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.

Thesis Overview

The project titled "Development of a Machine Learning Model for Automated Skin Cancer Detection" focuses on the application of advanced machine learning techniques in the field of dermatology to enhance the detection and diagnosis of skin cancer. Skin cancer is a significant public health concern globally, and early detection plays a crucial role in improving patient outcomes. Traditional methods of skin cancer detection rely heavily on visual inspection by dermatologists, which can be subjective and prone to human error. The primary objective of this research is to develop a machine learning model that can accurately and efficiently classify skin lesions as either benign or malignant based on image data. By leveraging the power of machine learning algorithms, this model aims to improve the accuracy, speed, and consistency of skin cancer diagnosis, ultimately leading to better patient care and outcomes. The research will begin with a comprehensive review of existing literature on skin cancer detection methods, machine learning algorithms, and their applications in dermatology. This literature review will provide a solid foundation for understanding the current state of the art in the field and identifying gaps that the proposed model aims to address. The methodology chapter will outline the process of data collection, preprocessing, feature extraction, model selection, training, and evaluation. Various machine learning algorithms, such as convolutional neural networks (CNNs), support vector machines (SVMs), and random forests, will be explored and compared to identify the most suitable approach for skin cancer detection. The discussion of findings chapter will present the results of the developed machine learning model in terms of accuracy, sensitivity, specificity, and other performance metrics. The model will be evaluated using a dataset of skin lesion images with ground truth labels to assess its effectiveness in detecting skin cancer accurately. In conclusion, this research aims to contribute to the advancement of automated skin cancer detection through the development of a robust machine learning model. By harnessing the power of artificial intelligence and image analysis, the proposed model has the potential to revolutionize the field of dermatology and improve the early detection and diagnosis of skin cancer, ultimately saving lives and reducing the burden on healthcare systems.