ETLoViT: an acne diagnose approach using vision-transformers and model ensembling

Abstract Acne, a widespread skin condition predominantly affecting teenagers, presents intricate challenges in its diagnosis. Recent advances in deep learning, machine learning, and image processing methods have made it possible to diagnose acne automatically and effectively. However, achieving higher acne classification accuracy is still one of the concerns with these methods. Therefore, this paper introduces a group of trained models based on transfer learning that is applied to Vision Transformer extracted features (ETLoViT). These models are trained using two innovative deep-learning methods: the Vision Transformer (ViT) and model ensembling for acne image classification. The ViT approach harnesses the power of the attention module to extract acne features efficiently. These extracted features are subsequently run through different transfer learning models, such as MobileNetV2, VGG16, and InceptionResNetV2. The predicted results were subsequently combined for classification. The proposed model (ETLoViT) is compared with standing deep learning models, and the outcomes demonstrate that the proposed (ETLoViT) model consistently performs better, achieving an astounding 96% classification accuracy.