Breast Cancer Detection Cum Classification using Feature Optimization and Transfer Learning
Ashish R. Dandekar · Panamerican mathematical journal. · 2024
Early detection of breast cancer can significantly reduce its high mortality rate. However, manual diagnosis from mammography images always requires a skilled professional. Various researchers have developed artificial intelligence-based methods to address this challenge, yet they encounter several issues such as overlapping cancerous and noncancerous regions, extraction of irrelevant features, and inadequate training models. In this paper, we propose a novel computationally automated biological mechanism for categorizing breast cancer. This approach leverages a new optimization technique based on the Moth Flame Optimization (MFO) algorithm, which enhances the classification of breast cancer cases. The proposed framework consists of two main stages: Utilizing Deep Neural Networks (DNN) based on transfer learning. Implementing a Convolutional Neural Network (DCNN) optimized with MFO. By integrating transfer learning with an optimized DCNN for classification, our method significantly improves accuracy compared to recent approaches. The proposed framework was evaluated using publicly available datasets, achieving an average classification accuracy of 97.95%. To ensure the statistical significance and robustness of our results, we conducted tests that demonstrated the effectiveness and superior performance of our methodology compared to current methods.