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COVID-19 Detection Systems Using Deep-Learning Algorithms Based on Speech and Image Data. MATHEMATICS 2022. [DOI: 10.3390/math10040564] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
The global epidemic caused by COVID-19 has had a severe impact on the health of human beings. The virus has wreaked havoc throughout the world since its declaration as a worldwide pandemic and has affected an expanding number of nations in numerous countries around the world. Recently, a substantial amount of work has been done by doctors, scientists, and many others working on the frontlines to battle the effects of the spreading virus. The integration of artificial intelligence, specifically deep- and machine-learning applications, in the health sector has contributed substantially to the fight against COVID-19 by providing a modern innovative approach for detecting, diagnosing, treating, and preventing the virus. In this proposed work, we focus mainly on the role of the speech signal and/or image processing in detecting the presence of COVID-19. Three types of experiments have been conducted, utilizing speech-based, image-based, and speech and image-based models. Long short-term memory (LSTM) has been utilized for the speech classification of the patient’s cough, voice, and breathing, obtaining an accuracy that exceeds 98%. Moreover, CNN models VGG16, VGG19, Densnet201, ResNet50, Inceptionv3, InceptionResNetV2, and Xception have been benchmarked for the classification of chest X-ray images. The VGG16 model outperforms all other CNN models, achieving an accuracy of 85.25% without fine-tuning and 89.64% after performing fine-tuning techniques. Furthermore, the speech–image-based model has been evaluated using the same seven models, attaining an accuracy of 82.22% by the InceptionResNetV2 model. Accordingly, it is inessential for the combined speech–image-based model to be employed for diagnosis purposes since the speech-based and image-based models have each shown higher terms of accuracy than the combined model.
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52
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An Efficient Deep Learning Model to Detect COVID-19 Using Chest X-ray Images. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19042013. [PMID: 35206201 PMCID: PMC8871610 DOI: 10.3390/ijerph19042013] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 01/25/2022] [Accepted: 02/09/2022] [Indexed: 12/18/2022]
Abstract
The tragic pandemic of COVID-19, due to the Severe Acute Respiratory Syndrome coronavirus-2 or SARS-CoV-2, has shaken the entire world, and has significantly disrupted healthcare systems in many countries. Because of the existing challenges and controversies to testing for COVID-19, improved and cost-effective methods are needed to detect the disease. For this purpose, machine learning (ML) has emerged as a strong forecasting method for detecting COVID-19 from chest X-ray images. In this paper, we used a Deep Learning Method (DLM) to detect COVID-19 using chest X-ray (CXR) images. Radiographic images are readily available and can be used effectively for COVID-19 detection compared to other expensive and time-consuming pathological tests. We used a dataset of 10,040 samples, of which 2143 had COVID-19, 3674 had pneumonia (but not COVID-19), and 4223 were normal (not COVID-19 or pneumonia). Our model had a detection accuracy of 96.43% and a sensitivity of 93.68%. The area under the ROC curve was 99% for COVID-19, 97% for pneumonia (but not COVID-19 positive), and 98% for normal cases. In conclusion, ML approaches may be used for rapid analysis of CXR images and thus enable radiologists to filter potential candidates in a time-effective manner to detect COVID-19.
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53
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Hassan H, Ren Z, Zhao H, Huang S, Li D, Xiang S, Kang Y, Chen S, Huang B. Review and classification of AI-enabled COVID-19 CT imaging models based on computer vision tasks. Comput Biol Med 2022; 141:105123. [PMID: 34953356 PMCID: PMC8684223 DOI: 10.1016/j.compbiomed.2021.105123] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 12/03/2021] [Accepted: 12/03/2021] [Indexed: 01/12/2023]
Abstract
This article presents a systematic overview of artificial intelligence (AI) and computer vision strategies for diagnosing the coronavirus disease of 2019 (COVID-19) using computerized tomography (CT) medical images. We analyzed the previous review works and found that all of them ignored classifying and categorizing COVID-19 literature based on computer vision tasks, such as classification, segmentation, and detection. Most of the COVID-19 CT diagnosis methods comprehensively use segmentation and classification tasks. Moreover, most of the review articles are diverse and cover CT as well as X-ray images. Therefore, we focused on the COVID-19 diagnostic methods based on CT images. Well-known search engines and databases such as Google, Google Scholar, Kaggle, Baidu, IEEE Xplore, Web of Science, PubMed, ScienceDirect, and Scopus were utilized to collect relevant studies. After deep analysis, we collected 114 studies and reported highly enriched information for each selected research. According to our analysis, AI and computer vision have substantial potential for rapid COVID-19 diagnosis as they could significantly assist in automating the diagnosis process. Accurate and efficient models will have real-time clinical implications, though further research is still required. Categorization of literature based on computer vision tasks could be helpful for future research; therefore, this review article will provide a good foundation for conducting such research.
