Integrating VGG 19 U-Net for Breast Thermogram Segmentation and Hybrid Enhancement With Optimized Classifier Selection: A Novel Approach to Breast Cancer Diagnosis

Early diagnosis of breast cancer is essential for improving patient survival rates and reducing treatment costs. Despite breast thermogram images having high quality, doctors in developing countries often struggle with early diagnosis due to difficulties in interpreting subtle details. Implementing a Computer-Aided Diagnosis (CAD) system can assist doctors in accurately analyzing these details. This article presents an innovative approach to breast cancer diagnosis using thermal images. The proposed method enhances the quality and clarity of relevant features while preserving sharp and curved edges through U-Net-based segmentation for automatic selection of the ROI, advanced hybrid image enhancement techniques, and a machine learning classifier. Subjective analysis compares the processed images with five conventional enhancement techniques, demonstrating the efficiency of the proposed method. The quantitative analysis further validates the effectiveness of the proposed method against five conventional methods using four quality measures. The proposed method achieves superior performance with PSNR of 15.27 for normal and 14.31 for malignant images, AMBE of 6.594 for normal and 7.46 for malignant images, SSIM of 0.829 for normal and 0.80 for malignant images, and DSSIM of 0.084 for normal and 0.14 for malignant images. The classification phase evaluates four classifiers using 13 features from three categories. The Random Forest (RF) classifier with Discrete Wavelet Transform (DWT) based features initially outperformed other classifier features but had limited performance, with accuracy, sensitivity and specificity of 81.8%, 88.8%, and 91%, respectively. To improve this, three categories of features were normalized and converted into two principal components using Principal Component Analysis (PCA) to train the RF classifier, which then showed superior performance with 97.7% accuracy, 96.5% sensitivity, and 98.2% specificity. The dataset utilized in this article is obtained from the Indira Gandhi Centre for Atomic Research (IGCAR), Kalpakkam, India. The entire proposed model is implemented in a Jupyter notebook.