Computer-aided diagnosis using white shark optimizer with attention-based deep learning for breast cancer classification

Breast cancer (BC) is categorized as the most widespread cancer among women throughout the world. The earlier analysis of BC assists to increase the survival rate of the disease. BC diagnosis on histopathology images (HIS) is a tedious process that includes recognizing cancerous regions within the microscopic image of breast tissue. There are various methods to discovering BC on HSI, namely deep learning (DL) based methods, classical image processing techniques, and machine learning (ML) based methods. The major problems in BC diagnosis on HSI are the larger size of images and the high degree of variability in the appearance of tumorous regions. With this motivation, this study develops a computer-aided diagnosis using a white shark optimizer with attention-based deep learning for the breast cancer classification (WSO-ABDLBCC) model. The presented WSO-ABDLBCC technique performs accurate classification the breast cancer using DL techniques. In the WSO-ABDLBCC technique, the Guided filtering (GF) based noise removal is applied to improve the image quality. Next, the Faster SqueezeNet model with WSO-based hyperparameter tuning performs the feature vector generation process. Finally, the classification of histopathological images takes place using attention-based bidirectional long short-term memory (ABiLSTM). A detailed experimental validation of the WSO-ABDLBCC occurs utilizing the benchmark Breakhis database. The proposed model achieved an accuracy of 95.2%. The experimental outcomes portrayed that the WSO-ABDLBCC technique accomplishes improved performance compared to other existing models.