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Affiliation(s)
- Haseeb Hassan
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China; Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Health Science Center, Shenzhen, China
| | - Zhaoyu Ren
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China
| | - Huishi Zhao
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China
| | - Shoujin Huang
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China
| | - Dan Li
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China
| | - Shaohua Xiang
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China
| | - Yan Kang
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Health Science Center, Shenzhen, China; Medical Device Innovation Research Center, Shenzhen Technology University, Shenzhen, China
| | - Sifan Chen
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China; Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Bingding Huang
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China.
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54
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Wang Z, Dong J, Zhang J. Multi-Model Ensemble Deep Learning Method to Diagnose COVID-19 Using Chest Computed Tomography Images. JOURNAL OF SHANGHAI JIAOTONG UNIVERSITY (SCIENCE) 2022; 27:70-80. [PMID: 34975263 PMCID: PMC8710815 DOI: 10.1007/s12204-021-2392-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 06/07/2021] [Indexed: 11/27/2022]
Abstract
Deep learning based analyses of computed tomography (CT) images contribute to automated diagnosis of COVID-19, and ensemble learning may commonly provide a better solution. Here, we proposed an ensemble learning method that integrates several component neural networks to jointly diagnose COVID-19. Two ensemble strategies are considered: the output scores of all component models that are combined with the weights adjusted adaptively by cost function back propagation; voting strategy. A database containing 8 347 CT slices of COVID-19, common pneumonia and normal subjects was used as training and testing sets. Results show that the novel method can reach a high accuracy of 99.37% (recall: 0.9981, precision: 0.989 3), with an increase of about 7% in comparison to single-component models. And the average test accuracy is 95.62% (recall: 0.958 7, precision: 0.955 9), with a corresponding increase of 5.2%. Compared with several latest deep learning models on the identical test set, our method made an accuracy improvement up to 10.88%. The proposed method may be a promising solution for the diagnosis of COVID-19.
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Affiliation(s)
- Zhiming Wang
- College of Electrical Engineering, Sichuan University, Chengdu, 610056 China
| | - Jingjing Dong
- Key Laboratory of Aerospace Medicine of Ministry of Education, Air Force Medical University, Xi’an, 710032 China
- Lintong Rehabilitation and Recuperation Center, PLA Joint Logistic Support Force, Xi’an, 710600 China
| | - Junpeng Zhang
- College of Electrical Engineering, Sichuan University, Chengdu, 610056 China
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55
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Irene D S, Beulah JR, K. A, K. K. An efficient COVID-19 detection from CT images using ensemble support vector machine with Ludo game-based swarm optimisation. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 2022. [DOI: 10.1080/21681163.2021.2024088] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Shiny Irene D
- Department of Computing Technologies, School of Computing, College of Engineering and Technology, Faculty of Engineering and Technology, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, Chennai, TN, India
| | - J. Rene Beulah
- Department of Computing Technologies, School of Computing, College of Engineering and Technology, Faculty of Engineering and Technology, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, Chennai, TN, India
| | - Anitha K.
- Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha nagar, Thandalam, Chennai, Tamil Nadu, India
| | - Kannan K.
