Convolution Neural Network for Breast Cancer Detection and Classification Using Deep Learning
Open accessAbstract
OBJECTIVE: Early detection and precise diagnosis of breast cancer (BC) plays an essential part in enhancing the diagnosis and improving the breast cancer survival rate of patients from 30 to 50%. Through the advances of technology in healthcare, deep learning takes a significant role in handling and inspecting a great number of X-ray, MRI, CTR images. The aim of this study is to propose a deep learning model (BCCNN) to detect and classify breast cancers into eight classes: benign adenosis (BA), benign fibroadenoma (BF), benign phyllodes tumor (BPT), benign tubular adenoma (BTA), malignant ductal carcinoma (MDC), malignant lobular carcinoma (MLC), malignant mucinous carcinoma (MMC), and malignant papillary carcinoma (MPC). METHODS: Breast cancer MRI images were classified into BA, BF, BPT, BTA, MDC, MLC, MMC, and MPC using a proposed Deep Learning model with additional 5 fine-tuned Deep learning models consisting of Xception, InceptionV3, VGG16, MobileNet and ResNet50 trained on ImageNet database. The dataset was collected from Kaggle depository for breast cancer detection and classification. That Dataset was boosted using GAN technique. The images in the dataset have 4 magnifications (40X, 100X, 200X, 400X, and Complete Dataset). Thus we evaluated the proposed Deep Learning model and 5 pre-trained models using each dataset individually. That means we carried out a total of 30 experiments. The measurement that was used in the evaluation of all models includes: F1-score, recall, precision, accuracy. RESULTS: The classification F1-score accuracies of Xception, InceptionV3, ResNet50, VGG16, MobileNet, and Proposed Model (BCCNN) were 97.54%, 95.33%, 98.14%, 97.67%, 93.98%, and 98.28%, respectively. CONCLUSION: Dataset Boosting, preprocessing and balancing played a good role in enhancing the detection and classification of breast cancer of the proposed model (BCCNN) and the fine-tuned pre-trained models' accuracies greatly. The best accuracies were attained when the 400X magnification of the MRI images due to their high images resolution.
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Abunasser et al. trained six deep learning models for classifying eight types of breast cancer on MRI, with accuracy rates of 97.54%, 95.33%, 98.14%, 97.67%, 93.98%, and 98.28% respectively.
“These models demonstrated remarkable accuracy in their classification tasks, with rates of 97.54%, 95.33%, 98.14%, 97.67%, 93.98%, and 98.28% respectively”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [98]
Abunasser et al. [98] achieved an F1-score of 98.28.
“| Abunasser et al. [98] | Original MRI data | MRI | Kaggle depository | 98.28 (F1-score) |”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [98]
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Zaidani, H., Koulali, R., Maizate, A., & Ouzzif, M. (2025) · Future Internet · doi:10.3390/fi17040176 · Cited by 4 · see citing works
“We tested Deep Neural Networks (DNNs), Long Short-Term Memory (LSTM), and Recurrent Neural Networks (RNNs) on our Moroccan dataset for text classification, measuring their performance using appropriate evaluation metrics [1,30].”
Borisova, J., Morshchinin, I. V., Nazarova, V. I., Molodkina, N. R., & Nikitin, N. O. (2025) · Sensors · doi:10.3390/s25154651 · Cited by 5 · see citing works
“Detection of cancer cells using machine learning is also a common task, with solutions based on simple models (such as support vector machines [26]), specific features extracted from microscope images [27], and variations of deep neural networks [28–30].”
Harnessing Inception V3 for Efficient Classification of Breast Cancer in Mammography Images
Rani, R., Gupta, S., & Lande, J. (2025) · OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry (OTCON) · doi:10.1109/otcon65728.2025.11071216 · Cited by 4 · see citing works
Pillai, R., Sharma, N. V., Chauhan, R. S., Rawat, D., & Gupta, R. (2023) · Global Conference on Information Technologies and Communications (GCITC) · doi:10.1109/gcitc60406.2023.10426155 · Cited by 4 · see citing works
“The construction of the confusion matrix [7], a reliable technique utilized in classification evaluations, is undertaken.”
