Chest X-ray classification using Deep learning for automated COVID-19 screening

Abstract

In today's world, we find ourselves struggling to fight one of the worst pandemics in the history of humanity known as COVID-2019 caused by a coronavirus. If we detect the virus at an early stage (before it enters the lower respiratory tract), the patient can be treated quickly. Once the virus reaches the lungs, we observe ground-glass opacity in the chest X-ray due to fibrosis in the lungs. Due to the significant differences between X-ray images of an infected and non-infected person, artificial intelligence techniques can be used to identify the presence and severity of the infection. We propose a classification model that can analyze the chest X-rays and help in the accurate diagnosis of COVID-19. Our methodology classifies the chest X-rays into 4 classes viz. normal, pneumonia, tuberculosis (TB), and COVID-19. Further, the X-rays indicating COVID-19 are classified on severity-basis into mild, medium, and severe. The deep learning model used for the classification of pneumonia, TB, and normal is VGG16 with an accuracy of 95.9 %. For the segregation of normal pneumonia and COVID-19, the DenseNet-161 was used with an accuracy of 98.9 %. ResNet-18 worked best for severity classification achieving accuracy up to 76 %. Our approach allows mass screening of the people using X-rays as a primary validation for COVID-19.

A Complete Survey on Automatically Diagnosing COVID-19 In the field of Computer Vision and A Collection of Medical Images

“…Using 8 different Deep Learning Model [4] researchers found NasNet and MobileNet outperformed all the models and they used both CT image and X-ray images to evaluate their system.DenseNets concatenates output features rather than sum the output feature maps of the layer with the incoming feature maps.DenseNet outperformed than other models [18] having 85% sensitivity.In the following section, some other feature extraction methods adopted by the researchers are discussed elaborately. [4], [7], [10], [13], [14], [18], [20], [22], [27], [28], [31], [32], [38], [40], [41], [44], [ 47], [51], [61], [62], [66], [67], [71], [73], [74] 26…”

“…DenseNet [1], [4], [7], [14], [18][22], [23], [28], [30], [31], [41], [47], [48], [52], [56], [66], [67], [7 2], [74] 19…”

“…Moreover, some researchers also use an adaptive winner filter for noise reduction [59], Affine Transformation [31] in their research. [2], [5], [6], [8], [10][11], [16], [18], [19], [20], [22], [23] [24],[ [26], [28], [ 30], [32], [37],43], [45], [46], [48], [50], [53], [54], [56], [60], [61], [63], [67] 31…”

“…Contrast adjusting [6], [21], [24], [28], [30], [35], [38], [41], [43], [ 48], [52], [53], [59], The ratio of preprocessing techniques are exhibited In Fig. 6.…”

“…However, Binary classification may create ambiguity while detecting COVID-19 as it can not distinguish between other Viral Pneumonia and COVID-19. [9], [11,][13], [14], [15], [18] [19], [21], [22], [23], [25], [27], [28], [29], [30], [32], [34] , [35], [37], [42], [44], [46], [ 48], [ 49], [ 51], [52], [ 53], [54], [ 55], [57], [58], [60], [62], [64], [66], [67], [ 68], [69], [72] 40…”