Development of an Algorithms for Human Tissues Characterization

Abstract

Various imaging technologies have been employed to investigate skin tissues over the years, but their inadequate sensitivity, specificity, and accuracy limit their usage. Optical coherence tomography (OCT) is a promising imaging technique in comparison to other imaging modalities since it is a non-invasive imaging modality with a high resolution that can do cellular level imaging as well as provide depth information. This imaging technique has been widely utilised to examine tissues in the human body, demonstrating its clinical promise. Furthermore, OCT can be regarded a possible tool for identification, however modern high-speed OCT systems capture a large amount of data, making human interpretation a time-consuming and tiresome operation. Computer-aided diagnostic (CAD) systems can support clinicians in diagnosing by rapidly assessing large amounts of data. The goal of this thesis is to create a CAD system that uses OCT for human tissue measurement. The feasibility of fully automated quantitative assessment based on morphological aspects of human tissue, which will become a biomarker for the removal of non-viable skin, is described in this thesis research work. We developed an automated algorithm for the classification of malignant and benign human skin tissue, using the dermoscopic images. The resulting algorithm gives a prospective approach for skin tissue characterization, which presents tangible findings in normal and melanoma infected skin tissue by statistical means. Our proposed automated procedure entails building a machine learning based classifier by extracting the features of normal and infected skin images, augmented with various classical transformations and Generative Adversarial Network. The resultant model obtained good accuracy by adding the synthetic data. Further, a robust machine learning approach was utilised to correctly and automatically identify breast cancer tissue.