A mobile app that uses deep learning to analyze images of skin lesions can detect monkeypox with 91 percent accuracy, according to new artificial intelligence (AI) research. | Techy Kings

[ad_1]

Recently, monkeypox has spread throughout the world. in 2022 August. nearly 47,000 laboratory-confirmed cases of monkeypox have been reported worldwide.

In its early stages, monkeypox was difficult to detect because monkeypox is quite similar to chickenpox and measles. PCR tests can be used to confirm detection, but PCR tests are not readily available. Thus, computational methods for the detection of monkeypox can be useful for easy and rapid detection of monkeypox in its early stages.

Although the human monkeypox disease appeared in ancient times (in the 1970s), computer vision-based research for the preliminary diagnosis of the disease has only recently begun. There is currently very little research on this. Therefore, computer intervention was needed in this matter.

Smartphones are becoming increasingly important in monitoring and delivering healthcare services. There are many benefits to using mobile healthcare apps. First, they can help people get information about their illnesses quickly. Second, they can be followed by their therapists. Third, they can help people manage their illnesses. 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 help classify human monkeypox based on skin lesion images. The application collects videos through the camera of the mobile device. These images are then sent to a deep convolutional neural network that runs on the same machine. The network then classifies the images as positive or negative to identify monkeypox. Images of skin lesions from individuals with monkeypox and other skin lesions were used to train the network.

For this purpose, a publicly available dataset and a deep transfer learning strategy were applied. The network classifies the images as positive or negative to identify monkeypox. Images of skin lesions in humans with monkeypox and other skin lesions were used to train the network. For this purpose, a publicly available dataset and a deep transfer learning strategy were applied. The entire training and testing process was performed in Matlab with various pre-trained networks. The best performing network was then iterated and trained using TensorFlow. With the move to TensorFlow Lite, the TensorFlow model has been adapted for mobile devices. The TensorFlow Lite model and monkeypox detection library have been integrated into the mobile application.

The program was run on three different devices, and the inference time was collected at runtime. The average output times were 197 ms, 91 ms, and 138 ms. Test results show that the system can accurately classify photos with 91.11 percent accuracy. The proposed smartphone app can also be trained to pre-diagnose other skin conditions.

People with physical damage can easily make a preliminary diagnosis using the provided system. Thus, patients with monkeypox can be urged to seek immediate medical attention for a definitive diagnosis.

The method proposed in this study could be a good solution for monkeypox detection as it is claimed to be faster, more reliable than clinical detection and more accessible than PCR tests. The method could be extended to detect more skin-related diseases, making the process of identifying 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.
Please Don't Forget To Join Our ML Subreddit


Rishabh Jain is a consulting intern at MarktechPost. He is currently pursuing B.tech in Computer Science at IIIT, Hyderabad. He is a machine learning enthusiast and has a strong interest in statistical AI techniques and data analytics. He is passionate about building better AI algorithms.


[ad_2]

Source link