- Department of Electronics and Communication Engineering, R.M.K College of Engineering And Technology, R.S.M. Nagar, Puduvoyal, Gummidipoondi (TK), Thiruvallur (DT), Tamilnadu, India
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56
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Ter-Sarkisov A. COVID-CT-Mask-Net: prediction of COVID-19 from CT scans using regional features. APPL INTELL 2022; 52:9664-9675. [PMID: 35035092 PMCID: PMC8741555 DOI: 10.1007/s10489-021-02731-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/28/2021] [Indexed: 12/20/2022]
Abstract
We present COVID-CT-Mask-Net model that predicts COVID-19 in chest CT scans. The model works in two stages: in the first stage, Mask R-CNN is trained to localize and detect two types of lesions in images. In the second stage, these detections are fused to classify the whole input image. To develop the solution for the three-class problem (COVID-19, Common Pneumonia and Control), we used the COVIDx-CT data split derived from the dataset of chest CT scans collected by China National Center for Bioinformation. We use 3000 images (about 5% of the train split of COVIDx-CT) to train the model. Without any complicated data normalization, balancing and regularization, and training only a small fraction of the model's parameters, we achieve a 9 0 . 8 0 % COVID-19 sensitivity, 9 1 . 6 2 % Common Pneumonia sensitivity and 9 2 . 1 0 % true negative rate (Control sensitivity), an overall accuracy of 9 1 . 6 6 % and F1-score of 9 1 . 5 0 % on the test data split with 21192 images, bringing the ratio of test to train data to 7.06. We also establish an important result that regional predictions (bounding boxes with confidence scores) detected by Mask R-CNN can be used to classify whole images. The full source code, models and pretrained weights are available on https://github.com/AlexTS1980/COVID-CT-Mask-Net.
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Affiliation(s)
- Aram Ter-Sarkisov
- CitAI Research Center, Department of Computer Science, City, University of London, Northampton Square, London, UK
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57
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Nasiri H, Alavi SA. A Novel Framework Based on Deep Learning and ANOVA Feature Selection Method for Diagnosis of COVID-19 Cases from Chest X-Ray Images. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:4694567. [PMID: 35013680 PMCID: PMC8742147 DOI: 10.1155/2022/4694567] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 12/20/2021] [Indexed: 12/12/2022]
Abstract
Background and Objective. The new coronavirus disease (known as COVID-19) was first identified in Wuhan and quickly spread worldwide, wreaking havoc on the economy and people's everyday lives. As the number of COVID-19 cases is rapidly increasing, a reliable detection technique is needed to identify affected individuals and care for them in the early stages of COVID-19 and reduce the virus's transmission. The most accessible method for COVID-19 identification is Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR); however, it is time-consuming and has false-negative results. These limitations encouraged us to propose a novel framework based on deep learning that can aid radiologists in diagnosing COVID-19 cases from chest X-ray images. Methods. In this paper, a pretrained network, DenseNet169, was employed to extract features from X-ray images. Features were chosen by a feature selection method, i.e., analysis of variance (ANOVA), to reduce computations and time complexity while overcoming the curse of dimensionality to improve accuracy. Finally, selected features were classified by the eXtreme Gradient Boosting (XGBoost). The ChestX-ray8 dataset was employed to train and evaluate the proposed method. Results and Conclusion. The proposed method reached 98.72% accuracy for two-class classification (COVID-19, No-findings) and 92% accuracy for multiclass classification (COVID-19, No-findings, and Pneumonia). The proposed method's precision, recall, and specificity rates on two-class classification were 99.21%, 93.33%, and 100%, respectively. Also, the proposed method achieved 94.07% precision, 88.46% recall, and 100% specificity for multiclass classification. The experimental results show that the proposed framework outperforms other methods and can be helpful for radiologists in the diagnosis of COVID-19 cases.
