Project Abstract

Abstract
Skin cancer is a prevalent and potentially life-threatening condition that requires early detection for effective treatment. In recent years, advancements in technology have paved the way for the development of computer-aided diagnosis systems to assist in the early detection of skin cancer. This research project aims to design and implement a Computer-Aided Diagnosis System for Skin Cancer Detection, utilizing image processing and machine learning techniques to improve accuracy and efficiency in diagnosing skin cancer. The research begins with a comprehensive literature review in Chapter Two, exploring existing techniques and technologies in the field of skin cancer detection. Various studies on image processing, machine learning algorithms, and diagnostic systems will be reviewed to provide a solid foundation for the development of the proposed system. Chapter Three details the research methodology employed in this project, which includes data collection, preprocessing, feature extraction, model training, and evaluation. The methodology will involve the use of a dataset comprising images of skin lesions with different types of skin cancer for training and testing the developed system. Chapter Four presents the findings of the research, including the performance evaluation of the Computer-Aided Diagnosis System for Skin Cancer Detection. The results will be analyzed in terms of accuracy, sensitivity, specificity, and other relevant metrics to assess the effectiveness of the system in detecting skin cancer accurately. The final chapter, Chapter Five, concludes the research by summarizing the key findings, discussing the implications of the study, and recommending future research directions. The significance of the developed Computer-Aided Diagnosis System for Skin Cancer Detection in improving early detection and treatment outcomes will be highlighted, along with potential areas for further enhancement and refinement. Overall, this research project aims to contribute to the field of dermatology by developing an innovative and efficient tool for early detection of skin cancer. By leveraging the power of image processing and machine learning, the proposed system has the potential to assist healthcare professionals in diagnosing skin cancer more accurately and swiftly, ultimately improving patient outcomes and saving lives.

Project Overview