Deep transfer learning for IDC breast cancer detection using fast AI technique and Sqeezenet architecture

One of the most effective approaches for identifying breast cancer is histology, which is the meticulous inspection of tissues under a microscope. The kind of cancer cells, or whether they are cancerous (malignant) or non-cancerous, is typically determined by the type of tissue that is analyzed by the test performed by the technician (benign). The goal of this study was to automate IDC classification within breast cancer histology samples using a transfer learning technique. To improve our outcomes, we combined a Gradient Color Activation Mapping (Grad CAM) and image coloring mechanism with a discriminative fine-tuning methodology employing a one-cycle strategy using FastAI techniques. There have been lots of research studies related to deep transfer learning which use the same mechanism, but this report uses a transfer learning mechanism based on lightweight Squeeze Net architecture, a variant of CNN (Convolution neural network). This strategy demonstrates that fine-tuning on Squeeze Net makes it possible to achieve satisfactory results when transitioning generic features from natural images to medical images.

Automated summary

Histology is one of the most effective approaches for identifying breast cancer, which involves meticulous inspection of tissues under a microscope. The type of cancer cells and their malignant or benign nature can be determined by analyzing the type of tissue in the test. This study aims to automate the classification of breast cancer histology samples using a transfer learning technique that combines Gradient Color Activation Mapping, image coloring, and discriminative fine-tuning based on the Squeeze Net architecture.