Big dermatological data service for precise and immediate diagnosis by utilizing pre-trained learning models

Abstract

Artificial intelligence (AI) approaches have been shown to be effective in classifying skin diseases and outperforming dermatologists in diagnosis. Using big data as a dermatological diagnosis service can present several challenges. One challenge is the need to accurately label and classify large amounts of data, such as images of infected skin. This can be time-consuming and resource-intensive. It is important to implement proper safeguards to protect sensitive medical information. Despite these challenges, the use of big data in dermatology can lead to several outcomes. It can improve the accuracy of diagnoses, leading to better patient outcomes. It can also help to identify patterns and trends in skin conditions, allowing for earlier detection and prevention. In addition, big data can be used to identify risk factors for certain conditions, enabling targeted preventative measures. Convolutional neural networks (CNNs) have been widely used for skin lesion classification, and recent advances in machine learning algorithms have led to a decrease in misclassification rates compared to manual categorization by dermatologists. This article utilizes the use of big data for accurate dermatological diagnosis services, it introduces the use of various CNNs for classifying different types of skin cancer. While deep learning and pretrained transfer learning techniques have advantages over traditional methods, they also have limitations and the potential for incorrect identification under certain circumstances. This work discusses these vulnerabilities and employs pretrained models to classify 11 different skin diseases, including monkey pox. The performance of the system is evaluated using accuracy, loss, precision, recall, and F1 score, and the results show that the system is able to diagnose the 11 skin illnesses with an accuracy rate of 97% using transfer learning in Keras and computer vision models. The best model was found to be Inception_ResNetV2 with 50 epochs and the Adam optimizer.