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Affiliation(s)
- Hamid Nasiri
- Department of Computer Engineering, Amirkabir University of Technology, Tehran, Iran
| | - Seyed Ali Alavi
- Electrical and Computer Engineering Department, Semnan University, Semnan, Iran
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58
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Abou-Kreisha MT, Yaseen HK, Fathy KA, Ebeid EA, ElDahshan KA. Multisource Smart Computer-Aided System for Mining COVID-19 Infection Data. Healthcare (Basel) 2022; 10:109. [PMID: 35052273 PMCID: PMC8775247 DOI: 10.3390/healthcare10010109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 12/25/2021] [Accepted: 12/29/2021] [Indexed: 11/16/2022] Open
Abstract
In this paper, we approach the problem of detecting and diagnosing COVID-19 infections using multisource scan images including CT and X-ray scans to assist the healthcare system during the COVID-19 pandemic. Here, a computer-aided diagnosis (CAD) system is proposed that utilizes analysis of the CT or X-ray to diagnose the impact of damage in the respiratory system per infected case. The CAD was utilized and optimized by hyper-parameters for shallow learning, e.g., SVM and deep learning. For the deep learning, mini-batch stochastic gradient descent was used to overcome fitting problems during transfer learning. The optimal parameter list values were found using the naïve Bayes technique. Our contributions are (i) a comparison among the detection rates of pre-trained CNN models, (ii) a suggested hybrid deep learning with shallow machine learning, (iii) an extensive analysis of the results of COVID-19 transition and informative conclusions through developing various transfer techniques, and (iv) a comparison of the accuracy of the previous models with the systems of the present study. The effectiveness of the proposed CAD is demonstrated using three datasets, either using an intense learning model as a fully end-to-end solution or using a hybrid deep learning model. Six experiments were designed to illustrate the superior performance of our suggested CAD when compared to other similar approaches. Our system achieves 99.94, 99.6, 100, 97.41, 99.23, and 98.94 accuracy for binary and three-class labels for the CT and two CXR datasets.
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Affiliation(s)
| | - Humam K. Yaseen
- Mathematics Department, Faculty of Science, Al-Azhar University, Cairo 11651, Egypt; (M.T.A.-K.); (K.A.F.); (E.A.E.); (K.A.E.)
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59
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Wu K, Jelfs B, Ma X, Ke R, Tan X, Fang Q. Weakly-supervised lesion analysis with a CNN-based framework for COVID-19. Phys Med Biol 2021; 66:245027. [PMID: 34905733 DOI: 10.1088/1361-6560/ac4316] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 12/14/2021] [Indexed: 02/05/2023]
Abstract
Objective.Lesions of COVID-19 can be clearly visualized using chest CT images, and hence provide valuable evidence for clinicians when making a diagnosis. However, due to the variety of COVID-19 lesions and the complexity of the manual delineation procedure, automatic analysis of lesions with unknown and diverse types from a CT image remains a challenging task. In this paper we propose a weakly-supervised framework for this task requiring only a series of normal and abnormal CT images without the need for annotations of the specific locations and types of lesions.Approach.A deep learning-based diagnosis branch is employed for classification of the CT image and then a lesion identification branch is leveraged to capture multiple types of lesions.Main Results.Our framework is verified on publicly available datasets and CT data collected from 13 patients of the First Affiliated Hospital of Shantou University Medical College, China. The results show that the proposed framework can achieve state-of-the-art diagnosis prediction, and the extracted lesion features are capable of distinguishing between lesions showing ground glass opacity and consolidation.Significance.The proposed approach integrates COVID-19 positive diagnosis and lesion analysis into a unified framework without extra pixel-wise supervision. Further exploration also demonstrates that this framework has the potential to discover lesion types that have not been reported and can potentially be generalized to lesion detection of other chest-based diseases.
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Affiliation(s)
- Kaichao Wu
- Department of Biomedical Engineering, Shantou University, Shantou, People's Republic of China
- School of Engineering, RMIT University, Melbourne, Australia
| | - Beth Jelfs
- School of Engineering, RMIT University, Melbourne, Australia
| | - Xiangyuan Ma
- Department of Biomedical Engineering, Shantou University, Shantou, People's Republic of China
| | - Ruitian Ke
- The First Affiliated Hospital of Shantou University Medical College, Shantou, People's Republic of China
| | - Xuerui Tan
- The First Affiliated Hospital of Shantou University Medical College, Shantou, People's Republic of China
| | - Qiang Fang
- Department of Biomedical Engineering, Shantou University, Shantou, People's Republic of China
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60
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Mondal AK, Bhattacharjee A, Singla P, Prathosh AP. xViTCOS: Explainable Vision Transformer Based COVID-19 Screening Using Radiography. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2021; 10:1100110. [PMID: 34956741 PMCID: PMC8691725 DOI: 10.1109/jtehm.2021.3134096] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Revised: 11/18/2021] [Accepted: 12/06/2021] [Indexed: 12/15/2022]
Abstract
Objective: Since its outbreak, the rapid spread of COrona VIrus Disease 2019 (COVID-19) across the globe has pushed the health care system in many countries to the verge of collapse. Therefore, it is imperative to correctly identify COVID-19 positive patients and isolate them as soon as possible to contain the spread of the disease and reduce the ongoing burden on the healthcare system. The primary COVID-19 screening test, RT-PCR although accurate and reliable, has a long turn-around time. In the recent past, several researchers have demonstrated the use of Deep Learning (DL) methods on chest radiography (such as X-ray and CT) for COVID-19 detection. However, existing CNN based DL methods fail to capture the global context due to their inherent image-specific inductive bias. Methods: Motivated by this, in this work, we propose the use of vision transformers (instead of convolutional networks) for COVID-19 screening using the X-ray and CT images. We employ a multi-stage transfer learning technique to address the issue of data scarcity. Furthermore, we show that the features learned by our transformer networks are explainable. Results: We demonstrate that our method not only quantitatively outperforms the recent benchmarks but also focuses on meaningful regions in the images for detection (as confirmed by Radiologists), aiding not only in accurate diagnosis of COVID-19 but also in localization of the infected area. The code for our implementation can be found here - https://github.com/arnabkmondal/xViTCOS. Conclusion: The proposed method will help in timely identification of COVID-19 and efficient utilization of limited resources.
