Work

Classification of mammographic abnormalities using convolutional neural networks

Year: 2025

Type: article

Abstract: Distinguishing between benign and malignant mammography images is a complex task even for experimented radiologists and, to deal with this issue, researchers developed computer-aided diagnosis systems. This study introduces a machine learning model to classify mammography images into benign and malignant classes. We extract region of interests using the appropriate mask for each mammography image, and we feed it into a modified LeNet model. We add two parallel convolutional blocks to the original LeNet architecture, and we notice a significant increase in the performance. We pre-train the model on the DMID dataset and a subset of the BCDR dataset, and we test it on the remaining subset. The modified lightweight model reached an accuracy of 99%.

Source: International Journal of Computing and Artificial Intelligence

DOI: 10.33545/27076571.2025.v6.i1b.143

Author: Louai Zaiter

Institution: Aberystwyth University

Language: English

Topic: AI in cancer detection

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: bronze

Funder: Aberystwyth University