Transfer learning-based diagnosis of breast cancer

被引：0

作者：

Yedjour, Hayat ^{[1
]}

Meftah, Bouchra ^{[1
]}

Merzoug, Hasnia ^{[1
]}

Yedjour, Dounia ^{[1
]}

机构：

[1] Univ Sci & Technol Oran Mohamed Boudiaf, Dept Comp Sci, BP 1505, El Mnaouer 31000, Algeria

来源：

2024 1ST INTERNATIONAL CONFERENCE ON ELECTRICAL, COMPUTER, TELECOMMUNICATION AND ENERGY TECHNOLOGIES, ECTE-TECH | 2024年

关键词：

Computer-aided detection; Convolutional neural networks; Transfer learning; Classification; ROI patches; MAMMOGRAPHY;

D O I：

10.1109/ECTE-TECH62477.2024.10851182

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Among cancer types affecting women worldwide, breast cancer ranks as one of the leading causes of death, and early detection through screening is vital for improving survival outcomes. This study presents a deep learning-based method for classifying mammogram images into benign and malignant categories using the MIAS dataset. The proposed approach includes preprocessing mammograms, extracting Region of Interest patches, and classifying them using Convolutional Neural Networks with Transfer Learning and data augmentation to address data scarcity. The evaluation results demonstrate that the framework achieves promising classification performance.

引用

页数：6

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