Thesis Abstract

Abstract
Skin cancer is a prevalent and potentially life-threatening disease that requires early detection and accurate diagnosis for effective treatment. In recent years, advancements in deep learning algorithms have shown promising results in various medical imaging applications, including dermatology. This research project aims to develop a Computer-Aided Diagnosis (CAD) system for the automated detection of skin cancer using deep learning algorithms. The proposed system will analyze dermoscopic images of skin lesions to assist dermatologists in making more accurate and timely diagnoses. The research methodology involves collecting a diverse dataset of dermoscopic images from various sources, including public databases and clinical settings. These images will be pre-processed to enhance quality and standardize the data for training the deep learning models. A comprehensive literature review will be conducted to analyze existing approaches and algorithms for skin cancer detection, providing a solid foundation for the design and implementation of the CAD system. The CAD system will be developed using state-of-the-art deep learning techniques, such as convolutional neural networks (CNNs) and transfer learning, to extract relevant features and classify skin lesions into different categories (benign, malignant, or uncertain). The performance of the system will be evaluated using metrics such as sensitivity, specificity, accuracy, and area under the curve (AUC) to assess its effectiveness in detecting skin cancer. The findings of this research will contribute to the advancement of computer-aided diagnosis systems in dermatology, providing a valuable tool for healthcare professionals to improve the early detection and diagnosis of skin cancer. The potential benefits of the proposed CAD system include reducing diagnostic errors, enhancing patient outcomes, and optimizing healthcare resources. In conclusion, the development of a Computer-Aided Diagnosis System for Skin Cancer Detection using Deep Learning Algorithms represents a significant step towards leveraging artificial intelligence in dermatology for more accurate and efficient diagnosis. This research project addresses a critical need in the field of dermatology and has the potential to make a positive impact on patient care and outcomes.

Thesis Overview