Classification of Breast Cancer Using Transfer Learning and Advanced Al-Biruni Earth Radius Optimization

Abstract

Breast cancer is one of the most common cancers in women, with an estimated 287,850 new cases identified in 2022. There were 43,250 female deaths attributed to this malignancy. The high death rate associated with this type of cancer can be reduced with early detection. Nonetheless, a skilled professional is always necessary to manually diagnose this malignancy from mammography images. Many researchers have proposed several approaches based on artificial intelligence. However, they still face several obstacles, such as overlapping cancerous and noncancerous regions, extracting irrelevant features, and inadequate training models. In this paper, we developed a novel computationally automated biological mechanism for categorizing breast cancer. Using a new optimization approach based on the Advanced Al-Biruni Earth Radius (ABER) optimization algorithm, a boosting to the classification of breast cancer cases is realized. The stages of the proposed framework include data augmentation, feature extraction using AlexNet based on transfer learning, and optimized classification using a convolutional neural network (CNN). Using transfer learning and optimized CNN for classification improved the accuracy when the results are compared to recent approaches. Two publicly available datasets are utilized to evaluate the proposed framework, and the average classification accuracy is 97.95%. To ensure the statistical significance and difference between the proposed methodology, additional tests are conducted, such as analysis of variance (ANOVA) and Wilcoxon, in addition to evaluating various statistical analysis metrics. The results of these tests emphasized the effectiveness and statistical difference of the proposed methodology compared to current methods.