  Cancer is an extremely difficult and dangerous health problem because it
manifests in so many different ways and affects so many different organs and
tissues. The primary goal of this research was to evaluate deep learning
models' ability to correctly identify breast cancer cases using the BreakHis
dataset. The BreakHis dataset covers a wide range of breast cancer subtypes
through its huge collection of histopathological pictures. In this study, we
use and compare the performance of five well-known deep learning models for
cancer classification: VGG, ResNet, Xception, Inception, and InceptionResNet.
The results placed the Xception model at the top, with an F1 score of 0.9 and
an accuracy of 89%. At the same time, the Inception and InceptionResNet models
both hit accuracy of 87% . However, the F1 score for the Inception model was
87, while that for the InceptionResNet model was 86. These results demonstrate
the importance of deep learning methods in making correct breast cancer
diagnoses. This highlights the potential to provide improved diagnostic
services to patients. The findings of this study not only improve current
methods of cancer diagnosis, but also make significant contributions to the
creation of new and improved cancer treatment strategies. In a nutshell, the
results of this study represent a major advancement in the direction of
achieving these vital healthcare goals.
