Ahmed Ben Atitallah1,*, Jannet Kamoun2,3, Meshari D. Alanazi1, Turki M. Alanazi4, Mohammed Albekairi1, Khaled Kaaniche1
CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5761-5779, 2025, DOI:10.32604/cmc.2025.063634
- 19 May 2025
Abstract Breast Cancer (BC) remains a leading malignancy among women, resulting in high mortality rates. Early and accurate detection is crucial for improving patient outcomes. Traditional diagnostic tools, while effective, have limitations that reduce their accessibility and accuracy. This study investigates the use of Convolutional Neural Networks (CNNs) to enhance the diagnostic process of BC histopathology. Utilizing the BreakHis dataset, which contains thousands of histopathological images, we developed a CNN model designed to improve the speed and accuracy of image analysis. Our CNN architecture was designed with multiple convolutional layers, max-pooling layers, and a fully connected… More >
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