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Affiliation(s)
- Arnab Kumar Mondal
- Amar Nath and Shashi Khosla School of Information TechnologyIndian Institute of Technology Delhi New Delhi 110016 India.,Department of Electrical Communication EngineeringIndian Institute of Science (IISc) Bangalore 560 India
| | - Arnab Bhattacharjee
- UQ-IITD Academy of ResearchIndian Institute of Technology Delhi New Delhi 110016 India
| | - Parag Singla
- Department of Computer Science and EngineeringIndian Institute of Technology Delhi New Delhi 110016 India
| | - A P Prathosh
- Department of Electrical Communication EngineeringIndian Institute of Science (IISc) Bangalore 560 India
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61
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Danilov VV, Proutski A, Karpovsky A, Kirpich A, Litmanovich D, Nefaridze D, Talalov O, Semyonov S, Koniukhovskii V, Shvartc V, Gankin Y. Indirect supervision applied to COVID-19 and pneumonia classification. INFORMATICS IN MEDICINE UNLOCKED 2021; 28:100835. [PMID: 34977331 PMCID: PMC8712713 DOI: 10.1016/j.imu.2021.100835] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 12/11/2021] [Accepted: 12/23/2021] [Indexed: 01/08/2023] Open
Abstract
The novel coronavirus 19 (COVID-19) continues to have a devastating effect around the globe, leading many scientists and clinicians to actively seek to develop new techniques to assist with the tackling of this disease. Modern machine learning methods have shown promise in their adoption to assist the healthcare industry through their data and analytics-driven decision making, inspiring researchers to develop new angles to fight the virus. In this paper, we aim to develop a CNN-based method for the detection of COVID-19 by utilizing patients' chest X-ray images. Developing upon the inclusion of convolutional units, the proposed method makes use of indirect supervision based on Grad-CAM. This technique is used in the training process where Grad-CAM's attention heatmaps support the network's predictions. Despite recent progress, scarcity of data has thus far limited the development of a robust solution. We extend upon existing work by combining publicly available data across 5 different sources and carefully annotate the comprising images across three categories: normal, pneumonia, and COVID-19. To achieve a high classification accuracy, we propose a training pipeline based on indirect supervision of traditional classification networks, where the guidance is directed by an external algorithm. With this method, we observed that the widely used, standard networks can achieve an accuracy comparable to tailor-made models, specifically for COVID-19, with one network in particular, VGG-16, outperforming the best of the tailor-made models.
