System for Quantitative Diagnosis of COVID-19-Associated Pneumonia Based on Superpixels With Deep Learning and Chest CT

Abstract

COVID-19 is a disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that can lead to complications such as acute respiratory distress syndrome, acute heart injury and secondary infections in a relatively high proportion of patients and, consequently, significant mortality. The definitive diagnosis of COVID-19 is performed by real-time Polymerase Chain Reaction (RT-PCR). However, as the result of RT-PCR, at least for now, has been made available within a longer period of time than the computed tomography (CT) report, this has taken on an important role in the detection of patients infected with COVID-19. A rough estimate of the extent of lung involvement by the disease is also important and considered an additional criterion for deciding on discharge or hospitalization. Recent research has adopted deep neural networks and other machine learning approaches to detect the presence of lung infection caused by COVID-19. However, the extent of lung involvement (volume) caused by the disease has been little investigated. In this work, we created an end-to-end computer vision system to automatically quantify the Percentage Of Infection (POI) in chest CT images of COVID-19 cases confirmed by the laboratory. Trying to obtain high accuracy, we evaluated the performance of three deep neural networks well known in the literature, trained with three different training strategies: (1) no transfer learning with randomly initialized weights; (2) transfer learning without fine-tuning with ImageNet weights; and (3) transfer learning with 100% fine-tuning. Data augmentation and dropout were used during networks training to reduce overfitting and increase the generalization capacity of the models. Our approach consists of segmenting a chest CT with the SLIC Superpixels method and classifying each segment (superpixel) into a specific class (COVID or non-COVID). We used the weights of the deep neural network best evaluated for accuracy in our computer vision system in order to classify the superpixels in the image and quantify the regions of COVID-19 infection, thus calculating the POI on chest CT. The results indicate that deep learning models can be successfully used to support radiologists in the quantitative diagnosis of lung infection caused by COVID-19, reaching an accuracy of up to 98.4% with Inception-Resnet-v2 architecture.

Explainable artificial intelligence: an analytical review

“…The prototypes are examples of CT scans of patients with or without COVID. This approach can be expanded readily to include more classes, such as "mild" or "severe" COVID, and so on, or go to the level of superpixels as in Tetila et al (2020). Furthermore, the proposed deep neural network has a clear and explainable architecture (with each layer having a very clear meaning and using visual images of CT scans so the decision can easily be visualized).…”

A New Approach to COVID-19 Detection: An ANN Proposal Optimized through Tree-Seed Algorithm

“…According to Table 4, it is seen that the proposed mAlexNet-TSA-ANN structure is superior to previous studies using the same dataset. [42] CNN + SVM 94.03% Tetila et al [43] Inception-Resnet-v2 98.4% Panwar et al [44] Color Visualization (Grad-CAM) 95% Wang et al [45] Contrastive Learning 90.83 ± 0.93 Jaiswal et al [20] DenseNet201 96.25% Öztürk et al [46] WOA-MLP 88.06% Silva et al [47] EfficientNet 98.50% Yazdani et al [48] Attentional Convolutional Network 92% Proposed approach mAlexNet-TSA-ANN 98.54%…”

An Analysis of Image Features Extracted by CNNs to Design Classification Models for COVID-19 and Non-COVID-19

“…The classification of COVID-19 and pneumonia seeks to separate patients infected with SARS-CoV-2 from those with pneumonia caused by another agent [7,[42][43][44][45][46][47][48][49][50]. The classification of COVID-19 and non-COVID-19 seeks to distinguish patients diagnosed with infection caused by SARS-Cov-2, from those that present characteristics of classic pneumonia, healthy or with a diagnostic of other lung diseases [51][52][53][54][55][56][57][58][59][60]. In addition, some works presented different methodologies [7] that are able to detect the lesion of the image indicating its severity.…”