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Ensemble Deep-Learning-Enabled Clinical Decision Support System for Breast Cancer Diagnosis and Classification on Ultrasound Images
Open accessAbstract
Clinical Decision Support Systems (CDSS) provide an efficient way to diagnose the presence of diseases such as breast cancer using ultrasound images (USIs). Globally, breast cancer is one of the major causes of increased mortality rates among women. Computer-Aided Diagnosis (CAD) models are widely employed in the detection and classification of tumors in USIs. The CAD systems are designed in such a way that they provide recommendations to help radiologists in diagnosing breast tumors and, furthermore, in disease prognosis. The accuracy of the classification process is decided by the quality of images and the radiologist’s experience. The design of Deep Learning (DL) models is found to be effective in the classification of breast cancer. In the current study, an Ensemble Deep-Learning-Enabled Clinical Decision Support System for Breast Cancer Diagnosis and Classification (EDLCDS-BCDC) technique was developed using USIs. The proposed EDLCDS-BCDC technique was intended to identify the existence of breast cancer using USIs. In this technique, USIs initially undergo pre-processing through two stages, namely wiener filtering and contrast enhancement. Furthermore, Chaotic Krill Herd Algorithm (CKHA) is applied with Kapur’s entropy (KE) for the image segmentation process. In addition, an ensemble of three deep learning models, VGG-16, VGG-19, and SqueezeNet, is used for feature extraction. Finally, Cat Swarm Optimization (CSO) with the Multilayer Perceptron (MLP) model is utilized to classify the images based on whether breast cancer exists or not. A wide range of simulations were carried out on benchmark databases and the extensive results highlight the better outcomes of the proposed EDLCDS-BCDC technique over recent methods.
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“| Ragab et al. [101] | Segmented regions from original | ultrasound images Ultrasound Images | - | 96.92% (ACC) |”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [101]
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Cites
Contrast Limited Adaptive Histogram Equalization
Zuiderveld, K. (1994) · Graphics Gems · doi:10.1016/b978-0-12-336156-1.50061-6 · Cited by 4,192
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
Deep Learning to Improve Breast Cancer Detection on Screening Mammography
Shen, L., Margolies, L. R., Rothstein, J. H., Fluder, E., McBride, R., & Sieh, W. (2019) · Scientific Reports · doi:10.1038/s41598-019-48995-4 · Cited by 1,151
Wang, G., Guo, L., Gandomi, A. H., Hao, G., & Wang, H. (2014) · Information Sciences · doi:10.1016/j.ins.2014.02.123 · Cited by 561
Ultrasound Imaging Technologies for Breast Cancer Detection and Management: A Review
Guo, R., Lu, G., Qin, B., & Fei, B. (2017) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2017.09.012 · Cited by 545
Breast Cancer Detection Using Infrared Thermal Imaging and a Deep Learning Model
Mambou, S., Marešová, P., Krejcar, O., Selamat, A., & Kuča, K. (2018) · Sensors · doi:10.3390/s18092799 · Cited by 309
Deeply-Supervised Networks With Threshold Loss for Cancer Detection in Automated Breast Ultrasound
Wang, Y., Wang, N., Xu, M., Yu, J., Qin, C., Luo, X., Yang, X., Wang, T., Li, A., & Ni, D. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2936500 · Cited by 181
Mohammed, M. A., Al‐Khateeb, B., Rashid, A. N., Ibrahim, D. A., Ghani, M. K. A., & Mostafa, S. A. (2018) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2018.01.033 · Cited by 180
Cao, Z., Duan, L., Yang, G., Yue, T., & Chen, Q. (2019) · BMC Medical Imaging · doi:10.1186/s12880-019-0349-x · Cited by 179
Sharma, R. A., Kim, M., & Gupta, A. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.103101 · Cited by 166
Cited by
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Xu, X., Li, J., Zhu, Z., Zhao, L., Wang, H., Song, C., Chen, Y., Zhao, Q., Yang, J., & Pei, Y. (2024) · Bioengineering · doi:10.3390/bioengineering11030219 · Cited by 242
