Breast Cancer Infrared Image Classification

Abstract

Breast cancer is the most typical malignancy among women. Different Researchers have devised procedures all around the world since early diagnosis leads to a better prognosis. Several studies have shown that infrared imaging is an effective test for breast cancer tool. This research provides a method for assessing infrared thermal of the breast using several procedures followed to identify patients' images as healthy or unhealthy due to disease like cancer maligancy. Many approaches, such as Support Vector Machines, rely on handmade features and classical classifiers. Breast cancer diagnosis utilising deep neural networks with attention models and transfer learning will be more accurate than using neural networks alone. Our research intends to assess how well deep learning models that have already been taught perform at identifying breast cancer. We utilise Prewitt and Roberts edge detectors to generate outputs from the raw thermal breast images. The DenseNet121 model receives the original image and these two edge-maps as input.in 3-channel picture form. It includes image preprocessing, transfer learning and deep attention on pre-trained models and visualises the result using grad Cam. Our proposed work improved the accuracy, precision, specificity, sensitivity and recall.