Özel Derin Konvolüsyonel Sinir Ağı Kullanarak Cilt Kanseri Tanıma

Abstract

Skin cancer is a common form of cancer that affects millions of people worldwide. Early detection and accurate diagnosis of skin cancer are crucial for effective treatment and management of the disease. There has been a growing interest in using deep learning techniques and computer vision algorithms to develop automated skin cancer detection systems in recent years. Among these techniques, convolutional neural networks (CNN) have shown remarkable performance in detecting and classifying skin lesions. This paper presents a comprehensive study using CNN and deep learning techniques for skin cancer detection using the International Skin Imaging Collaboration (ISIC) dataset. The proposed architecture is a compact deep CNN that is trained using a dataset of benign and malignant skin lesion images. The proposed architecture has achieved 84.8% accuracy, 83.8% TPR, 83.7% TNR, 81.6% F1-score and 80.5% precision for performance evaluation. The experimental results show promising results for the accurate and efficient detection of skin cancer, which has the potential to improve the diagnosis and treatment of this life-threatening disease.