Iqbal, S., Qureshi, A. N., Li, J., & Mahmood, T. (2023) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-023-09899-9 · Cited by 247
Rane, N. L., Choudhary, S. P., & Rane, J. (2024) · Studies in medical and health sciences. · doi:10.48185/smhs.v1i2.1225 · Cited by 150
Ogundokun, R. O., Misra, S., Douglas, M., Damaševičius, R., & Maskeliūnas, R. (2022) · Future Internet · doi:10.3390/fi14050153 · Cited by 114
Kara, G., & Polat, L. Ö. (2024) · Advances in healthcare information systems and administration book series · doi:10.4018/979-8-3693-7277-7.ch006 · Cited by 105
A review on recent developments in cancer detection using Machine Learning and Deep Learning models
Maurya, S., Tiwari, S., Mothukuri, M. C., Tangeda, C. M., Nandigam, R. N. S., & Addagiri, D. C. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104398 · Cited by 98
The Impact of Artificial Intelligence on Microbial Diagnosis
Alsulimani, A., Akhter, N., Jameela, F., Ashgar, R. I., Jawed, A., Hassani, M. A., & Dar, S. A. (2024) · Microorganisms · doi:10.3390/microorganisms12061051 · Cited by 96
A New Deep-Learning-Based Model for Breast Cancer Diagnosis from Medical Images
Zakareya, S., Izadkhah, H., & Karimpour, J. (2023) · Diagnostics · doi:10.3390/diagnostics13111944 · Cited by 84
Mondol, R. K., Millar, E. K. A., Graham, P., Browne, L. H., Sowmya, A., & Meijering, E. H. W. (2023) · Cancers · doi:10.3390/cancers15092569 · Cited by 71
Yan, F., Huang, H., Pedrycz, W., & Hirota, K. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120282 · Cited by 65
Alotaibi, M. O. S., Aljouie, A., Alluhaidan, N., Qureshi, W., Almatar, H., Alduhayan, R., Alsomaie, B., & Almazroa, A. A. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e22406 · Cited by 68
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
Baccouche, A., García-Zapirain, B., & Elmaghraby, A. S. (2022) · Scientific Reports · doi:10.1038/s41598-022-15632-6 · Cited by 54
Deep Learning Approaches for Classification of Breast Cancer in Ultrasound (US) Images
Paçal, İ. (2022) · Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi · doi:10.21597/jist.1183679 · Cited by 60
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
Du, Y., Antoniadi, A. M., McNestry, C., McAuliffe, F. M., & Mooney, C. (2022) · Applied Sciences · doi:10.3390/app122010323 · Cited by 47
Stimuli-responsive switchable MRI nanoprobe for tumor theranostics
Chen, B., Liu, L., Yue, R., Dong, Z., Lu, C., Zhang, C., Guan, G., Liu, H., Zhang, Q., & Song, G. (2023) · Nano Today · doi:10.1016/j.nantod.2023.101931 · Cited by 46
Alkhalaf, S., Alturise, F., Bahaddad, A. A. S., Elnaim, B. M. E., Shabana, S., Abdel‐Khalek, S., & Mansour, R. F. (2023) · Cancers · doi:10.3390/cancers15051492 · Cited by 49
Self-citationRai, H. M., & Yoo, J. (2023) · Journal of Cancer Research and Clinical Oncology · doi:10.1007/s00432-023-05216-w · Cited by 50
Afrin, H., Larson, N. B., Fatemi, M., & Alizad, A. (2023) · Cancers · doi:10.3390/cancers15123139 · Cited by 46
Breast Cancer Diagnosis Based on IoT and Deep Transfer Learning Enabled by Fog Computing
Pati, A., Parhi, M., Pattanayak, B. K., Singh, D., Singh, V. P., Kadry, S., Nam, Y., & Kang, B.-G. (2023) · Diagnostics · doi:10.3390/diagnostics13132191 · Cited by 48
Basurto-Hurtado, J. A., Cruz-Albarrán, I. A., Toledano‐Ayala, M., Ibarra-Manzano, M.-A., Morales-Hernandez, L. A., & Perez-Ramirez, C. A. (2022) · Cancers · doi:10.3390/cancers14143442 · Cited by 48
Aldhyani, T. H. H., Khan, M. A., Almaiah, M. A., Alnazzawi, N., Hwaitat, A. K. A., Elhag, A. K., Shehab, R., & Alshebami, A. S. (2023) · Electronics · doi:10.3390/electronics12040858 · Cited by 43
Rao, K. S., Terlapu, P. V., Jayaram, D. K., Raju, K. K., Kumar, G., Pemula, R., Gopalachari, M. V., & Rakesh, S. (2024) · IEEE Access · doi:10.1109/access.2024.3358448 · Cited by 40
Yaqoob, A., Verma, N. K., Mir, M. A., Tejani, G. G., Eisa, N. H. B., Osman, H. M. H., & Shah, M. A. (2025) · Scientific Reports · doi:10.1038/s41598-025-95786-1 · Cited by 39
A blockchain-enabled internet of medical things system for breast cancer detection in healthcare
Chaudhury, S., & Sau, K. (2023) · Healthcare Analytics · doi:10.1016/j.health.2023.100221 · Cited by 36
