Detection of skin diseases using deep learning

Abstract

As a topic of global health importance, skin diseases must be quickly identified and accurately diagnosed to allow for effective treatment. Specifically for the classification of skin diseases, the use of deep learning models in the analysis of medical images has shown remarkable promise. To improve accuracy and predictability when classifying skin diseases, this paper suggests using an ensemble model composed of the ResNet-50, EfficientNet, Inception V3, MobileNet, NASNetMobile, DenseNet201 and Xception architectures. The first step of the investigation is to examine the existing research on deep learning models used for skin disease diagnosis and categorisation. ResNet-50, EfficientNet, MobileNet, Inception V3, DenseNet201, NASNetMobile and Xception have demonstrated their efficacy in several medical imaging applications, such as the identification and categorization of skin diseases. The utilization of diagnostic and classification methods in the context of skin illnesses serves as illustrative instances of such applications. It is important to note, however, that every construction possesses inherent imperfections. The present study is further enhanced by the use of a novel notion referred to as a ”ensemble,” which amalgamates the most advantageous attributes of many models. To ensure proper functioning, the ensemble model must initially extract and subsequently aggregate information. The comprehensive set of fundamental models underwent training utilizing a vast dataset of dermatological information. The objective of this training session was to acquire the knowledge and skills necessary to identify and discern the distinguishing features of skin lesions via the analysis of photographic representations. The ensemble model incorporates feature-level fusion to aggregate information obtained from many base models. When many data types are merged in this manner, it results in the creation of a cohesive representation. In order to improve the process of classification and generalization, the model utilizes the varied members of the ensemble. The efficacy of the ensemble model is assessed by a wide array of experiments. These research utilize a meticulously collected and standardized dataset encompassing many skinrelated disorders. The ensemble model demonstrates superiority over the individual models in terms of accuracy, precision, recall, and F1-score. The fusion methodology, which leverages several sources, holds the potential to extract supplementary data from diverse systems. The utilization of gradient rendering techniques enables the comprehensive evaluation of a model’s readability. This study examines the decision-making process of an ensemble in determining the salient features of a picture for the purpose of labeling. This thesis presents an ensemble architecture for identifying skin issues by utilizing ResNet-50, EfficientNet, Inception V3, MobileNet, NASNetMobile, DenseNet201, and Xception models. When compared to the gold standard dataset, the proposed model demonstrates superior performance, indicating its potential to assist dermatologists in making more accurate diagnoses in real-world clinical scenarios.