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Affiliation(s)
- Viacheslav V Danilov
- Tomsk Polytechnic University, Tomsk, Russia
- Research Institute for Complex Issues of Cardiovascular Diseases, Kemerovo, Russia
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62
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Aboutalebi H, Pavlova M, Shafiee MJ, Sabri A, Alaref A, Wong A. COVID-Net CXR-S: Deep Convolutional Neural Network for Severity Assessment of COVID-19 Cases from Chest X-ray Images. Diagnostics (Basel) 2021; 12:25. [PMID: 35054194 PMCID: PMC8774375 DOI: 10.3390/diagnostics12010025] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Revised: 12/07/2021] [Accepted: 12/13/2021] [Indexed: 12/17/2022] Open
Abstract
The world is still struggling in controlling and containing the spread of the COVID-19 pandemic caused by the SARS-CoV-2 virus. The medical conditions associated with SARS-CoV-2 infections have resulted in a surge in the number of patients at clinics and hospitals, leading to a significantly increased strain on healthcare resources. As such, an important part of managing and handling patients with SARS-CoV-2 infections within the clinical workflow is severity assessment, which is often conducted with the use of chest X-ray (CXR) images. In this work, we introduce COVID-Net CXR-S, a convolutional neural network for predicting the airspace severity of a SARS-CoV-2 positive patient based on a CXR image of the patient's chest. More specifically, we leveraged transfer learning to transfer representational knowledge gained from over 16,000 CXR images from a multinational cohort of over 15,000 SARS-CoV-2 positive and negative patient cases into a custom network architecture for severity assessment. Experimental results using the RSNA RICORD dataset showed that the proposed COVID-Net CXR-S has potential to be a powerful tool for computer-aided severity assessment of CXR images of COVID-19 positive patients. Furthermore, radiologist validation on select cases by two board-certified radiologists with over 10 and 19 years of experience, respectively, showed consistency between radiologist interpretation and critical factors leveraged by COVID-Net CXR-S for severity assessment. While not a production-ready solution, the ultimate goal for the open source release of COVID-Net CXR-S is to act as a catalyst for clinical scientists, machine learning researchers, as well as citizen scientists to develop innovative new clinical decision support solutions for helping clinicians around the world manage the continuing pandemic.
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Affiliation(s)
- Hossein Aboutalebi
- Department of Computer Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada
- Waterloo Artificial Intelligence Institute, University of Waterloo, Waterloo, ON N2L 3G1, Canada; (M.J.S.); (A.W.)
| | - Maya Pavlova
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada;
| | - Mohammad Javad Shafiee
- Waterloo Artificial Intelligence Institute, University of Waterloo, Waterloo, ON N2L 3G1, Canada; (M.J.S.); (A.W.)
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada;
- DarwinAI Corp., Waterloo, ON N2V 1K4, Canada
| | - Ali Sabri
- Department of Radiology, Niagara Health, McMaster University, St. Catharines, ON L2S 0A9, Canada;
| | - Amer Alaref
- Department of Diagnostic Radiology, Thunder Bay Regional Health Sciences Centre, Thunder Bay, ON P7B 6V4, Canada;
- Department of Diagnostic Imaging, Northern Ontario School of Medicine, Thunder Bay, ON P7B 5E1, Canada
| | - Alexander Wong
- Waterloo Artificial Intelligence Institute, University of Waterloo, Waterloo, ON N2L 3G1, Canada; (M.J.S.); (A.W.)
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada;
- DarwinAI Corp., Waterloo, ON N2V 1K4, Canada
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63
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Ter-Sarkisov A. One Shot Model For The Prediction of COVID-19 And Lesions Segmentation In Chest CT Scans Through The Affinity Among Lesion Mask Features. Appl Soft Comput 2021; 116:108261. [PMID: 34924896 PMCID: PMC8668605 DOI: 10.1016/j.asoc.2021.108261] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 11/09/2021] [Accepted: 11/27/2021] [Indexed: 01/15/2023]
Abstract
We present a novel framework that integrates segmentation of lesion masks and prediction of COVID-19 in chest CT scans in one shot. In order to classify the whole input image, we introduce a type of associations among lesion mask features extracted from the scan slice that we refer to as affinities. First, we map mask features to the affinity space by training an affinity matrix. Next, we map them back into the feature space through a trainable affinity vector. Finally, this feature representation is used for the classification of the whole input scan slice. We achieve a 93.55% COVID-19 sensitivity, 96.93% common pneumonia sensitivity, 99.37% true negative rate and 97.37% F1-score on the test split of CNCB-NCOV dataset with 21192 chest CT scan slices. We also achieve a 0.4240 mean average precision on the lesion segmentation task. All source code, models and results are publicly available on https://github.com/AlexTS1980/COVID-Affinity-Model.