Classification of Breast Cancer Ultrasound Images with Deep Learning-Based Models
Uysal, F., & Köse, M. M. (2022) · International Electronic Conference on Applied Sciences (ASEC) · doi:10.3390/asec2022-13791 · Cited by 30
A deep learning model for detection of leukocytes under various interference factors
Li, M., Lin, C., Ge, P., Li, L., Song, S., Zhang, H., Lu, L., Liu, X., Zheng, F., Zhang, S., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-29331-3 · Cited by 30
Abbas, S., Asif, M. W., Rehman, A., Alharbi, M., Khan, M. A., & Elmitwally, N. S. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e36743 · Cited by 34
RetractedA novel medical text classification model with Kalman filter for clinical decision making
Li, J., Huang, Q., Ren, S., Jiang, L., Deng, B., & Qin, Y. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104503 · Cited by 30
Althobaiti, M. M., Ashour, A. A., Alhindi, N. A., Althobaiti, A., Mansour, R. F., Gupta, D. K., & Khanna, A. K. (2022) · BioMed Research International · doi:10.1155/2022/3714422 · Cited by 27
Self-citationRetractedPerformance evaluation of machine learning for breast cancer diagnosis: A case study
Shanbehzadeh, M., Kazemi-Arpanahi, H., Ghalibaf, M. B., & Orooji, A. (2022) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2022.101009 · Cited by 23
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Ya, G., Zhang, H., Yuan, L., Chen, W., Zhao, H., Yu, Q., & Shi, W. (2024) · Journal of International Medical Research · doi:10.1177/03000605241237867 · Cited by 24
RICHA, R., & Patro, B. D. K. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.109751 · Cited by 22
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Lee, M. J., Yang, M., Khwarg, S. I., Oh, E. K., Choi, Y. J., Kim, N., Choung, H.-K., Seo, C. W., Ha, Y. J., Cho, M. H., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-30699-5 · Cited by 22
An Enhanced LightGBM-Based Breast Cancer Detection Technique Using Mammography Images
Sait, A. R. W., & Nagaraj, R. (2024) · Diagnostics · doi:10.3390/diagnostics14020227 · Cited by 21
Chikkala, R. B., Anuradha, C., Murty, P. S. R. C., Rajeswari, S., Rajeswaran, N., Murugappan, M., & Chowdhury, M. E. H. (2025) · IEEE Access · doi:10.1109/access.2025.3542989 · Cited by 20
Rai, H. M., Dashkevych, S., & Yoo, J. (2024) · Mathematics · doi:10.3390/math12182808 · Cited by 20
Molaei, S., Ghorbani, N., Dashtiahangar, F., Peivandi, M., Pourasad, Y., & Esmaeili, M. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/7543429 · Cited by 20
Mansouri, R. A., & Ragab, M. (2022) · Healthcare · doi:10.3390/healthcare11010055 · Cited by 19
Self-citationBreast Cancer Detection and Classification Using Hybrid Feature Selection and DenseXtNet Approach
Alshehri, M. (2023) · Mathematics · doi:10.3390/math11234725 · Cited by 19
Han, J., Hua, H., Fei, J., Liu, J., Guo, Y., Ma, W., & Chen, J. (2024) · Clinical Breast Cancer · doi:10.1016/j.clbc.2024.01.005 · Cited by 21
Transforming Cancer Classification: The Role of Advanced Gene Selection
Yaqoob, A., Mir, M. A., Rao, G. V. V. J., & Tejani, G. G. (2024) · Diagnostics · doi:10.3390/diagnostics14232632 · Cited by 18
Wang, Y., Wang, C.-Y., Liu, K., Huang, Y.-H., Chen, T., Chiu, K.-N., Liang, C., & Lu, N. (2024) · Tomography · doi:10.3390/tomography10120145 · Cited by 18
Ragab, M., Kateb, F., El-Sawy, E.-S. K., Binyamin, S. S., Al-Rabia, M. W., & Mansouri, R. A. (2023) · Healthcare · doi:10.3390/healthcare11040590 · Cited by 18
Self-citationA Review on Medical Image Analysis Using Deep Learning
Egala, R., & Sairam, M. V. S. (2024) · doi:10.3390/engproc2024066007 · Cited by 17
Enhanced deep learning model for diagnosing breast cancer using thermal images
Dharani, N. P., Immadi, G., & Narayana, M. V. (2024) · Soft Computing · doi:10.1007/s00500-024-09742-8 · Cited by 17
Rai, H. M., Yoo, J., Agarwal, S., & Agarwal, N. (2025) · Bioengineering · doi:10.3390/bioengineering12010073 · Cited by 17
Feng, J., Lu, J., Jin, C., Chen, Y., Chen, S., Guo, G., & Gong, X. (2022) · Diagnostics · doi:10.3390/diagnostics12112648 · Cited by 16
Umer, M. J., Sharif, M. I., & Kim, J. (2024) · IEEE Access · doi:10.1109/access.2024.3429386 · Cited by 17
