Transfer Learning-Based Skin Tumor Identification Improvement

This study addresses the critical challenge of accurately identifying skin disorders as benign, malignant, or non-tumors, essential for timely and successful treatment.Early identification can greatly minimize tumor development and cut fatality rates.Given the high costs involved with standard medical detection approaches, this research addresses using sophisticated Convolutional Neural Networks (CNNs) with transfer learning to categorize skin malignancies efficiently.Specifically, the study assesses the performance of MobileNetV2, VGG16, and VGG19 architectures.The primary objective is to find which model has the maximum accuracy in classifying skin cancers.Our findings reveal that while a standard CNN reached an accuracy of 62.2%, the transfer learning models greatly outperformed it, with MobileNetV2 achieving the highest accuracy at 93.9%, followed by VGG19 at 90.0% and VGG16 at 88.9%.These results imply that MobileNetV2 is the most successful solution for this task since it consistently obtained a prediction accuracy of 90% for both in-dataset and out-ofdataset images.