Skin Cancer Classification and Detection Using Deep Learning

Abstract

Cancer is one of the most lethal diseases that threatens humanity today. Skin cancer is one of the most dangerous cancers. Skin cancer rates are the 6th most common type of cancer that is increasing globally. Patient's chances of survival are very low when they get inadequate therapy and an inaccurate diagnosis. The patient's prognosis and likelihood of survival improve with each passing day that the illness is detected. As a result, early diagnosis and treatment may be challenging and intricate. To tackle these problems, we need a technology that can diagnose and identify skin cancer sooner on its own. To achieve the best results from the CNN model, we started by preparing the publicly available dataset. This involved improving the photos’ color, contrast, and clarity, and then resizing and fine-tuning the data. We tested various CNN models with these enhanced datasets and found that EfficientNetB7 performed the best, achieving a score of 86.81%. To further improve performance, we made additional adjustments to the EfficientNetB7 model by adding more layers and tweaking hyperparameters. We evaluated the performance of the final model using several metrics, including sensitivity, specificity, misclassification rate, and accuracy. The model achieved an accuracy of 95.98%, a specificity of 85.4%, a sensitivity of 86.38%, and a misclassification rate of 9.39%. This led to our suggested approach surpassing state-of-the-art CNN designs and techniques.