Deep learning-based common skin disease image classification

Abstract

Skin disease is currently considered to be one of the most common diseases in the globe. Most of the human population has experienced it at some point but not all skin illnesses are as severe as others. There are some diseases that are symptomless or show fewer symptoms. Skin cancer is a potentially fatal outcome of serious skin illnesses that might develop if they are not detected in time. Due to the fact that medical professionals aren’t always quick or reliable enough to make a proper diagnosis. There is a hefty price tag attached to employing sophisticated equipment. Therefore, we propose a system capable of classifying skin diseases using deep learning approaches, such as CNN architecture and six preset models including MobileNet, VGG19, ResNet, EfficientNet, Inception, and DenseNet. Acne, blisters, cold sores, psoriasis, and vitiligo are some of the most often seen skin conditions, thus we scoured the web resources for relevant photographs of these conditions. We have applied data augmentation methods to extend the size of the dataset and include more image variations. In the validation dataset, we achieved an accuracy rate of approx 99 percent, while in the test dataset; we achieved an accuracy rate of approx 90 percent. Our proposed method would help to diagnose skin diseases in a faster and more cost-effective way.

Machine learning and deep learning-based approach in smart healthcare: Recent advances, applications, challenges and opportunities

“…In addition, according to this figure, the X-axis describes the name of the methods, and the Y-axis explains the frequently used methods. The observation is collected from the Table 5 data along with some others studies from [51] , [117] [122] , [143] – [154] . The CNN model is used for the maximum time.…”

SkinLiTE: Lightweight Supervised Contrastive Learning Model for Enhanced Skin Lesion Detection and Disease Typification in Dermoscopic Images

“…Studies by the same group evaluated four pre-trained transfer learning models on skin dataset images, focusing on preprocessing steps and various simulation parameters and comparing it with different ResNet models [39,40]. Nath, et al [41] developed a system using CNN architecture and six preset models to classify common skin conditions, applying data augmentation on the dataset. Arora, et al [42] evaluated the performance of fourteen deep learning networks using transfer learning on the ISIC 2018 dataset for skin lesion classification.…”