Hybrid sigmoid activation function and transfer learning assisted breast cancer classification on histopathological images

Breast cancer is the most widespread form of cancer diseases among women. Such oncoviral cancer starts in the epithelial lining of the lobules or ducts in the breast gland tissue. Identifying and classifying breast cancer presents a significant challenge for researchers and scientists. Neural networks have emerged as a powerful tool for classifying cancer data through feature extraction. This paper addresses the challenge of accurately classifying breast cancer using a novel approach that combines a hybrid sigmoid activation function (HSAF) with transfer learning, utilizing the pre-trained EfficientNetB6 model. The HSAF is specifically designed to capture complex patterns within histopathological images, while transfer learning leverages prior knowledge from the pre-trained model. In our experimental approach, we employ a breast histopathological image dataset, dividing it into three segments: 60% for training, 20% for validation, and 20% for testing. Furthermore, data augmentation techniques are performed to increase the size of training data. The experimental results of this research indicate an impressive precision, recall, and F1 score of 91%. Furthermore, our proposed model is compared to existing methods, demonstrating its efficiency. We also conduct a comparative study of activation functions (AFs), highlighting the classification performance of HSAF for breast cancer. This research not only advances our ability to classify breast cancer more accurately but also serves as a catalyst for raising awareness and alleviating concerns related to breast cancer. By integrating advanced technology and innovative techniques, this paper aims to make a meaningful contribution to the early detection and effective treatment of this widespread and life-affecting disease.