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Development of a Mobile Application for Skin Cancer Detection and Monitoring

 

Table Of Contents


Chapter ONE

: Introduction 1.1 Introduction
1.2 Background of Study
1.3 Problem Statement
1.4 Objective of Study
1.5 Limitation of Study
1.6 Scope of Study
1.7 Significance of Study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter TWO

: Literature Review 2.1 Overview of Dermatological Conditions
2.2 Skin Cancer Detection Methods
2.3 Mobile Applications in Dermatology
2.4 Technologies for Skin Cancer Monitoring
2.5 Importance of Early Detection in Dermatology
2.6 Artificial Intelligence in Dermatological Diagnosis
2.7 Challenges in Skin Cancer Detection
2.8 Impact of Skin Cancer on Public Health
2.9 Current Trends in Dermatology Research
2.10 Ethical Considerations in Dermatological Studies

Chapter THREE

: Research Methodology 3.1 Research Design
3.2 Data Collection Methods
3.3 Sampling Techniques
3.4 Data Analysis Procedures
3.5 Research Instruments
3.6 Ethical Considerations
3.7 Pilot Study
3.8 Validity and Reliability Measures

Chapter FOUR

: Discussion of Findings 4.1 Overview of Research Findings
4.2 Comparison with Existing Studies
4.3 Interpretation of Results
4.4 Implications of Findings
4.5 Recommendations for Practice
4.6 Suggestions for Future Research

Chapter FIVE

: Conclusion and Summary 5.1 Summary of Key Findings
5.2 Conclusions Drawn from the Study
5.3 Contributions to the Field of Dermatology
5.4 Practical Implications
5.5 Limitations of the Study
5.6 Recommendations for Further Research

Thesis Abstract

Abstract
Skin cancer is a prevalent health concern worldwide, with early detection and monitoring being crucial for successful treatment. In recent years, the advancement of mobile technologies has provided new opportunities for healthcare applications. This thesis presents the development of a mobile application specifically designed for skin cancer detection and monitoring. The aim of this project is to leverage the capabilities of mobile devices to empower users in self-assessment and monitoring of skin lesions, ultimately improving early detection and timely intervention. The development process involved a comprehensive review of existing literature on skin cancer detection methods and mobile health applications. A user-centered design approach was adopted, involving dermatologists, software developers, and end-users in the design and evaluation process. The mobile application integrates features such as image capture, analysis using artificial intelligence algorithms, lesion tracking, risk assessment, and educational resources on skin cancer prevention. The literature review highlighted the importance of early detection in improving skin cancer outcomes and the potential of mobile applications in enhancing access to healthcare services. The research methodology section details the design process, including requirements analysis, system architecture, software development tools, and evaluation methods. The evaluation involved usability testing, accuracy assessment of the image analysis algorithm, and feedback from dermatologists and end-users. The discussion of findings section presents the results of the evaluation process, highlighting the strengths and limitations of the mobile application. The findings indicate that the application is user-friendly, provides accurate lesion analysis, and enhances user engagement in skin cancer prevention. The implications of the study suggest that mobile applications have the potential to complement traditional healthcare services and improve awareness and early detection of skin cancer. In conclusion, the development of a mobile application for skin cancer detection and monitoring represents a significant advancement in leveraging technology for healthcare. The project contributes to the field of dermatology by providing a practical tool for self-assessment and monitoring of skin lesions. The thesis concludes with recommendations for further research and potential enhancements to the mobile application to enhance its effectiveness in skin cancer detection and monitoring.

Thesis Overview

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