Review of AI & XAI-based breast cancer diagnosis methods using various imaging modalities
Karthiga, R., Narasimhan, K., Thanikaiselvan, V., Hemalatha, M., & Amirtharajan, R. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-20271-2 · Cited by 20
Khaled, M., Touazi, F., & Gaceb, D. (2024) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-024-09428-1 · Cited by 15
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Yaqoob, A., & Verma, N. K. (2025) · Journal of Medical Systems · doi:10.1007/s10916-025-02171-6 · Cited by 15
Jiraniramai, S., Pinyopornpanish, K., Wongpakaran, N., Angkurawaranon, C., Champion, V. L., Chitapanarux, I., Jiraporncharoen, W., & Wongpakaran, T. (2024) · Scientific Reports · doi:10.1038/s41598-024-58155-y · Cited by 14
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Iglesias, G., Talavera, E., Troya, J., Díaz-Álvarez, A., & García-Remesal, M. (2024) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2024.108228 · Cited by 15
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Almutairi, S. M., Manimurugan, S., Aborokbah, M. M., Narmatha, C., Ganesan, S., & Karthikeyan, P. (2023) · International Journal of Intelligent Systems · doi:10.1155/2023/8509433 · Cited by 14
Ravikumar, A., Sriraman, H., Saleena, B., & Prakash, B. (2023) · Health and Technology · doi:10.1007/s12553-023-00772-0 · Cited by 14
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Hartmann, T. J., Passauer, J., Hartmann, J., Schmidberger, L., Kneilling, M., & Volc, S. (2024) · JDDG Journal der Deutschen Dermatologischen Gesellschaft · doi:10.1111/ddg.15322 · Cited by 15
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Zhao, L., Sun, Y., Fu, Q., & Xiao, W. (2024) · Chemical Engineering Journal · doi:10.1016/j.cej.2024.157554 · Cited by 14
Abhisheka, B., Biswas, S. K., Purkayastha, B., & Das, S. (2025) · Multimedia Tools and Applications · doi:10.1007/s11042-025-20859-2 · Cited by 12
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Nissar, I., Alam, S., & Masood, S. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-23092-9_33 · Cited by 11
Basheri, M. (2023) · Biomimetics · doi:10.3390/biomimetics8060463 · Cited by 11
The Diagnostic Classification of the Pathological Image Using Computer Vision
Matsuzaka, Y., & Yashiro, R. (2025) · Algorithms · doi:10.3390/a18020096 · Cited by 12
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Guo, Y., Jiang, R., Gu, X., Cheng, H.-D., & Garg, H. (2023) · Healthcare · doi:10.3390/healthcare11182530 · Cited by 12
Alharbe, N., Munshi, R. M., Khayyat, M. M., Khayyat, M. M., Hamza, S. H. A., & Aljohani, A. A. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/4629178 · Cited by 11
RetractedKormpos, C., Zantalis, F., Katsoulis, S., & Koulouras, G. (2025) · Big Data and Cognitive Computing · doi:10.3390/bdcc9050111 · Cited by 11
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Ragab, M. (2022) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.031037 · Cited by 10
Self-citationA fully automatic framework for evaluating cosmetic results of breast conserving therapy
Guo, C., Smith, T. L., Feng, Q., Benitez-Quiroz, F., Vicini, F. A., Arthur, D. W., White, J. R., & Martı́nez, A. M. (2022) · Machine Learning with Applications · doi:10.1016/j.mlwa.2022.100430 · Cited by 11
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Rehman, M. Z. U., Ahmad, J. E., Jaha, E. S., Ali, A., AlZain, M. A., & Saeed, F. (2023) · Computer Systems Science and Engineering · doi:10.32604/csse.2023.035382 · Cited by 10
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Gagliardi, M., Ruga, T., Vocaturo, E., & Zumpano, E. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-6992-6_5 · Cited by 9
Rai, H. M., Yoo, J., & Dashkevych, S. (2025) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-024-10219-y · Cited by 9
Naas, M., Mzoughi, H., Njeh, I., & Slima, M. B. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06561-8 · Cited by 9
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Roy, D., & Dutta, M. (2023) · International Conference on Computing and Communication Systems (I3CS) · doi:10.1109/i3cs58314.2023.10127238 · Cited by 9
