Thesis Abstract

Abstract
Skin cancer is one of the most common types of cancer worldwide, with early detection being crucial for successful treatment outcomes. Machine learning techniques have shown promise in aiding the detection and classification of skin cancer through the analysis of dermatological images. This thesis explores the utilization of machine learning algorithms for skin cancer detection and classification to improve diagnostic accuracy and efficiency in clinical settings. The study begins with a comprehensive literature review to establish the current state of research in skin cancer detection using machine learning techniques. Various algorithms and methodologies employed in previous studies are critically evaluated to identify gaps in the existing literature. The research methodology section outlines the data collection process, preprocessing steps, feature extraction techniques, and model training approaches utilized in this study. The primary objective of this research is to develop a machine learning-based system capable of accurately detecting and classifying different types of skin cancer from dermatological images. A dataset consisting of diverse skin lesion images is utilized to train and evaluate the performance of the developed models. The findings from the experimental evaluation are presented and analyzed in detail in the discussion section, highlighting the strengths and limitations of the proposed approach. The significance of this study lies in the potential to enhance the diagnostic capabilities of healthcare professionals in identifying skin cancer at an early stage, thereby improving patient outcomes and reducing the burden on healthcare systems. The research contributes to the growing body of literature on the application of machine learning in dermatology and provides insights into the challenges and opportunities in implementing such systems in clinical practice. In conclusion, this thesis demonstrates the effectiveness of machine learning algorithms in skin cancer detection and classification, showcasing their potential to assist dermatologists in making accurate and timely diagnoses. The study underscores the importance of continued research and development in this field to harness the full capabilities of artificial intelligence in improving healthcare outcomes. Future work may focus on refining the proposed models, integrating additional data sources, and validating the approach through clinical trials to facilitate real-world deployment of machine learning systems for skin cancer diagnosis.

Thesis Overview