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Advances in CNN-Based Breast Cancer Detection
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Author(s): Sana Salhi (Tunis El Manar University, Tunisia), Nour Fakhfakh (National School of Advanced Science and Technologies in Borj Cedria, Tunisia)and Malak Sayeb (National School of Advanced Science and Technologies in Borj Cedria, Tunisia)
Copyright: 2026
Pages: 32
Source title:
The Emerging Role of Advanced Technologies in Neurological Diseases
Source Author(s)/Editor(s): Adnène Arbi (National Institute of Applied Sciences and Technology, University of Carthage, Tunisia & Laboratory of Mathematical Engineering, Tunisia Polytechnic School, University of Carthage, Tunisia)and Walid Ben Ameur (Faculty of Sciences of Gabes, Tunisia)
DOI: 10.4018/979-8-3373-2706-8.ch002
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Abstract
This chapter investigates the recent advances in Convolutional Neural Networks for breast cancer detection in mammography. The survey reviews how emerging progress in CNN architectures are reshaping mammography interpretation and diagnostics. State-of-the-art and evaluation of optimization techniques are provided. A dedicated use case is analyzes and recommendations for model training optimization are revealed. The study is conducted on the Mammogram Mastery dataset which comprises 745 original images and 9,685 augmented images. Furthermore, the chapter addresses deep learning's potential to enhance mammographic assessment by improving diagnostic accuracy and reducing false errors. The insights presented aim to guide researchers and practitioners towards developing robust, reliable, and clinically impactful AI for breast cancer detection, highlighting key requirements for achieving advanced trustworthy and equitable outcomes.
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