Investigating the Use of Artificial Intelligence in the Early Detection of Skin Cancer.

Thesis Abstract

Abstract
Skin cancer is one of the most common types of cancer worldwide, with early detection playing a crucial role in successful treatment outcomes. The advancements in artificial intelligence (AI) have offered new opportunities for improving the accuracy and efficiency of skin cancer detection. This thesis investigates the use of AI in the early detection of skin cancer, aiming to enhance diagnostic processes and ultimately improve patient outcomes. The study begins with a comprehensive review of the current literature on skin cancer detection methods and the application of AI in dermatology. Various AI techniques, such as machine learning algorithms and deep learning models, are explored for their potential in analyzing skin images and identifying cancerous lesions. The research methodology section details the design and implementation of the study, including data collection methods, image preprocessing techniques, and the development of AI models for skin cancer detection. The study utilizes a dataset of skin images with annotated labels to train and test the AI models, evaluating their performance in detecting different types of skin lesions accurately. The findings from the study are discussed in depth in Chapter Four, highlighting the strengths and limitations of the AI models in early skin cancer detection. The results demonstrate the potential of AI technologies to assist dermatologists in improving diagnostic accuracy and efficiency, leading to timely interventions and better patient outcomes. In conclusion, this thesis contributes to the growing body of research on the application of AI in dermatology and skin cancer detection. The study underscores the importance of integrating AI technologies into clinical practice to enhance the capabilities of healthcare professionals in identifying and treating skin cancer at its early stages. Keywords Skin cancer, Artificial intelligence, Machine learning, Deep learning, Dermatology, Early detection, Diagnosis, Healthcare.