<![CDATA[Breast cancer is one of the deadliest types of cancer among women worldwide. Early detection plays a crucial role in increasing the chances of successful treatment and reducing the risk of death. Various efforts have been made by both the general public and medical professionals to raise awareness, promote early screening, and ensure timely medical intervention. With advances in technology, the use of computer-based systems, particularly in the field of medical image analysis, has become increasingly important. One such application is histopathological image analysis to support the diagnostic process in breast cancer cases. Histopathological image classification has gained significant attention from researchers in recent years, and various machine learning and deep learning techniques have been applied to improve its accuracy. Convolutional Neural Networks (CNNs), as part of the deep learning framework, have shown promising results in identifying tissue patterns in histopathological images. However, despite their high accuracy, CNNs are often less interpretable, making it difficult to understand the reasoning behind their predictions—especially when dealing with subtle features such as small spots, dots, or fine lines that may be overlooked. This study addresses these limitations by proposing a method that not only classifies histopathological images with high accuracy but also enhances readability through localization techniques. The goal is to make the classification process more transparent and clinically useful. Using widely recognized datasets like BreakHIS, the proposed method achieves a classification accuracy of up to 97.50%, demonstrating its potential as a reliable tool in medical diagnostics and breast cancer research.]]>
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