Breast cancer prediction using a pretrained CNN Model ResNet-50

Abstract

Early diagnosis and administration of suitable treatment can substantially enhance the probability of human survival from breast cancer. This study utilized a large dataset comprising thousands of labeled breast images representing various types of breast cancer to train and validate the ResNet-50 model. Important features were extracted from the images using the residual blocks of the network and then fine-tuned for optimal performance. The experiments demonstrated that the ResNet-50 model achieved a fair level of accuracy in differentiating various forms of breast cancer, such as benign, malignant, and others. The ResNet-50 performed reasonably well, identifying benign, malignant, and normal cases with 98% accuracy when using accuracy as the metric, 97% when using precision, and 100% when using recall. Consequently, the trained ResNet-50 model was combined with the Flask framework to generate a simple user interface. These results suggest that employing residual networks in detecting breast cancer can significantly aid in early diagnosis and treatment. This study has important implications for public health and medical practice, providing physicians with a valuable resource in their fight against breast tumors.