MethodA Novel CNN Approach for Accurate Breast Cancer Diagnosis
Singh, R., Sharma, N. V., & Shory, A. (2024) · International Conference on Innovations in Communications, Electrical and Computer Engineering (ICICEC) · doi:10.1109/icicec62498.2024.10808811 · Cited by 4 · see citing works
Optimized Hybrid CNN Framework for Enhanced Tumor Classification in Breast Cancer Diagnosis
Batool, S., Zainab, S., Usman, M., & Pu, J. (2025) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.70252 · Cited by 3 · see citing works
Balaha, H. M., AbdulAzeem, Y., Aljohani, M., El-Abd, M., Badawy, M., & Elhosseini, M. A. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.113458 · Cited by 3 · see citing works
Yadav, L., Chandra, G., & Yadav, D. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.131089 · Cited by 3 · see citing works
Flamingo Search Sailfish Optimizer Based SqueezeNet for Detection of Breast Cancer Using MRI Images
Vijaya, P., Chander, S., Fernandes, R., Rodrigues, A. P., & Raja, M. (2024) · Cancer Investigation · doi:10.1080/07357907.2024.2403088 · Cited by 3 · see citing works
Catalano, R., Tibaldi, M. G., Lombardi, L., Santone, A., Cesarelli, M., & Mercaldo, F. (2024) · Sensors · doi:10.3390/s24217022 · Cited by 3 · see citing works
Li, H., & Cheng, T. (2025) · Journal of Radiation Research and Applied Sciences · doi:10.1016/j.jrras.2025.101656 · Cited by 3 · see citing works
Annepu, V., Abbas, M. A. E., Bitra, H., Kumar, V. N., & Bagadi, K. (2024) · Applied Computational Intelligence and Soft Computing · doi:10.1155/2024/5574638 · Cited by 3 · see citing works
Accurate breast cancer diagnosis strategy (BCDS) based on deep learning techniques
Ibrahim, T. S., Saraya, M. S., Saleh, A. I., & Rabie, A. H. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10849-0 · Cited by 3 · see citing works
Lu, G., Tian, R., Yang, W. P., Liu, D., Chen, W., Liang, J., Qi, P., Hao, S., & Zhang, G. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1668793 · Cited by 2 · see citing works
Jyothisree, K. R., & Jacob, V. (2025) · Scientific Reports · doi:10.1038/s41598-025-28133-z · Cited by 2 · see citing works
“The GAN method was used to enhance the dataset 11 .”
Taljeh, M. Z., Mannan, S., & Shekar, B. H. (2025) · IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) · doi:10.1109/discover66922.2025.11258999 · Cited by 2 · see citing works
“Despite these alarming numbers, advances in early detection, imaging technologies and personalized treatment strategies have significantly improved survival rates, making timely and accurate diagnosis a crucial aspect of combating the disease [1].”
Göktepe, Y. E. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.109645 · Cited by 2 · see citing works
Elumalai, S., & Surendran, R. (2025) · International Conference on NexGen Networks and Cybernetics (IC2NC) · doi:10.1109/ic2nc67409.2025.11376499 · Cited by 2 · see citing works
“The [11] proposed a deep convolutional model (BCCNN) for eight-class classification of breast cancer from MRI.”
Fusion of genomic and pathological data for breast cancer detection using BCDNN
Bilal, A., Obidallah, W. J., Wassan, S., Albathan, M., Almakki, R. S., Alshaikh, Z., & Shafiq, M. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1726223 · Cited by 2 · see citing works
BreasTransNeXt: An Enhanced Multi-Module Vision Transformer For Early Breast Cancer Diagnosis
Açıkgöz, H., Aytekin, A., & Gezici, S. (2026) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-026-01863-w · Cited by 2 · see citing works
Getu, M. A., Lu, C., Liu, Y., Mehmood, A., Iqbal, Z., & Zhang, X. (2025) · IntechOpen eBooks · doi:10.5772/intechopen.1007833 · Cited by 2 · see citing works
A hybrid random forest and k-nearest neighbors approach for breast cancer detection
(2024) · International Journal of Advanced Computer Research · doi:10.19101/ijacr.2024.1466003 · Cited by 1 · see citing works
“These limitations include reduced sensitivity in detecting cancer in dense breast tissue and a high rate of false positives, which can result in unnecessary stress and medical interventions [8, 9].”
Waele, T. D., Peralta, D., Poorter, E. D., & Shahid, A. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10651531 · Cited by 1 · see citing works
“Researchers have successfully applied neural networks to detect and classify breast cancer based on Magnetic Resonance Imagery (MRI) images [2].”
MethodLlaguno-Roque, J.-L., Barrientos-Martínez, R.-E., Acosta‐Mesa, H., Barranca-Enríquez, A., Mezura‐Montes, E., & Romo‐González, T. (2024) · Journal of Proteome Research · doi:10.1021/acs.jproteome.4c00759 · Cited by 1 · see citing works
AI-Powered Deep CNN Models for Automated Classification of Multiple Cancer Types
Appavu, N. (2025) · International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) · doi:10.1109/icdcece65353.2025.11035762 · Cited by 1 · see citing works
Histopathology Image Augmentation Using Generative Adversarial Models for Breast Cancer Diagnostics
Shah, P. B., Ganesan, S., Ling, S., Pokharel, S., & Somasiri, N. (2025) · International Aegean Conference on Electrical Machines and Power Electronics & International Conference on Optimization of Electrical and Electronic Equipment (ACEMP-OPTIM) · doi:10.1109/optim-acemp62776.2025.11075256 · Cited by 1 · see citing works
“Furthermore, a deep learning model called BCCNN has been proposed for the detection and classification of breast cancers into eight classes, achieving high accuracy rates [16].”
