Feature Extraction Using Deep Models For Disease Classification In Dermatoscopic Images

Abstract

Dermatoscopic image identification using convolutional neural networks (CNNs) has showed potential in the detection and diagnosis of skin conditions. The analysis of dermatoscopic images using ResNet50, VGG19, and MobileNetV2 deep learning models for feature extraction and disease classification. We do extensive training on several image datasets and transfer learning using a particular dermatoscopic dataset. The models use their complex structures to identify patterns and distinguish between various types of illnesses. Accuracy is used to evaluate performance, and comparison assessments are completed. Results show that ResNet50, VGG19, and MobileNetV2 can accurately categorize data. These models successfully discriminate between healthy and sick skin based on numerous criteria. Improved generalization to new data results from transfer learning. Accurate disease classification facilitates quick and accurate diagnosis, which is advantageous for dermatological practice. Deep learning models have the ability to automate early intervention and diagnosis. In conclusion, dermatoscopic pictures of skin diseases may be precisely identified and categorized by ResNet50, VGG19, and MobileNetV2 CNNs. These models improve skin disease diagnosis and therapy, leading to better dermatological patient outcomes.