven Short-Term Solar PV Forecasting for Smart Agricultural Irrigation Systems Using a Novel Hybrid Machine Learning Model

Piridi, S., Asundi, S., Koduri, N. K., Sahoo, S. S., & Garg, A. (2025) · IEEE International Conference on Green Industrial Electronics and Sustainable Technologies (GIEST) · doi:10.1109/giest66547.2025.11387100

Thermal Image Super-Resolution: Trends and Challenges

Rivadeneira, R. E., Sappa, A. D., & Hammoud, R. I. (2026) · ELCVIA Electronic Letters on Computer Vision and Image Analysis · doi:10.5565/rev/elcvia.2374

A Novel Hybrid CNN-ViT-Based Bi-Directional Cross-Guidance Fusion-Driven Breast Cancer Detection Model

Sait, A. R. W., & Alkhurayyif, Y. (2026) · Life · doi:10.3390/life16030474 · Cited by 2

A Hybrid Data-Augmented Deep Learning Pipeline for Breast Tumor Diagnosis

Mounika, V., & Martha, S. (2025) · Asian Conference on Intelligent Technologies (ACOIT) · doi:10.1109/acoit66109.2025.11436977

Quantum Approximate Optimization Algorithm for Reliable Diagnosis of Benign vs. Malignant Breast Tumors

Ghantasala, G. P. (2026) · International Conference on Image Processing and Capsule Networks (ICIPCN) · doi:10.1109/icipcn67432.2026.11438803

Deep Cross-Modal Learning and Multi-Modal Feature Fusion for Automated Detection of Breast Cancer From Mammography and Ultrasound Images

Santhi, M., & Kumar, M. S. (2026) · International Conference on Communication, Computing and Electronics Systems (ICCCES) · doi:10.1109/iccces62661.2026.11436944

FT-MDNNMDs: early detection of breast cancer using fine-tuned multi-deep neural networks with TCGA and clinical image datasets

Shafique, A., Mehmood, A., Al-qudah, R., & Zakaria, K. (2026) · Scientific Reports · doi:10.1038/s41598-026-47731-z

AI and the Evolution of Human Communication

Sasikala, C. S., Vinoth, R., Thilagam, T., Premalatha, G., Manisha, G., & Dinesh, M. G. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-8337-8.ch001

Automatic segmentation of target volume of breast radiotherapy on CT using MSR-UNet: A multicenter study

Zheng, D., Wang, J., Liu, J., Yuan, H., Xue, J., Zeng, H., Zhang, W., Li, X., Cai, J., & Tian, X. (2026) · Applied Radiation and Isotopes · doi:10.1016/j.apradiso.2026.112635

Multimodal Palpation Sensing for Precise Prediction of Breast Lump Hardness and Size

Zang, Y., Xiao, S., Yang, P., Yang, Y., Lu, Z., Sun, R., Wei, C., Zheng, S., Wang, C., Tan, X., et al. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11464121

A Self-Calibrating Soft-Voting Ensemble Model for Reliable Breast Cancer Classification

Yenna, G. R., Vaishnavi, P., Taruni, P., Deepika, P., Amulya, P. G., & Reddy, R. V. (2025) · International Conference Communication, Networks & Computing · doi:10.1109/cnc68716.2025.11484983

Efficient AI-Based breast cancer diagnosis using a pruned Adaptive dilated residual attention network with transformer network for Low-Computational environments

Revathi, K., & V.V.Karthikeyan (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.110553

OAF-CLIP: Ordinal Attention Fusion for Fine-Grained Prior-Guided Breast Ultrasound Benign-Malignant Classification

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