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Cancer is an extremely difficult and dangerous health problem because itmanifests in so many different ways and affects so many different organs andtissues. The primary goal of this research was to evaluate deep learningmodels' ability to correctly identify breast cancer cases using the BreakHisdataset. The BreakHis dataset covers a wide range of breast cancer subtypesthrough its huge collection of histopathological pictures. In this study, weuse and compare the performance of five well-known deep learning models forcancer classification: VGG, ResNet, Xception, Inception, and InceptionResNet.The results placed the Xception model at the top, with an F1 score of 0.9 andan accuracy of 89%. At the same time, the Inception and InceptionResNet modelsboth hit accuracy of 87% . However, the F1 score for the Inception model was87, while that for the InceptionResNet model was 86. These results demonstratethe importance of deep learning methods in making correct breast cancerdiagnoses. This highlights the potential to provide improved diagnosticservices to patients. The findings of this study not only improve currentmethods of cancer diagnosis, but also make significant contributions to thecreation of new and improved cancer treatment strategies. In a nutshell, theresults of this study represent a major advancement in the direction ofachieving these vital healthcare goals.

Comparative Analysis of Deep Learning Architectures for Breast Cancer Diagnosis Using the BreaKHis Dataset