Thesis Abstract

Abstract
Skin cancer is a critical health issue globally, with early detection being crucial for successful treatment outcomes. In recent years, advancements in machine learning and image processing techniques have shown promise in aiding dermatologists in the early diagnosis of skin cancer. This thesis aims to develop a machine learning algorithm for skin cancer detection using dermoscopy images, which can assist healthcare professionals in accurately identifying and classifying skin lesions. The research begins with a comprehensive review of the existing literature on skin cancer detection, machine learning algorithms, and dermoscopy image analysis. This literature review highlights the current challenges in skin cancer diagnosis and the potential of machine learning models in improving diagnostic accuracy. The methodology chapter outlines the approach taken in developing the machine learning algorithm, including data collection, preprocessing, feature extraction, model selection, and evaluation. The research methodology incorporates various machine learning techniques, such as convolutional neural networks (CNNs) and support vector machines (SVMs), to train and test the algorithm on a diverse dataset of dermoscopy images. The findings chapter presents the results of the developed machine learning algorithm, including its performance metrics, such as accuracy, sensitivity, specificity, and area under the curve (AUC). The discussion delves into the strengths and limitations of the algorithm, highlighting its potential for clinical application and areas for future improvement. In conclusion, the study demonstrates the feasibility and effectiveness of using machine learning algorithms for skin cancer detection using dermoscopy images. The developed algorithm shows promising results in accurately classifying skin lesions and has the potential to assist dermatologists in making more informed diagnostic decisions. This research contributes to the ongoing efforts to enhance the early detection and management of skin cancer, ultimately improving patient outcomes and reducing healthcare costs. Overall, this thesis lays the foundation for further research and development in the field of dermatology, showcasing the potential of machine learning algorithms in revolutionizing skin cancer diagnosis and treatment. By leveraging advanced technologies, such as machine learning and image analysis, healthcare professionals can enhance their diagnostic capabilities and provide better care to patients with skin cancer.

Thesis Overview

"Development of a Machine Learning Algorithm for Skin Cancer Detection using Dermoscopy Images" aims to address the critical need for accurate and efficient skin cancer detection through the utilization of advanced technology. Skin cancer is a prevalent and potentially life-threatening disease, making early detection essential for successful treatment outcomes. Dermoscopy, a non-invasive imaging technique that allows for the visualization of skin structures not visible to the naked eye, has shown promise in improving diagnostic accuracy. Machine learning algorithms have garnered significant attention in the healthcare field for their ability to analyze vast amounts of data and identify patterns that may not be apparent to human observers. By leveraging dermoscopy images and machine learning techniques, this project seeks to develop a robust algorithm capable of accurately detecting skin cancer at an early stage. The research will commence with a comprehensive literature review to explore existing studies related to skin cancer detection, dermoscopy imaging, and machine learning applications in dermatology. This review will provide a solid foundation for understanding the current state of the art and identifying gaps in the existing research that the proposed algorithm aims to address. Following the literature review, the research methodology will be detailed, outlining the steps involved in data collection, preprocessing, feature extraction, model development, and evaluation. The project will utilize a dataset of dermoscopy images containing various types of skin lesions to train and validate the machine learning algorithm. The core of the project will involve the development and optimization of the machine learning algorithm for skin cancer detection. Various machine learning techniques, such as convolutional neural networks (CNNs) and support vector machines (SVMs), will be explored to determine the most effective approach for analyzing dermoscopy images and distinguishing between benign and malignant lesions. The findings of the project will be presented and discussed in Chapter Four, highlighting the performance metrics of the developed algorithm, including sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC). The discussion will also delve into the strengths and limitations of the algorithm, potential areas for improvement, and implications for clinical practice. In conclusion, the project will summarize its key findings, contributions to the field of dermatology, and recommendations for future research directions. The ultimate goal of the research is to enhance skin cancer detection accuracy, facilitate early diagnosis, and improve patient outcomes through the innovative application of machine learning technology to dermoscopy images."