Dual-Tree Complex Wavelet Pooling and Attention based Modified U-Net Architecture for Automated Breast Thermogram Segmentation and Classification

Abstract

Thermography is a non-invasive and non-contact method for detecting cancer in their initial stages by examining the temperature variation between both breasts. Resizing, ROI (region of interest) segmentation, and augmentation are frequently used methods for pre-processing breast thermograms. In this study, a modified U-Net architecture (DTCWAU-Net) that uses Dual-Tree Complex Wavelet Transform (DTCWT) and Attention gate for breast thermal image segmentation for frontal and lateral view thermograms, aiming to outline ROI for potential tumor detection was proposed. The proposed approach achieved an average Dice coefficient of 93.03% and a sensitivity of 94.82%, showcasing its potential for accurate breast thermogram segmentation. The automated segmentation of breast thermograms into categories as healthy or cancerous was achieved by texture and histogram-based feature and deep feature extraction from these segmented thermograms, feature selection using Neighborhood Component Analysis (NCA), and applying machine learning classifiers. When compared to other state-of the art approaches for detecting breast cancer using thermogram, the proposed methodology showed higher accuracy. Simulation results clearly expounds that the proposed method can be used in breast cancer screening, facilitating early detection, and enhancing treatment outcomes.