Work

The Study of the Effectiveness and Efficiency of Multiple DCNN Models for Breast Cancer Diagnosis Using a Small Mammography Dataset

Year: 2024

Type: article

Abstract: Breast cancer (BC), the most prevalent cancer worldwide, poses a significant threat to women’s health, often resulting in mortality. Early intervention is crucial for reducing mortality rates and improving recovery. Mammography plays a pivotal role in early detection through high-resolution imaging. Various classification techniques, including classical and deep learning (DL) methods, assist in diagnosing BC. Convolutional neural networks (CNN)-based classification with transfer learning enhances efficiency and accuracy, especially with limited datasets. This study evaluates the performance of different pretrained deep CNN architectures in classifying pathological mammography scans from the Mini-MIAS dataset. The results show that Xception, VGG16, VGG19, and MobileNetV2 achieve the highest accuracy (97%), with VGG19 demonstrating the fastest prediction speed (0.53 s).

Source: International Journal of Online and Biomedical Engineering (iJOE)

DOI: 10.3991/ijoe.v20i12.49739

Authors: Nourane Laaffat, Ahmad Outfarouin, Walid Bouarifi, Abdelilah Jraifi

Institutions: Cadi Ayyad University, Université Ibn Zohr

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: gold

Estimated APC paid: $312