International Journal of Computational and Electronic Aspects in Engineering
Volume 6 · Issue 4 · December 2025 · pp. 243-251
Research Article · Peer Reviewed
Received: October 21, 2025 · Accepted: December 15, 2025 · Published: December 30, 2025
DOI: 10.26706/ijceae.6.4.20250904
Open Access · CC BY 4.0

Deep Convolutional Neural Network–Based Image Processing for Automated Mammography Analysis and Breast Cancer Detection

Thaer M. Kadhim
Department of Training, Ministry of Education, Thi-Qar, Iraq ·
Corresponding author: cse.61012@uotechnology.edu.iq
Download PDF Cite this article Certificate Volume 6 Issue 4

Abstract

The Deep Convolutional Neural Networks (DCNNs) model has become a prevalent model in image processing and computer vision tasks, particularly in biomedical image analysis. Among biomedical imaging modalities, mammography remains one of the most important techniques for early breast cancer detection. However, image interpretation can be challenging due to low contrast and complex tissue patterns. This study proposes and implements a comprehensive DCNN-based framework for mammography image enhancement and automated breast cancer diagnosis. Publicly available datasets including MIAS, DDSM, and INbreast are used to evaluate multiple deep learning architectures. Image quality enhancement is achieved using Contrast Limited Adaptive Histogram Equalization (CLAHE), followed by feature extraction using VGG-16, VGG-19, and ResNet-50 models. Experimental results demonstrate that ensemble learning approaches significantly improve accuracy, AUC-ROC, sensitivity, and specificity compared to individual models. The system is also successfully deployed on Raspberry Pi 3B hardware using a lightweight MobileNetV2-based web application, proving its feasibility for embedded medical diagnostic support systems.

Keywords

Deep Convolutional Neural Networks Mammography Breast Cancer Detection Ensemble Learning Transfer Learning Medical Image Processing Embedded Deployment

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