Project Abstract

Abstract
Skin cancer is a prevalent and potentially life-threatening disease that requires early detection for successful treatment. The advancement of artificial intelligence (AI) technology has shown promising results in various medical fields, including dermatology. This research aims to investigate the use of AI in diagnosing skin cancer, specifically focusing on its accuracy, efficiency, and reliability compared to traditional diagnostic methods. The research will begin with a comprehensive literature review to explore existing studies and technologies related to AI in dermatology and skin cancer diagnosis. Various AI algorithms, such as deep learning and machine learning, will be examined to understand their capabilities in analyzing skin lesions and identifying potential signs of skin cancer. The methodology of the research will involve collecting a dataset of skin images containing both benign and malignant lesions. These images will be used to train and test different AI models to evaluate their performance in accurately diagnosing skin cancer. Factors such as sensitivity, specificity, and overall accuracy will be assessed to determine the effectiveness of AI in this context. The findings of the research will be discussed in detail, highlighting the strengths and limitations of using AI for skin cancer diagnosis. The implications of these findings on the field of dermatology and the potential benefits for patients and healthcare providers will be explored. In conclusion, this research aims to contribute to the growing body of knowledge on the application of AI in dermatology, particularly in the diagnosis of skin cancer. By leveraging the capabilities of AI technology, healthcare professionals may improve the accuracy and efficiency of skin cancer diagnosis, ultimately leading to better patient outcomes and survival rates.

Project Overview