Recently, Monkeypox has been spreading all across the world. Nearly 47,000 monkeypox cases with laboratory confirmation had been reported globally as of August 2022.
Clinical detection of monkeypox in its early stages has been difficult as monkeypox is quite similar to chickenpox and measles. PCR tests can be used to confirm detection, but PCR tests are not easily available. Hence computation methods to detect monkeypox can be helpful for easy and fast identification of monkeypox in its early stages.
Although the human monkeypox disease originates back to ancient times (the 1970s), computer vision-based studies for preliminary disease diagnosis have recently begun. There are very few studies on it currently. Hence there was a need for computer intervention in the matter.
Smartphones are becoming increasingly significant in the monitoring and delivery of healthcare. There are many advantages to using mobile healthcare applications. First, they can help people quickly get information about their diseases. Second, they can be followed by their therapists. Third, they can help people manage their diseases. Fourth, they can help people track their progress. Fifth, they can help people stay motivated.
A recently published study presents an Android mobile application that uses Deep Pre‑trained Networks to assist Human Monkeypox Classification from Skin Lesion Images. The app gathers Video images through the mobile device’s camera. These images are then dispatched to a deep convolutional neural network that runs on the same device. The network subsequently categorizes images as positive or negative for the identification of monkeypox. Images of skin lesions on persons with monkeypox and other skin lesions have been used to train the network.
A publicly accessible dataset and a deep transfer learning strategy have been applied for this goal. The network categorizes images as positive or negative for the identification of monkeypox. Images of skin lesions on persons with monkeypox and other skin lesions were used to train the network. A publicly accessible dataset and a deep transfer learning strategy have been applied for this goal. The entire training and testing process was carried out in Matlab with various pre-trained networks. The best-performing network was then replicated and trained using TensorFlow. By switching to the TensorFlow Lite model, the TensorFlow model has been made suitable for mobile devices. The TensorFlow Lite model and library for monkeypox detection were integrated into the mobile application.
The application was run on three different devices, and the inference times were collected during the runtime. The averages of inference times came out to be 197ms, 91ms, and 138ms. The test results show that the system can accurately classify photos with 91.11% of them. The suggested smartphone app can also be trained to make a preliminary diagnosis of other skin conditions.
People with body lesions can easily determine a preliminary diagnosis using the system that has been given. Thus, monkeypox patients can be urged to seek medical attention immediately for a conclusive diagnosis.
The proposed method in this research could be a good solution for the detection of monkeypox as it is claimed to be faster, more reliable than clinical detection, and more easily available than PCR tests. The method could be expanded to detect more diseases related to the skin, which would make the process of detection of all skin-related diseases easier, faster, and more reliable.
This Article is written as a research summary article by Marktechpost Staff based on the research paper 'Human Monkeypox Classification from Skin Lesion Images with Deep Pre-trained Network using Mobile Application'. All Credit For This Research Goes To Researchers on This Project. Check out the paper.
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Rishabh Jain, is a consulting intern at MarktechPost. He is currently pursuing B.tech in computer sciences from IIIT, Hyderabad. He is a Machine Learning enthusiast and has keen interest in Statistical Methods in artificial intelligence and Data analytics. He is passionate about developing better algorithms for AI.