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Affiliation(s)
- Aram Ter-Sarkisov
- CitAI Research Center, Department of Computer Science, City University of London, United Kingdom
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Suri JS, Agarwal S, Carriero A, Paschè A, Danna PSC, Columbu M, Saba L, Viskovic K, Mehmedović A, Agarwal S, Gupta L, Faa G, Singh IM, Turk M, Chadha PS, Johri AM, Khanna NN, Mavrogeni S, Laird JR, Pareek G, Miner M, Sobel DW, Balestrieri A, Sfikakis PP, Tsoulfas G, Protogerou A, Misra DP, Agarwal V, Kitas GD, Teji JS, Al-Maini M, Dhanjil SK, Nicolaides A, Sharma A, Rathore V, Fatemi M, Alizad A, Krishnan PR, Nagy F, Ruzsa Z, Gupta A, Naidu S, Paraskevas KI, Kalra MK. COVLIAS 1.0 vs. MedSeg: Artificial Intelligence-Based Comparative Study for Automated COVID-19 Computed Tomography Lung Segmentation in Italian and Croatian Cohorts. Diagnostics (Basel) 2021; 11:diagnostics11122367. [PMID: 34943603 PMCID: PMC8699928 DOI: 10.3390/diagnostics11122367] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 11/29/2021] [Accepted: 12/13/2021] [Indexed: 02/07/2023] Open
Abstract
(1) Background: COVID-19 computed tomography (CT) lung segmentation is critical for COVID lung severity diagnosis. Earlier proposed approaches during 2020–2021 were semiautomated or automated but not accurate, user-friendly, and industry-standard benchmarked. The proposed study compared the COVID Lung Image Analysis System, COVLIAS 1.0 (GBTI, Inc., and AtheroPointTM, Roseville, CA, USA, referred to as COVLIAS), against MedSeg, a web-based Artificial Intelligence (AI) segmentation tool, where COVLIAS uses hybrid deep learning (HDL) models for CT lung segmentation. (2) Materials and Methods: The proposed study used 5000 ITALIAN COVID-19 positive CT lung images collected from 72 patients (experimental data) that confirmed the reverse transcription-polymerase chain reaction (RT-PCR) test. Two hybrid AI models from the COVLIAS system, namely, VGG-SegNet (HDL 1) and ResNet-SegNet (HDL 2), were used to segment the CT lungs. As part of the results, we compared both COVLIAS and MedSeg against two manual delineations (MD 1 and MD 2) using (i) Bland–Altman plots, (ii) Correlation coefficient (CC) plots, (iii) Receiver operating characteristic curve, and (iv) Figure of Merit and (v) visual overlays. A cohort of 500 CROATIA COVID-19 positive CT lung images (validation data) was used. A previously trained COVLIAS model was directly applied to the validation data (as part of Unseen-AI) to segment the CT lungs and compare them against MedSeg. (3) Result: For the experimental data, the four CCs between COVLIAS (HDL 1) vs. MD 1, COVLIAS (HDL 1) vs. MD 2, COVLIAS (HDL 2) vs. MD 1, and COVLIAS (HDL 2) vs. MD 2 were 0.96, 0.96, 0.96, and 0.96, respectively. The mean value of the COVLIAS system for the above four readings was 0.96. CC between MedSeg vs. MD 1 and MedSeg vs. MD 2 was 0.98 and 0.98, respectively. Both had a mean value of 0.98. On the validation data, the CC between COVLIAS (HDL 1) vs. MedSeg and COVLIAS (HDL 2) vs. MedSeg was 0.98 and 0.99, respectively. For the experimental data, the difference between the mean values for COVLIAS and MedSeg showed a difference of <2.5%, meeting the standard of equivalence. The average running times for COVLIAS and MedSeg on a single lung CT slice were ~4 s and ~10 s, respectively. (4) Conclusions: The performances of COVLIAS and MedSeg were similar. However, COVLIAS showed improved computing time over MedSeg.
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Affiliation(s)
- Jasjit S. Suri
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA; (I.M.S.); (P.S.C.)
- Advanced Knowledge Engineering Centre, Global Biomedical Technologies, Inc., Roseville, CA 95661, USA; (S.A.); (S.A.); (L.G.)
