Learning from Small Datasets: An Efficient Deep Learning Model for Covid-19 Detection from Chest X-ray Using Dataset Distillation Technique

Abstract

Ever since the spread of the Coronavirus pandemic popularly known as Covid-19, researchers have dwelled in finding ways to curtail the spread of this disease. The disease has no known treatment but the best way of reducing its spread is by conducting tests, to identify people with positive cases, and isolate them from the general public. Despite the efforts being made by medical practitioners and media houses to provide public awareness, the general public is still shunning away from the covid-19 tests, because of the sentiments rumored about the disease and the complications of the testing process. In some countries, even the cost of the tests is beyond the reach of common citizens or simply not affordable. Researchers proposed cost-effective deep learning models of detecting covid-19 from the chest x-ray images, to serve as a diagnostics aid or an improvised tool in places where the testing materials are not affordable or available. However, the models are very cumbersome, making them expensive to train, the model also suffers from a long inference time. As the matter of diagnosis is critical, it is necessary to provide a new faster models with shorter inference time. Therefore, this paper proposed a novel covid-19 diagnosis using dataset distillation. The model used only 70 instances out of 3,616 available instances in the X-ray dataset, making the model resource inexpensive, and faster to train. The performance of the proposed model achieved 95% accuracy when tested, the model also outperformed the convolutional neural network (CNN) model trained with a full dataset in terms of accuracy.