Skin cancer severity analysis and prediction framework based on deep learning

This paper provides a Convolutional Neural Network (CNN) based skin cancer detection model that categorizes skin lesions as benign or malignant. To increase its resilience, the model makes use of picture enhancing strategies such data augmentation and normalization. The approach automatically collects characteristics from pictures via the construction of a deep convolutional network, hence enabling efficient categorization. The dataset was divided into training and validation sets inside the experimental framework, using convolutional layers, max pooling, and fully connected layers—common deep learning architectures. We used the Adam optimizer for performance optimization and binary cross-entropy as the loss function in a binary classification challenge. We assessed the model's performance during training by looking at parameters like accuracy and AUC (Area Under the Curve). The testing results show that the model detects skin cancer with excellent accuracy and reliability; on the test set, both accuracy and AUC approach substantial values. In addition, we used a confusion matrix to determine the model's sensitivity, specificity, and positive predictive value, which allowed for a more thorough examination of its categorization capabilities. The results indicate that the CNN-based model has a great deal of promise for use in skin cancer diagnosis, providing a theoretical framework for later practical application.