Machine Learning for Automated Skin Cancer Diagnosis and Classification

Thesis Abstract

Abstract
This thesis focuses on the application of machine learning techniques for the automated diagnosis and classification of skin cancer. Skin cancer is one of the most common types of cancer globally, and early detection plays a crucial role in improving patient outcomes. Traditional methods of diagnosing skin cancer rely on visual inspection by dermatologists, which can be subjective and time-consuming. Machine learning algorithms have shown promising results in assisting with the automated analysis of skin lesions to aid in the early detection and classification of skin cancer. The research begins with an introduction to the problem statement, highlighting the challenges faced in traditional methods of diagnosing skin cancer and the potential benefits of utilizing machine learning algorithms for automated diagnosis. The background of the study provides an overview of skin cancer, its types, causes, and prevalence, emphasizing the importance of early detection and classification. The objectives of the study are outlined to develop a robust machine learning model that can accurately diagnose and classify skin lesions. The literature review delves into existing studies and research related to machine learning applications in dermatology, particularly in the field of skin cancer diagnosis. The review covers various machine learning algorithms, image processing techniques, and datasets used in previous studies, laying the foundation for the methodology adopted in this research. The research methodology section details the data collection process, feature extraction methods, model selection, training, and evaluation procedures. The methodology encompasses the steps involved in preprocessing skin lesion images, extracting relevant features, training machine learning models, and evaluating their performance using appropriate metrics. The discussion of findings presents the results obtained from the experiments conducted during the research. The performance of the developed machine learning model in diagnosing and classifying skin lesions is critically analyzed, highlighting the strengths and limitations of the approach. The findings are compared with existing literature and state-of-the-art methods to showcase the effectiveness of the proposed model. In conclusion, this thesis provides valuable insights into the potential of machine learning for automated skin cancer diagnosis and classification. The significance of the study lies in its contribution to improving the accuracy and efficiency of skin cancer diagnosis, ultimately leading to better patient outcomes. The limitations and scope of the study are acknowledged, along with recommendations for future research in this field. Overall, this research underscores the importance of leveraging technology to enhance medical diagnostics and improve healthcare outcomes.