Deep transfer learning - based automated detection of COVID-19 from lung CT scan slices

Abstract

In the proposed research work; the COVID-19 is detected using transfer learning from CT scan images decomposed to three-level using stationary wavelet. A three-phase detection model is proposed to improve the detection accuracy and the procedures are as follows; Phase1- data augmentation using stationary wavelets, Phase2- COVID-19 detection using pre-trained CNN model and Phase3- abnormality localization in CT scan images. This work has considered the well known pre-trained architectures, such as ResNet18, ResNet50, ResNet101, and SqueezeNet for the experimental evaluation. In this work, 70% of images are considered to train the network and 30% images are considered to validate the network. The performance of the considered architectures is evaluated by computing the common performance measures.<br><br>

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

“…Table 1 presents the available data sources that researchers used in their research to find out the promising model for detecting COVID-19 that requires less time as well as less effort. In addition Table 1 represents papers that make use of datasets along with a number of the papers.Besides, some papers utilized CT images datasource [4], [21], [42], [49], [58] that are not publicly available.Further in this section frequency of papers that used a specific dataset of CT images are illustrated in Fig. 2.…”

“…Flipping or Rotating [1], [2][15], [16], [21], [22], [24], [25], [27], [35], [36], [37], [38], [42], [43], [4 6], [47], [48], [50], [51], [53], [55], [57], [61], [62], [65], [71], [74] 29…”

“…Table 4, among all ResNet is used largely then VGG, DenseNET, and others. Researchers use ResNet as a pre-trained model [42] for binary classification of CT scan images that is 71 layers deep and requires an input image size of 224x224x3. As an extension of CNN researchers uses DenseNet [29] to boost computational efficiency reducing image dimension and obtain 90.61% accuracy.As a feature extraction method researchers employ a modified version of inception V3 (IV3*) then train the extracted features using layers of the capsule network [9] that achieved the highest sensitivity and lowest specificity compared to other pre-trained models.…”

“…Researchers use eight different Deep Learning Models in the paper [4] and found NasNet and MobileNet performed better than the other six models. [9], [12], [21], [29][39], [42], [45], [46], [55], [58] 11 VGG [4], [8], [9], [11], [46], [55], [58] 7…”

“…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…”