Thesis Abstract

Abstract
Skin cancer is a significant global health concern, with early detection being crucial for successful treatment outcomes. This study presents the development of a mobile application aimed at aiding users in skin cancer detection and self-examination. The mobile application leverages image processing and machine learning algorithms to analyze images of suspicious skin lesions and provide users with immediate feedback on potential risks. Chapter 1 introduces the background of the study, highlighting the rising prevalence of skin cancer and the importance of early detection. The problem statement emphasizes the limitations of current methods for skin cancer detection and the need for accessible and accurate tools for self-examination. The objectives of the study include the development of a user-friendly mobile application for skin cancer detection, while also addressing the limitations and scope of the research. The significance of the study lies in its potential to empower individuals to monitor their skin health proactively. Chapter 2 consists of a comprehensive literature review covering ten key aspects related to skin cancer detection, mobile health applications, image processing techniques, and machine learning algorithms. This review provides a foundation for understanding existing research and technologies in the field, guiding the development of the proposed mobile application. Chapter 3 details the research methodology employed in developing the mobile application. This chapter includes sections on data collection, image preprocessing techniques, algorithm selection, model training, and application design. The methodology emphasizes the integration of user-friendly features and robust backend algorithms to ensure the accuracy and reliability of skin cancer detection. Chapter 4 presents a thorough discussion of the findings obtained through the development and testing of the mobile application. This chapter analyzes the performance metrics of the image processing and machine learning algorithms, evaluates user feedback and usability, and discusses potential areas for improvement and future research directions. Chapter 5 concludes the thesis with a summary of the key findings and contributions of the study. The conclusion highlights the efficacy of the developed mobile application in aiding users with skin cancer detection and self-examination. Recommendations for further enhancements and validation studies are provided to ensure the continued advancement of the mobile application as a valuable tool in skin health monitoring. In conclusion, the development of a mobile application for skin cancer detection and self-examination represents a significant step towards enabling individuals to take proactive measures in monitoring their skin health. The integration of image processing and machine learning technologies in a user-friendly interface holds great promise for improving early detection rates and ultimately saving lives in the fight against skin cancer.

Thesis Overview