- New study is 98.4% accurate at detecting Covid-19 from X-rays.
- Researchers trained a convolutional neural network on Kaggle dataset.
- The hope is that the technology can be used to quickly and effectively identify Covid-19 patients.
As the Covid-19 pandemic continues to evolve, there is a pressing need for a faster diagnostic system. Testing kit shortages, virus mutations, and soaring numbers of cases have overwhelmed health care systems worldwide. Even when a good testing policy is in place, lab testing is arduous, expensive, and time consuming. Cheap antigen tests, which can give results in 30 seconds, are widely available but suffer from low sensitivity; The tests correctly identifying just 75% of Covid-19 cases a week after symptoms start .
Shashwat Sanket and colleagues set out to find an easy, fast, and accurate alternative using simple chest X-ray images. The team found that bilateral changes seen in chest X-rays of patients with Covid-19 can be analyzed and classified without a radiologist’s interpretation, using Convolutional Neural Networks (CNNs). The study, published in the September issue of Multimedia tools and Applications, successfully trained a CNN to accurately diagnose Covid-19 from Chest X-Rays, achieving an impressive 98.4% classification accuracy.. The journal article, titled Detection of novel coronavirus from chest X-rays using deep convolutional neural networks, shows some exciting promise in the ongoing efforts to find ways to detect Covid-19 quickly and effectively,
What are Convolutional Neural Networks?
A convolutional neural network (CNN) is a Deep Learning algorithm that resembles the response of neurons in the visual cortex. The algorithm takes an input image and weighs the relative importance of various aspects in the image. The neurons overlap to span the entire field of vision, comprising a completely connected network where neurons in one layer link to neurons in other layers. The multilayered CNN includes an input layer, an output layer, and several hidden layers. A simple process called pooling keeps the most important features while reducing the dimensionality of the feature map.
One major advantage of CNNs is that, compared to other classification algorithms, the required pre-processing is much lower. In addition, CNNs use regularized weights over fewer parameters. This avoids the exploding gradient and vanishing gradient problems of traditional neural networks during backpropagation.
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