Alrowais, F., Alotaibi, S. S., Marzouk, R., Salama, A. S., Rizwanullah, M., Zamani, A. S., Abdelmageed, A. A., & Eldesouki, M. I. (2022) · Cancers · doi:10.3390/cancers14225661 · Cited by 9
Chowa, S. S., Azam, S., Montaha, S., Bhuiyan, M. R. I., & Jonkman, M. (2024) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-024-00983-5 · Cited by 8
Coxe, T., & Azad, R. K. (2023) · Antibiotics · doi:10.3390/antibiotics12111604 · Cited by 8
Chen, B., Jin, J., Liu, H., Yang, Z., Zhu, H., Wang, Y., Lin, J., Wang, S., & Chen, S. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1289669 · Cited by 9
Saini, M., Hassanzadeh, S., Musa, B., Fatemi, M., & Alizad, A. (2025) · Scientific Reports · doi:10.1038/s41598-025-99009-5 · Cited by 10
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Zováthi, B. H., Mohácsi, R., Szász, A. M., & Cserey, G. (2022) · Diagnostics · doi:10.3390/diagnostics12092161 · Cited by 8
Yaqoob, A., Verma, N. K., Rao, G. V. V. J., & Aziz, R. M. (2025) · De Gruyter eBooks · doi:10.1515/9783111504667-009 · Cited by 7
Ragab, M., Kateb, F., Al-Rabia, M. W., Hamed, D., Althaqafi, T. A., & ALGhamdi, A. S. A.-M. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20064794 · Cited by 7
Self-citationPriyadarshni, V., Sharma, S. K., Rahmani, M. K. I., Kaushik, B., & Almajalid, R. (2024) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2024.044963 · Cited by 9
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Balaji, S., Arunprasath, T., Rajasekaran, M. P., Sindhuja, K., & Kottaimalai, R. (2023) · International Conference on Smart Electronics and Communication (ICOSEC) · doi:10.1109/icosec58147.2023.10275824 · Cited by 7
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Bißantz, S., Frick, S., Melinščak, F., Iliescu, D., & Wetzel, E. (2024) · European Journal of Psychological Assessment · doi:10.1027/1015-5759/a000817 · Cited by 9
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Mahmood, D. A., & Aminfar, S. A. (2024) · BioMed Target Journal · doi:10.59786/bmtj.211 · Cited by 7
Ding, T., Shi, K., Pan, Z., & Ding, C. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.2226 · Cited by 6
Singh, V. K., Mohamed, E. M., & Abdel‐Nasser, M. (2024) · Neural Computing and Applications · doi:10.1007/s00521-023-09363-6 · Cited by 7
Katib, I. A., & Ragab, M. (2023) · Computer Systems Science and Engineering · doi:10.32604/csse.2023.036796 · Cited by 6
Self-citationBokade, A., & Shah, A. K. (2023) · International Journal of Computing and Digital Systems · doi:10.12785/ijcds/1301117 · Cited by 6
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Muttair, M. K. (2024) · Journal of Electrical Systems · doi:10.52783/jes.2291 · Cited by 5
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Prasetyo, S. Y. (2023) · International Conference on Smart Cities, Automation & Intelligent Computing Systems (ICON-SONICS) · doi:10.1109/icon-sonics59898.2023.10435127 · Cited by 5
Cui, Y., Li, X., Wang, Y., Yuan, W., Cheng, X. W., & Samiei, M. (2022) · Cybernetics & Systems · doi:10.1080/01969722.2022.2159151 · Cited by 5
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Metadata
- Authors
- Ragab, M., Albukhari, A., Alyami, J., & Mansour, R. F.
- Affiliations
- Al-Azhar University (EG)
- King Abdulaziz University (SA)
- New Valley University (EG)
- Corresponding author
- Ragab, Mahmoud
- Venue
- Biology
- Publisher
- MDPI AG
- Published
- 2022-03-14
- Volume
- 11
- Issue
- 3
- Pages
- 439
- ISSN
- 2079-7737 (ISSN-L)
- PMC
- PMC8945718
- OpenAlex
- W4221086517
- Cited by
- 172
- References
- 26
- Citations by year
- 2026: 23 · 2025: 32 · 2024: 46 · 2023: 46 · 2022: 24
- FWCI
- 12.18
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Biology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- King Abdulaziz University (D-000-000-0000)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- breast cancer diagnosis, breast ultrasound images, computer-aided diagnosis, clinical decision support systems, ensemble deep learning, Kapur's entropy, image segmentation, VGG16, VGG19, SqueezeNet, cat swarm optimization, multilayer perceptron, Wiener filter, contrast enhancement
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