Enhanced Melanoma Classifier with VGG16-CNN

Melanoma is the most severe kind of skin cancer that is becoming more common in the Western world. This is still thought to be caused primarily by exposure to the sun. Patients with malignant melanoma have a wide range of prognoses; however public awareness initiatives encouraging early detection have resulted in considerable reductions in mortality rates. This disease primarily affects Caucasian men and women and has a terrible prognosis once it has spread to other parts of the body. As a result, early detection of this malignancy is critical for patient treatment success.

In this paper, we present an experimental result of a Melanoma Image Classifier using the VGG16 model for preprocessing the images dataset. Thedataset comprises 4596 image samples with 2239 images for training, 2239 images formodel validationand 118 images for testing the model. The resultant images were trained with a Convolutional Neural Network(CNN) Sequential model of a learning rate of 0.0001,adam optimizer with binary cross-entropy as loss and accuracy as a metric. The model yields an accuracy of 93%, thereby outperforming other Deep learning models. The approach is viable and effective, and it achieves the preliminary goal of classifying melanoma lesion images.