Skin Cancer Identification Using Deep Learning Technique

The most common diseases in the world are skin problems because of hereditary features and environmental factors. It frequently suffers from both common and uncommon ailments. Skin cancer presents in various forms, including squamous cell carcinoma, basal cell carcinoma, and the highly aggressive melanoma, which accounts for the majority of fatalities associated with this condition. Dermatology poses unique diagnostic challenges due to its intricate nature, often necessitating thorough testing to accurately determine the specific type of skin disorder afflicting patients. It can be very challenging to distinguish between different skin illnesses and their different types. The person's personal knowledge of the subject also matters. It is essential to use a technique that is unrestricted by these limitations in order to detect skin diseases. Due to the complexity of human skin complexion and the visual proximity effect on the illnesses, it can be quite difficult to pinpoint the exact type of disease. Therefore, it's crucial to recognize and classify skin problems as soon as they are found. The identification of human skin diseases is thus the most complex and difficult area of research. This work presents an automated deep learning-based image-based system for recognizing and categorizing skin problems along with the implementation of new approach called federated learning for the same. In the biomedical sector, Ml & DL techniques are extensively used for segmentation and augmentation, feature extraction and classification diagnosis and detection. Advanced techniques like Convolutional Neural Network (CNN) can be employed to extract characteristics from a picture, classify the image using the EfficientNet algorithm with fairness evaluation was used to detect the two primary types of tumours, malignant and benign, using the HAM10000 dataset and using federated learning approach using different datasets, and generate a diagnostic report. Since this research will provide results more quickly and accurately than the previous procedure, it will be a more reliable and effective strategy than the conventional method for identifying dermatological problems. The necessity for a lot of manual diagnosis work might potentially be eliminated by a system that can analyze images and alert physicians to the presence of a certain type of skin cancer.