Skin disease classification using fine-tuned transfer learning techniques

Abstract

Millions of individuals worldwide suffer from skin illnesses, which pose considerable health and economic difficulties. Accurate and prompt diagnosis is crucial for successful treatment and management. Traditional treatments relying on dermatologists' visual assessments can be time-consuming and prone to mistakes. This study studies the use of transfer learning with fine-tuned convolutional neural networks (CNNs) to increase the classification accuracy of skin disorders. Five pretrained models—VGG16, InceptionV3, MobileNetV3, EfficientNetB2, and EfficientNetB5—were chosen for their demonstrated performance in image recognition tests. The study's objective is to build a credible skin disease categorization system utilizing these models. Experimental results indicated that the EfficientNetB2 model attained the best accuracy at 89.37%, followed closely by EfficientNetB5 with roughly 87%. The findings emphasize the potential of transfer learning to transform dermatological diagnostics by delivering a reliable and efficient method to early diagnosis and treatment, therefore improving patient outcomes and maximizing healthcare resources. Future research should focus on increasing the dataset and researching more AI approaches to better the diagnostic capabilities of these models. This technique will further develop their efficacy and dependability in clinical applications, opening the path for improved patient care and resource management.