Empirical Evaluation of Breast Cancer Detection Using Machine Learning and Deep Learning Techniques
Hira, T., Jain, A., Sharma, V., Jindal, S., & Yadav, R. (2025) · International Conference on Computing for Sustainable Global Development · doi:10.23919/indiacom66777.2025.11115717 · Cited by 1 · see citing works
Advances in Kidney Cancer Detection: Harnessing the Power of Deep Learning for Accurate Diagnosis
Alkahlout, M. A., & Abu-Naser, S. S. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-5214-3_21 · Cited by 1 · see citing works
Self-citationHybrid Approach for Breast Cancer Detection Using CNNs and Real-Time Risk Assessment
Arabov, B., Razaque, A., Kairedenova, G., & Khan, M. A. (2025) · IEEE International Conference on Secure IoT, Assured and Trusted Computing (SATC) · doi:10.1109/satc65530.2025.11137232 · Cited by 1 · see citing works
“In the following study, Abunasser et al. [11] focused on breast cancer detection and classification based on different datasets (40X, 100X, 200X, 400X, all datasets together) by balancing the datasets and evaluating the proposed deep learning model and using 5 pre-trained models (Xception,…”
Tummala, S., Chauhdary, S. H., Singh, V., Kumar, R., Kadry, S., & Kim, J. (2025) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2025.069731 · Cited by 1 · see citing works
“Additionally, the study in [38] developed a custom CNN with 21M parameters, almost 40 times more than BC-Net, and through extensive data augmentation, obtained an accuracy of 98.3%.”
InfluentialTeeth in focus: a deep dive into convolutional neural networks for dental image analysis
Arya, V., Duraiswamy, S., & Jagadeesan, D. (2025) · International Journal of Systems Assurance Engineering and Management · doi:10.1007/s13198-025-03061-3 · Cited by 1 · see citing works
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Sriramakrishnan, G. V., Ponnada, S., Ayyappan, S., & Ganeshan, R. (2025) · Iranian Journal of Science and Technology Transactions of Electrical Engineering · doi:10.1007/s40998-025-00969-1 · Cited by 1 · see citing works
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Pasha, M. I., Subashini, S. M. C., Begum, A., Pathuri, S. K., Pabboju, S. S., & Kaliappan, S. (2025) · IEEE Pune Section International Conference (PuneCon) · doi:10.1109/punecon67554.2025.11379204 · Cited by 1 · see citing works
Deep Learning-Based Breast Cancer Classification: A Comprehensive Study on Mammogram Images
Ahmed, S., Shouma, S. S., Jaman, M. F., & Mitu, S. J. (2026) · International Conference on Electrical, Computer & Telecommunication Engineering (ICECTE) · doi:10.1109/icecte69292.2026.11429259 · Cited by 1 · see citing works
“Abunasser et al. [9] also used BreakHis dataset and developed BCCNN along with some other deep learning models.”
Ali, Y., Müller, J., Weinmann, A., & Gregori, J. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1715858 · Cited by 1 · see citing works
Hybrid YOLO Feature Extraction with RF–AdaBoost Classifier for Accurate Breast Tumor Detection
Ramesh, P., Rikhari, S., Boga, J., & Kaliappan, S. (2026) · International Conference on Smart Futuristic Technology (SMARTTECH) · doi:10.1109/icsft66733.2026.11506829 · Cited by 1 · see citing works
Zhou, H., Li, J., Huang, J., & Yue, Z. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1237816 · Cited by 1 · see citing works
“(19) used the pre-trained ResNet50 model as a feature extractor and swapped a new densely connected classifier for prediction.”
MethodAlhassan, A. M. (2024) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2024.046427 · Cited by 1 · see citing works
Deep learning approaches for breast cancer detection using breast MRI
Sahu, T., & Subaşı, A. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-22308-2.00012-3 · Cited by 1 · see citing works
A Single Stage Detector for Breast Cancer Detection on Digital Mammogram
Xu, L., Jia, N., & Zhang, M. (2024) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2024.0150312 · Cited by 1 · see citing works
“Abunasser et al. [20] developed a CNN based method for breast cancer detection.”
