COVID-19 detection and heatmap generation in chest x-ray images

Abstract

. Purpose: The outbreak of COVID-19 or coronavirus was first reported in 2019. It has widely and rapidly spread around the world. The detection of COVID-19 cases is one of the important factors to stop the epidemic, because the infected individuals must be quarantined. One reliable way to detect COVID-19 cases is using chest x-ray images, where signals of the infection are located in lung areas. We propose a solution to automatically classify COVID-19 cases in chest x-ray images. Approach: The ResNet-101 architecture is adopted as the main network with more than 44 millions parameters. The whole net is trained using the large size of x-ray images. The heatmap under the region of interest of segmented lung is constructed to visualize and emphasize signals of COVID-19 in each input x-ray image. Lungs are segmented using the pretrained U-Net. The confidence score of being COVID-19 is also calculated for each classification result. Results: The proposed solution is evaluated based on COVID-19 and normal cases. It is also tested on unseen classes to validate a regularization of the constructed model. They include other normal cases where chest x-ray images are normal without any disease but with some small remarks, and other abnormal cases where chest x-ray images are abnormal with some other diseases containing remarks similar to COVID-19. The proposed method can achieve the sensitivity, specificity, and accuracy of 97%, 98%, and 98%, respectively. Conclusions: It can be concluded that the proposed solution can detect COVID-19 in a chest x-ray image. The heatmap and confidence score of the detection are also demonstrated, such that users or human experts can use them for a final diagnosis in practical usages.

Examining the effect of explanation on satisfaction and trust in AI diagnostic systems

“…For imagery, AI healthcare systems may use graphs to show the relative probability of different outcomes or the relative importance of different symptoms for those outcomes, which is more akin to how the LIME algorithm [58] works for diagnostic features. Physicians may present visualization differently from how AI systems offer visual explanations, but even the use of x-rays and other test reports are generally accompanied by explanations highlighting the location of critical signs indicating a diagnosis-with a similar goal as gradient-based heatmaps [59][60][61][62] in XAI systems. Of course, the particular visualizations provided by algorithms such as LIME [63] may themselves be hard to understand.…”

Four Types of Multiclass Frameworks for Pneumonia Classification and Its Validation in X-ray Scans Using Seven Types of Deep Learning Artificial Intelligence Models

“…In the extension of the work, we will make an effort for the advancement of the system, as it could be able to detect COVID-19 as well as the severity of the disease. In addition, we will include the heatmap images [127][128][129] of the disease, which will show the affected areas of the lungs. Broader advanced one-pass machine learning such as extreme learning machines [130] can be explored as more data are collected along with pruning methods [131][132][133] to lower the storage and improve the speed.…”

A Histogram-Based Low-Complexity Approach for the Effective Detection of COVID-19 Disease from CT and X-ray Images

“…An overview of these works points out that DL approaches, mainly based on the supervised paradigm, can be divided into two main families: those based on segmentation and those that perform the classification task directly. The approaches, which are based on segmentation, are usually founded on U-Net type architecture to identify relevant part of the CXRs/CT scans and perform classification, focusing the attention only on these sections [56,[79][80][81][82][83][84]. The second family of approaches, instead, is based on the binary classification problem of COVID/Non-COVID images [20,69,70,[85][86][87][88] and utilize deep Convolutional Neural Networks (CNNs) and their variants, including VGG16, InceptionV3, ResNet, and DenseNet.…”

“…A synoptic overview of the related work is provided in Table 1, that summarizes the main approaches pursued by the referred papers. CT [74] CT [75] CXR + CT [65] CXR + CT [76] CXR + CT [77] CXR + CT [78] CXR + CT Segmentation [79] CXR [80] CT [81] CT [82] CT [56] CT [83] CXR [84] CXR CT [85] CT [86] CT [87] CXR [20] CXR [88] CXR Unsupervised [89] Histopathological [90] CT [91] CT [92] CXR…”