Al-Qadisiyah Journal for Engineering Sciences

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

Document Type : Research Paper

Authors

Qutaiba Humadi Mohammed 1
Noor Alhuda Kh Ibrahim 2
Anupama Namburu 3
Chaitanya Konda 4

1 Media Technology and Communications Eng. Department, College of Engineering, University of Information Technology and Communications, Baghdad, Iraq.

2 Department of Basic Sciences, College of Nursing, University of Baghdad, Baghdad, Iraq.

3 School of Engineering, Jawaharlal Nehru University, New Delhi 110067, India.

4 Department of CSE, College of Engineering and Technology, Acharya Nagarjuna University, Nagarjuna Nagar, Guntur, 522510, India.

10.30772/qjes.2025.155626.1429
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
Crossmark
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Al-Qadisiyah Journal for Engineering Sciences
Volume 19, Issue 1
Winter 2026
Pages 59-70

  • Receive Date 26 December 2024
  • Revise Date 09 April 2025
  • Accept Date 08 October 2025

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