MethodA Systematic Review of Deep Learning Techniques for Breast Cancer Detection
Deepika, D., & Kanmani, P. (2024) · doi:10.1109/iccsp60870.2024.10543472 · Cited by 1 · see citing works
“…86% A. Raza et al.(2023) [11] Deep Learning (DeepBreastCancerNet) Enhanced breast cancer detection and classification 99.63% Abunasser et al.(2023) [12] Deep Learning (BCCNN) Detect and classify breast tumors into eight categories 98.28% M. Alqhtani, S.(2022) [15] BreastCNN Breast cancer detection…”
BackgroundMethodInfluentialAbunasser, B. S., & Abu-Naser, S. S. (2024) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-031-59711-4_3 · Cited by 1 · see citing works
Self-citationBreast Cancer Determination Using B-Mode Ultrasound Imaging Using Deep Learning Technique
Baranidharan, B., Alekhya, S., & Tiwari, V. (2024) · IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS) · doi:10.1109/iciteics61368.2024.10625597 · Cited by 1 · see citing works
A Novel Approach for Breast Tumor MRI Classification: Vision Transformers and Majority Integration
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“[17]These studies underscore the growing role of advanced computational techniques in improving diagnostic processes in medical imaging.”
BackgroundA Review of Deep Learning Models for Brain Tumor Prediction Using MRI Images
Sharma, M., Beniwal, S., & Singh, S. (2024) · Asia Pacific Conference on Innovation in Technology (APCIT) · doi:10.1109/apcit62007.2024.10673698 · Cited by 1 · see citing works
“Various deep learning models which are used for tumor detection are Convolutional neural network (CNN), VGG-16, ExceptionNet, inceptionV3, and Resnet50 [10].”
MethodHybrid Spinal-Fuzzy-DKN approach for breast cancer detection using magnetic resonance images
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BCDM: A Novel AI-Based Model for Detection of Breast Cancer
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Breast Cancer Detection using Deep Learning
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“LITERATURE REVIEW Abunasser et al., (2023) [8] suggested that early detection and accurate diagnosis of BC were crucial factors in improving the survival rate of patients, increasing it from 30% to 50%.”
Legal Education in Malaysia in the Context of Legal Practice
Ali, N. M., Rusli, M. H. M., Shukor, S. A., Majid, M. N. A., Shah, H. A. R., Salleh, A. Z., Mutalib, L. A., Al’Uyuna, Amin, M., & Nor, M. M. (2018) · International Journal of Engineering & Technology · doi:10.14419/ijet.v7i2.29.13806 · Cited by 1 · see citing works
Classification of Breast Cancer Patients using Deep Learning Techniques
Navghare, T., Muley, A. A., & Jadhav, V. (2024) · Indian Journal of Science and Technology · doi:10.17485/ijst/v16i47.2758
BREAST CANCER DETECTION USING PROBABILISTIC NEURAL NETWORKS
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Kumari, T. L. P., & Perumal, K. (2024) · International Journal of Data Science and Analytics · doi:10.1007/s41060-024-00662-2
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Breast Cancer Image Classification Using Convolutional Neural Networks
Ravi Teja, S., & Joshi, S. (2024) · International Conference on Integrated Intelligence and Communication Systems (ICIICS) · doi:10.1109/iciics63763.2024.10859380
Automated Breast Cancer Detection: A Review
Alkhater, R. H. M., & Al-Maadeed, S. A. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-82156-1_23
- Authors
- Abunasser, B., AL-Hiealy, M. R., Zaqout, I., & Abu-Naser, S.
- Affiliations
- University Malaysia of Computer Science and Engineering (MY)
- Al-Azhar University – Gaza (PS)
- Corresponding author
- Abunasser, Basem
- Venue
- Asian Pacific Journal of Cancer Prevention
- Publisher
- EpiSmart Science Vector Ltd
- Published
- 2023-02-01
- Volume
- 24
- Issue
- 2
- Pages
- 531-544
- ISSN
- 1513-7368 (ISSN-L)
- 2476-762X
- PMC
- PMC10162639
- OpenAlex
- W4322616751
- Cited by
- 174
- References
- 51
- Citations by year
- 2026: 41 · 2025: 81 · 2024: 45 · 2023: 6 · 2018: 1
- FWCI
- 16.31
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Asian Pacific Journal of Cancer Prevention (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › MRI in cancer diagnosis
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- breast cancer, breast cancer classification, convolutional neural networks, pre-trained models, transfer learning, GAN data augmentation, benign breast tumors, ductal carcinoma, lobular carcinoma, papillary carcinoma, magnification, early detection
- MeSH terms
- Deep Learning, Breast Neoplasms, Carcinoma, Papillary, Female, Fibroma, Humans, Fibroadenoma, Carcinoma, Ductal, Breast, Carcinoma, Lobular
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