Clinical Breast Cancer Diagnosis Enhanced by Deep Convolutional Neural Networks for Early Detection
Keywords:
Breast cancer diagnosis, Histopathology images, Convolutional neural network, Deep learning, Early cancer detection, Medical image classification, Binary classification, Computer-aided diagnosisAbstract
Breast cancer continue to be one of the leading cause of death for women worldwide and amongst them, early and accurate diagnosis of breast cancer is crucial to improve the patient's outcome. This study presents an automated breast cancer diagnosis framework, which is based on a deep Convolutional Neural Network (CNN) applied to histopathological images. The proposed model is intended to be effective in sampling discriminative spatial and morphological features that differentiate benign from malignant breast tissue. Experiments are performed on a large-scale histopathology image dataset extracted from a public repository, thus ensuring a realistic clinical representation. The dataset is systematically pre-processed and split into training and validation datasets and testings to allow the performance to be evaluated in an unbiased manner. The proposed CNN architecture exhibits the stability of learning and generalization ability, which is also proved by the comprehensive experimental analysis. The model is able to take a high classification accuracy of 96% with robust precision, recall, F1 score and excellent ROC-AUC performance pointing to its descended discriminative power. Comparative analysis with popular deep learning models has additionally proven the superiority of the proposed approach. Overall, this work shows the promise of CNN-based frameworks as powerful, decision-aiding tools that can aid in early detection of breast cancer in the clinic.
References
[1] Mashekova, A., Zhao, M.Y., Zarikas, V., Mukhmetov, O., Aidossov, N., Ng, E.Y.K., Wei, D. and Shapatova, M., 2025. Review of artificial intelligence techniques for breast cancer detection with different modalities: Mammography, ultrasound, and thermography images. Bioengineering, 12(10), p.1110.
[2] Sadr, S., Rahdar, A., Pandey, S., Hajjafari, A., Soroushianfar, M., Sepahvand, H., Sasani, B., Salimpour Kavasebi, S. and Borji, H., 2025. Revolutionizing cancer detection: harnessing quantum dots and graphene-based nanobiosensors for lung and breast cancer diagnosis. BioNanoScience, 15(1), p.111.
[3] Saeidi, T., Mahmood, S.N., Saleh, S., Timmons, N., Al-Gburi, A.J.A. and Razzaz, F., 2025. Ultra-wideband (UWB) antennas for breast cancer detection with microwave imaging: a review. Results in Engineering, p.104167.
[4] Bilal, A., Alkhathlan, A., Kateb, F.A., Tahir, A., Shafiq, M. and Long, H., 2025. A quantum-optimized approach for breast cancer detection using SqueezeNet-SVM. Scientific Reports, 15(1), p.3254.
[5] Setiadi, D.R.I.M., Ojugo, A.A., Pribadi, O., Kartikadarma, E., Setyoko, B.H., Widiono, S., Robet, R., Aghaunor, T.C. and Ugbotu, E.V., 2025. Integrating hybrid statistical and unsupervised LSTM-guided feature extraction for breast cancer detection. Journal of Computing Theories and Applications, 2(4), pp.536–552.
[6] Alhussen, A., Haq, M.A., Khan, A.A., Mahendran, R.K. and Kadry, S., 2025. XAI-RACapsNet: Relevance-aware capsule network-based breast cancer detection using mammography images via explainability O-net ROI segmentation. Expert Systems with Applications, 261, p.125461.
[7] Verma, G., Pasha, S.N. and Singh, C., 2025, April. Leveraging EfficientNetB5 for accurate classification of diverse human cancer tissues. In 2025 3rd International Conference on Advancement in Computation & Computer Technologies (InCACCT) (pp. 20–24). IEEE.
[8] Shahid, M.S. and Imran, A., 2025. Breast cancer detection using deep learning techniques: challenges and future directions. Multimedia Tools and Applications, 84(6), pp.3257–3304.
[9] Muduli, D., Kumar, R.R., Pradhan, J. and Kumar, A., 2025. An empirical evaluation of extreme learning machine uncertainty quantification for automated breast cancer detection. Neural Computing and Applications, 37(12), pp.7909–7924.
[10] Verma, G. and Kumar, G.R., 2025, April. Blood cell cancer classification using the EfficientNetB3 model: A deep learning approach. In 2025 4th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0 (pp. 1–6). IEEE.
[11] Wekalao, J., 2025. High-sensitivity terahertz biosensor for breast cancer detection using nanostructured metasurfaces and machine learning. Optical and Quantum Electronics, 57(6), p.349.
[12] Kant, V. and Kumar, B.V., 2025, April. Deep learning-based classification of lung and colon cancer subtypes using histology images. In 2025 4th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0 (pp. 1–5). IEEE.
[13] Kant, V., Gupta, D. and Aluvala, S., 2024, November. AI-powered breast cancer classification: Leveraging CNNs for early detection. In 2024 International Conference on Information Science and Communications Technologies (ICISCT) (pp. 434–439). IEEE.
[14] Díaz, O., Rodríguez-Ruíz, A. and Sechopoulos, I., 2024. Artificial intelligence for breast cancer detection: Technology, challenges, and prospects. European Journal of Radiology, 175, p.111457.
[15] Pourmadadi, M., Ghaemi, A., Khanizadeh, A., Yazdian, F., Mollajavadi, Y., Arshad, R. and Rahdar, A., 2024. Breast cancer detection based on cancer antigen 15-3; emphasis on optical and electrochemical methods: A review. Biosensors and Bioelectronics, 260, p.116425.
[16] Oyebanji, O.S., Apampa, A.R., Idoko, P.I., Babalola, A., Ijiga, O.M., Afolabi, O. and Michael, C.I., 2024. Enhancing breast cancer detection accuracy through transfer learning: A case study using EfficientNet. World Journal of Advanced Engineering Technology and Sciences, 13(01), pp.285–318.