Thesis Abstract

Abstract
Skin cancer is a significant public health concern worldwide, with early detection and classification playing a crucial role in improving patient outcomes. This thesis explores the utilization of Artificial Intelligence (AI) techniques for the detection and classification of skin cancer, aiming to enhance diagnostic accuracy and efficiency. The study focuses on the development and evaluation of AI algorithms that can analyze dermatological images to differentiate between benign and malignant skin lesions, ultimately aiding dermatologists in making more accurate diagnoses. The thesis begins with an introductory chapter that provides background information on skin cancer, the current challenges in its detection and classification, and the potential of AI in addressing these challenges. The problem statement highlights the limitations of existing diagnostic methods and emphasizes the need for more advanced technologies to improve accuracy and speed of diagnosis. The objectives of the study are outlined, aiming to develop AI models that can effectively detect and classify skin cancer lesions based on image analysis. The literature review chapter critically examines existing research on AI applications in dermatology, highlighting the strengths and limitations of various algorithms and methodologies. The chapter synthesizes the findings of previous studies to identify gaps in the current knowledge and propose novel approaches for skin cancer detection and classification. The research methodology chapter details the experimental design, dataset collection, preprocessing techniques, feature extraction methods, and the development of AI models for skin cancer detection and classification. The chapter also describes the evaluation metrics used to assess the performance of the AI algorithms, including sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve. The discussion of findings chapter presents the results of the experiments conducted to evaluate the performance of the developed AI models. The chapter analyzes the accuracy, sensitivity, and specificity of the models in detecting and classifying skin cancer lesions, comparing them to traditional diagnostic methods. The findings provide insights into the effectiveness of AI in improving diagnostic accuracy and efficiency in dermatology. Finally, the conclusion and summary chapter summarize the key findings of the study, highlighting the contributions to the field of dermatology and the implications for clinical practice. The chapter discusses the strengths and limitations of the study, proposes future research directions, and emphasizes the significance of utilizing AI for skin cancer detection and classification in improving patient outcomes. Overall, this thesis contributes to the growing body of research on AI applications in dermatology, demonstrating the potential of advanced technologies in enhancing diagnostic accuracy and efficiency in skin cancer detection and classification. The findings of this study have implications for clinical practice, highlighting the importance of integrating AI tools into dermatological practice to improve patient care and outcomes.

Thesis Overview

The project titled "Utilizing Artificial Intelligence for Skin Cancer Detection and Classification" aims to leverage the power of artificial intelligence (AI) in the field of dermatology to enhance the accuracy and efficiency of skin cancer diagnosis and classification. Skin cancer is a significant global health issue, with melanoma being one of the deadliest forms of skin cancer. Early detection and accurate classification of skin lesions are critical for successful treatment and improved patient outcomes. Traditional methods of skin cancer diagnosis rely on visual inspection by dermatologists, which can be subjective and prone to errors. By integrating AI technologies, such as deep learning algorithms and image recognition systems, this project seeks to develop a more objective and reliable approach to skin cancer detection and classification. The research will involve collecting a large dataset of skin lesion images, including various types of skin cancers and benign lesions, to train the AI model. The AI system will be designed to analyze and interpret these images, identifying key features and patterns indicative of different skin conditions. Through a process of machine learning, the AI model will be refined and optimized to achieve high levels of accuracy in identifying skin cancer lesions and categorizing them into specific types. The project will also explore the integration of AI-based diagnostic tools with existing clinical practices in dermatology. Dermatologists will have the opportunity to use the AI system as a supportive tool in their decision-making process, helping them to make more informed and timely diagnoses. Ultimately, the research aims to demonstrate the potential of AI technology in revolutionizing skin cancer diagnosis and classification. By improving the efficiency and accuracy of skin cancer detection, this project has the potential to enhance patient care, facilitate early intervention, and contribute to better overall outcomes in the management of skin cancer.