Human Monkeypox Classification from Skin Lesion Images with Deep Pre-trained Network using Mobile Application

Abstract

Recently, human monkeypox outbreaks have been reported in many countries. According to the reports and studies, quick determination and isolation of infected people are essential to reduce the spread rate. This study presents an Android mobile application that uses deep learning to assist this situation. The application has been developed with Android Studio using Java programming language and Android SDK 12. Video images gathered through the mobile device’s camera are dispatched to a deep convolutional neural network that runs on the same device. Camera2 API of the Android platform has been used for camera access and operations. The network then classifies images as positive or negative for monkeypox detection. The network’s training has been carried out using skin lesion images of monkeypox-infected people and other skin lesion images. For this purpose, a publicly available dataset and a deep transfer learning approach have been used. All training and testing steps have been applied on Matlab using different pre-trained networks. Then, the network that has the best accuracy has been recreated and trained using TensorFlow. The TensorFlow model has been adapted to mobile devices by converting to the TensorFlow Lite model. The TensorFlow Lite model has been then embedded into the mobile application together with the TensorFlow Lite library for monkeypox detection. The application has been run on three devices successfully. During the run-time, the inference times have been gathered. 197 ms, 91 ms, and 138 ms average inference times have been observed. The presented system allows people with body lesions to quickly make a preliminary diagnosis. Thus, monkeypox-infected people can be encouraged to act rapidly to see an expert for a definitive diagnosis. According to the test results, the system can classify the images with 91.11% accuracy. In addition, the proposed mobile application can be trained for the preliminary diagnosis of other skin diseases.

Deep Learning Models for the Detection of Monkeypox Skin Lesion on Digital Skin Images

“…Findings established that the classification algorithm managed an F1-score of 0.99 and an average accuracy score of 0.96. Similarly, the investigation by [13] used MATLAB and TensorFlow to classify skin lesion images in the detection of Monkeypox. Moreover, the mention study was particularly unique in that it created a new mobile application that would be used to scan new digital images and report the classification results.…”

“…Artificial intelligence can rid the diagnosis process of these bottlenecks because of its high accuracy and proven reliability in the past. Researchers are continuously creating databases of Monkeypox and other pox images to aid in the classification and isolation of the virus to curb its spread [13]. Hence, this paper tests the accuracy of machine learning models in classifying digital skin images to detect Monkeypox.…”

Utilizing the Ensemble of Deep Learning Approaches to Identify Monkeypox Disease

“…Among the models, MobileNetv2 showed the best performance in terms of accuracy and it was integrated into the mobile applications. Accuracy, jaccard, precision, sensitivity, and F1-score was used to evaluate the performance of the systems [8].…”

“…The number of cases reported worldwide is increasing every year, with over 6200 and 9400 confirmed and suspected cases reported by the World Health Organization (WHO) in 2020 and 2021, respectively. According to the WHO, from 1 January to 22 June 2022, 3413 laboratory-confirmed cases and one death have been reported [2], [4], [5], [6], [7], [8].…”

Deep transfer learning approaches for Monkeypox disease diagnosis

“…Therefore, a TL-based model also needs to be evaluated with various optimizers on different datasets in order to understand the model’s stability as well. Most of the previous research also did not provide any clear explanation as to whether they had used generalization and regularization approaches ( Eid et al, 2022 , Haque, Islam et al, 2022 , Islam and Shin, 2022 , Sahin et al, 2022 ). Therefore, it is also not clear if their proposed model is suffering from overfitting issues or not, even though the reported accuracy is much higher ( Akin et al, 2022 , Haque, Ahmed et al, 2022 ).…”