1. Ph.D Theses
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Item Detection of Breast Cancer Using Thermal Patterns and Artificial Intelligence(National Institute of Technology Karnataka, Surathkal., 2024) Venkatapathy, Gonuguntla; N., GnanasekaranBreast cancer remains one of the leading causes of cancer-related deaths among women worldwide, emphasizing the crucial need for early detection to enhance treatment outcomes and survival rates. While advanced screening technologies have significantly benefited developed countries, underdeveloped and developing nations face significant challenges due to limited access and high costs. Traditional screening methods, though effective, are often invasive, uncomfortable, costly, and expose patients to harmful radiation. Present study explores the potential of thermography as a non-invasive, cost-effective adjunct tool for breast cancer screening. By utilizing advanced computational techniques and artificial intelligence, the study aims to improve the accuracy and reliability of breast cancer detection through thermal patterns on the surface of the breast. The initial phase of the research investigates the feasibility of using breast skin surface temperature variations, caused by underlying tumors, to estimate tumor size and location. A simplified two-dimensional numerical model is developed using COMSOL Multiphysics software to simulate breast thermal patterns resulting from underlying tumors. A dataset comprising of surface temperatures is generated that are correlated with tumor diameters and locations. This dataset is then used to train an artificial neural network, which demonstrates that thermography can serve as an effective adjunct screening tool alongside the more invasive gold standard technique, mammography. The study further develops a comprehensive numerical thermal image dataset through successive numerical simulations, addressing the absence of actual labelled thermal images. A three-dimensional breast model, representing a spherical tumor within a hemispherical breast, is created to simulate numerical thermal images for different sizes and locations of the tumor. Various machine learning regression models including linear regression, support vector regression, K-nearest neighbor regression, and decision tree regression, are evaluated. Among these, the decision tree regression model shows superior predictive performance, effectively distinguishing minor temperature variations that correspond to tumor characteristics. In the following phase, instead of numerical thermal image data a limited set of temperature data on the surface of the breast used to train a random forest machine learning algorithm. Random forest, which is an ensemble of decision trees, accurately estimates the tumor size and location, demonstrating the effectiveness of using temperature patterns data in breast cancer detection. The final phase of the research integrates real thermal imaging with deep learning to propose a novel, non-invasive breast cancer diagnosis framework. Using the database for Mastology Research with Infrared Image (DMR-IR) dataset, a specialized segmentation algorithm is developed to identify regions of interest within thermal images. These segmented images are then used to train a convolutional neural network based on the AlexNet architecture, which achieves exceptional classification accuracy. This integrated approach, combining segmentation, classification, and thermal analysis provides a reliable and cost-effective system for early breast cancer detection. Present study demonstrates that thermography, supported by computational models and artificial intelligence offers a promising supplementary tool for breast cancer screening. It bridges the gap between non-invasive imaging and precise tumor localization, contributing to the early detection and treatment of breast cancer.