@inproceedings{64da8c64185f42518765959c75f37fba,
title = "Deep Learning Based Methods for Breast Cancer Diagnosis",
abstract = "Breast cancer remains one of the leading causes of cancer-related deaths among women worldwide. Its impact on patients' lives and their loved ones is immense, underscoring the crucial need for more effective methods of early detection and diagnosis. This research proposes a CNN-based pipeline for classification of breast histology images. Over the past decades, artificial intelligence (AI) has emerged as a revolutionary technology in the field of medicine, offering new prospects to enhance the accuracy, speed, and accessibility of breast cancer diagnostics. It is within this context that our paper was developed, aiming to provide healthcare professionals with an efficient and reliable solution for cancer detection and classification using deep Learning. The proposed CNN model achieved an accuracy of 95.6\% and an AUC of 0.98.",
keywords = "Artificial Intelligence, Big Data, Breast cancer, Data science, Deep Learning",
author = "Sameh Souli and Amira Soltani and Rimah Amami and Yahia, \{Sadok Ben\}",
note = "Publisher Copyright: {\textcopyright} ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2026.; 8th EAI International Conference on Emerging Technologies for Developing Countries, AFRICATEK 2025 ; Conference date: 11-06-2025 Through 13-06-2025",
year = "2026",
doi = "10.1007/978-3-032-16635-7\_4",
language = "English",
isbn = "9783032166340",
series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "56--68",
editor = "Faouzi Kamoun and Lamjed Bettaieb and Fatna Belqasmi and Abderrazek Hachani and Thar Baker and Mohamed Tabaa and Adekunle Adeleke",
booktitle = "Emerging Technologies for Developing Countries - 8th EAI International Conference, AFRICATEK 2025, Proceedings",
}