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.

Integration of Multimodal Data Sources for Enhanced Skin Disease Classification and Cancer Prediction: A Study Leveraging Pre-Trained Models on HAM_10000 Metadata and Squamous Cell Carcinoma (SCC) Images.

“…They utilized different Convolutional Neural Network (CNN) models on the HAM10000 dataset, which is designed to distinguish various skin lesions, including melanoma [10]. The researchers examined different CNN models, maintaining a weight matrix where the elements were derived from neural network lesion classes [2]. As a result of their analysis, the accuracy of their system improved by approximately three percent.…”

“…The strategic utilization of artificial intelligence (AI) methods has led to substantial improvements in various sectors, including commerce, medical services, farming, online networking, safety, advanced transportation, logistics, environmentally-friendly urban planning, and numerous other intelligent applications [1] [2].Convolutional Neural Networks (CNNs) have found extensive applications in the classification of skin lesions. Recent progress in machine learning algorithms has significantly reduced misclassification rates when compared to manual categorization by dermatologists [3].…”