Development of a Machine Learning Model for Automated Skin Cancer Detection

Thesis Abstract

Abstract
Skin cancer is one of the most prevalent types of cancer worldwide, with early detection being crucial for effective treatment and improved patient outcomes. The use of machine learning algorithms in the field of dermatology has shown promising results in automating the process of skin cancer detection. This research project aims to develop a machine learning model for automated skin cancer detection, leveraging a dataset of dermatoscopic images to train and validate the model. The thesis begins with an introduction providing background information on skin cancer, the current methods of detection, and the potential benefits of using machine learning in this context. The problem statement highlights the challenges faced in accurate and timely skin cancer diagnosis, emphasizing the need for automated solutions to assist dermatologists in their decision-making process. The objectives of the study include developing a machine learning model capable of accurately classifying skin lesions as benign or malignant, evaluating the performance of the model against existing diagnostic methods, and exploring the potential impact of automated skin cancer detection in clinical practice. The limitations of the study, such as the availability and quality of the dataset, are also considered, along with the scope of the research, which focuses on the development and validation of the machine learning model. The significance of the study lies in its potential to improve the efficiency and accuracy of skin cancer diagnosis, leading to earlier detection, better treatment outcomes, and reduced healthcare costs. The structure of the thesis is outlined to guide the reader through the research process, including the methodology, findings, and conclusion. A comprehensive literature review is presented in Chapter Two, covering relevant studies on machine learning in dermatology, skin cancer detection techniques, and the use of dermatoscopic images for diagnostic purposes. The research methodology in Chapter Three details the dataset collection, preprocessing steps, feature extraction techniques, model selection, training, and evaluation procedures. Chapter Four provides a detailed discussion of the findings, including the performance metrics of the developed machine learning model, comparisons with existing diagnostic methods, and insights gained from the analysis of the results. Finally, Chapter Five concludes the thesis by summarizing the key findings, discussing the implications of the study, and suggesting avenues for future research in automated skin cancer detection. In conclusion, the development of a machine learning model for automated skin cancer detection represents a significant advancement in the field of dermatology, with the potential to revolutionize the way skin lesions are diagnosed and treated. By leveraging the power of artificial intelligence, this research project contributes to the ongoing efforts to improve healthcare outcomes and enhance patient care in the realm of skin cancer detection and management.