Mohammed, Q., Ibrahim, N., Namburu, A., Konda, C. (2026). Breast cancer detection using deep learning techniques: An investigation using the CBIS-DDSM dataset and customized neural network model, ResNetV2 and YOLO. , 19(1), 59-70. doi: 10.30772/qjes.2025.155626.1429
Qutaiba Humadi Mohammed; Noor Alhuda Kh Ibrahim; Anupama Namburu; Chaitanya Konda. "Breast cancer detection using deep learning techniques: An investigation using the CBIS-DDSM dataset and customized neural network model, ResNetV2 and YOLO". , 19, 1, 2026, 59-70. doi: 10.30772/qjes.2025.155626.1429
Mohammed, Q., Ibrahim, N., Namburu, A., Konda, C. (2026). 'Breast cancer detection using deep learning techniques: An investigation using the CBIS-DDSM dataset and customized neural network model, ResNetV2 and YOLO', , 19(1), pp. 59-70. doi: 10.30772/qjes.2025.155626.1429
Mohammed, Q., Ibrahim, N., Namburu, A., Konda, C. Breast cancer detection using deep learning techniques: An investigation using the CBIS-DDSM dataset and customized neural network model, ResNetV2 and YOLO. , 2026; 19(1): 59-70. doi: 10.30772/qjes.2025.155626.1429

Breast cancer detection using deep learning techniques: An investigation using the CBIS-DDSM dataset and customized neural network model, ResNetV2 and YOLO

Al-Qadisiyah Journal for Engineering Sciences
Volume 19, Issue 1, March 2026, Pages 59-70    PDF (7.83 M)
Document Type: Research Paper
DOI: 10.30772/qjes.2025.155626.1429
Authors
Qutaiba Humadi Mohammed* 1; Noor Alhuda Kh Ibrahim2; Anupama Namburu3; Chaitanya Konda4
1Media Technology and Communications Eng. Department, College of Engineering, University of Information Technology and Communications, Baghdad, Iraq.
2Department of Basic Sciences, College of Nursing, University of Baghdad, Baghdad, Iraq.
3School of Engineering, Jawaharlal Nehru University, New Delhi 110067, India.
4Department of CSE, College of Engineering and Technology, Acharya Nagarjuna University, Nagarjuna Nagar, Guntur, 522510, India.
Abstract
The current work investigates the application of deep learning methodologies for detecting and classifying tumors in mammograms. A comprehensive analysis was conducted by evaluating various pre-trained neural network architectures to identify the model that delivers optimal performance. Among the tested architectures, Residual Neural Networks and Densely Connected Convolutional Networks were explored extensively. Data preprocessing, including data augmentation, was a critical step due to the limited availability of public medical imaging datasets. This process ensured diversity in the data and improved model robustness. This paper evaluates deep-learning models for mammogram tumor detection. Experiments without the AdamW optimizer and horizontal flip showed overfitting and low precision (below 40\%). Densely Connected Convolutional Networks achieved high precision but exhibited overfitting with noisy validation loss curves. Adding AdamW and horizontal flip reduced overfitting but lowered overall performance. In classification, the model detected tumors in 9 out of 16 images, showing potential but requiring improvement. The model's ability to detect calcification tumors enhances robustness. YOLO network metrics were modest, reflecting the task's complexity, but results were acceptable for tumor classification challenges.
Keywords
Breast Cancer; CNN; Data augmentation; Deep Learning; ResNetV2; YOLO
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