Deep Learning and Blockchain for Accurate Skin Cancer and Disease Detection
Dr.A. Suneetha1, B. Niharika2, A. Meghana3, B.V. Kousalya4 , B.Bhavyasri Pravallika5
1Associate Professor, Department of Computer Science and Engineering, KKR AND KSR INSTITUTE OF TECHNOLOGY AND SCIENCES (AUTONOMOUS), GUNTUR
2Student, Department of Computer Science and Engineering, KKR AND KSR INSTITUTE OF TECHNOLOGY AND SCIENCES (AUTONOMOUS), GUNTUR
3Student, Department of Computer Science and Engineering, KKR AND KSR INSTITUTE OF TECHNOLOGY AND SCIENCES (AUTONOMOUS), GUNTUR
4Student, Department of Computer Science and Engineering, KKR AND KSR INSTITUTE OF TECHNOLOGY AND SCIENCES (AUTONOMOUS), GUNTUR
5Student, Department of Computer Science and Engineering, KKR AND KSR INSTITUTE OF TECHNOLOGY AND SCIENCES (AUTONOMOUS), GUNTUR
---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Skin cancer is a significant global health concern, primarily caused by excessive exposure to ultraviolet (UV) radiation. If not detected and treated early, skin cancer can spread to vital organs such as the lungs, brain, and liver, complicating treatment and reducing survival rates. Early detection is crucial for maximizing recovery chances and improving patient outcomes. This research proposes a deep learning-based application to enhance the accuracy and efficiency of skin cancer and disease prediction. This study combines Artificial Intelligence (AI) and blockchain technology to create a secure and efficient system. Patients can upload skin images, which are analysed by AI models, such as convolutional neural networks (CNNs) and logistic regression, to predict skin diseases, including cancer. Blockchain technology ensures data immutability, transparency, and security. Based on AI predictions, the system recommends verified doctors based on specialty and availability. This innovative approach aims to provide a reliable solution for early detection and treatment of skin cancer and related diseases.
Key Words: Convolutional neural networks, DenseNet, ResNet, EfficientNet, blockchain, smart contracts.
