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The most serious disease that affect the women worldwide is breast cancer. Early identification of breast cancer contributes to lower death rates. Through the process of histopathological examination, a pathologist looks at the tissues’ microscopic features to find different malignant patterns. However, the process of manual histological analysis is challenging, unreliable, and time-consuming. Therefore, in order to determine the malignancy, a computer-aided system created to analyze these images must take these two magnifications into account completely. As a result, this research suggests a distinct deep neural algorithms for the categorization of breast cancer histological images, designed to imitate a pathologist. ResNet-50 focuses on possible locations that are relevant for the precise classification of tumor images by utilizing global and local information in an integrated manner. Preprocessing of the images with …