Skin Cancer Diagnosis with a Customized CNN Model using Deep Learning Approaches

Abstract

Medical imaging has a significant challenge in accurately classifying skin lesions into benign and malignant classifications. To solve this issue, we have developed a technique that utilizes a custom convolutional neural network classifier with a support vector machine. Our customized CNN architecture is designed to address the core issue of skin cancer categorization. DenseNet121, DenseNet201, InceptionV3, InceptionResNetV2, MobileNet, ResNet50V2, ResNet101, VGG16, VGG19, and Xception are among the most prominent pre-trained models evaluated in our study. The customized CNN exceeds existing models on an average basis, displaying greater accuracy, recall, precision, and F1-Score for both benign and malignant cases. This technique has significant prospects for enhancing early skin cancer diagnosis, perhaps leading to better patient results and more efficient medical treatments.