- Correspondence: ; Tel.: +1-(916)-749-5628
| | - Sushant Agarwal
- Advanced Knowledge Engineering Centre, Global Biomedical Technologies, Inc., Roseville, CA 95661, USA; (S.A.); (S.A.); (L.G.)
- Department of Computer Science Engineering, Pranveer Singh Institute of Technology, Kanpur 209305, India
| | - Alessandro Carriero
- Department of Radiology, “Maggiore della Carità” Hospital, University of Piemonte Orientale (UPO), 28100 Novara, Italy;
| | - Alessio Paschè
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy; (A.P.); (P.S.C.D.); (M.C.); (L.S.); (A.B.)
| | - Pietro S. C. Danna
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy; (A.P.); (P.S.C.D.); (M.C.); (L.S.); (A.B.)
| | - Marta Columbu
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy; (A.P.); (P.S.C.D.); (M.C.); (L.S.); (A.B.)
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy; (A.P.); (P.S.C.D.); (M.C.); (L.S.); (A.B.)
| | - Klaudija Viskovic
- Department of Radiology and Ultrasound, University Hospital for Infectious Diseases, 10 000 Zagreb, Croatia; (K.V.); (A.M.)
| | - Armin Mehmedović
- Department of Radiology and Ultrasound, University Hospital for Infectious Diseases, 10 000 Zagreb, Croatia; (K.V.); (A.M.)
| | - Samriddhi Agarwal
- Advanced Knowledge Engineering Centre, Global Biomedical Technologies, Inc., Roseville, CA 95661, USA; (S.A.); (S.A.); (L.G.)
- Department of Computer Science Engineering, Pranveer Singh Institute of Technology, Kanpur 209305, India
| | - Lakshya Gupta
- Advanced Knowledge Engineering Centre, Global Biomedical Technologies, Inc., Roseville, CA 95661, USA; (S.A.); (S.A.); (L.G.)
| | - Gavino Faa
- Department of Pathology, AOU of Cagliari, 09124 Cagliari, Italy;
| | - Inder M. Singh
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA; (I.M.S.); (P.S.C.)
| | - Monika Turk
- The Hanse-Wissenschaftskolleg Institute for Advanced Study, 27753 Delmenhorst, Germany;
| | - Paramjit S. Chadha
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA; (I.M.S.); (P.S.C.)
| | - Amer M. Johri
- Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, Canada;
| | - Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110076, India;
| | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Center, 17674 Athens, Greece;
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA 94574, USA;
| | - Gyan Pareek
- Minimally Invasive Urology Institute, Brown University, Providence, RI 02912, USA; (G.P.); (D.W.S.)
| | - Martin Miner
- Men’s Health Center, Miriam Hospital, Providence, RI 02906, USA;
| | - David W. Sobel
- Minimally Invasive Urology Institute, Brown University, Providence, RI 02912, USA; (G.P.); (D.W.S.)
| | - Antonella Balestrieri
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy; (A.P.); (P.S.C.D.); (M.C.); (L.S.); (A.B.)
| | - Petros P. Sfikakis
- Rheumatology Unit, National Kapodistrian University of Athens, 15772 Athens, Greece;
| | - George Tsoulfas
- Department of Surgery, Aristoteleion University of Thessaloniki, 54124 Thessaloniki, Greece;
| | - Athanasios Protogerou
- Cardiovascular Prevention and Research Unit, Department of Pathophysiology, National & Kapodistrian University of Athens, 15772 Athens, Greece;
| | - Durga Prasanna Misra
- Department of Immunology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India; (D.P.M.); (V.A.)
| | - Vikas Agarwal
- Department of Immunology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India; (D.P.M.); (V.A.)
| | - George D. Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley DY1 2HQ, UK;
- Arthritis Research UK Epidemiology Unit, Manchester University, Manchester M13 9PL, UK
| | - Jagjit S. Teji
- Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA;
| | - Mustafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON L4Z 4C4, Canada;
| | | | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre and University of Nicosia Medical School, Nicosia 2408, Cyprus;
| | - Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA 22904, USA;
| | - Vijay Rathore
- AtheroPoint LLC, Roseville, CA 95611, USA; (S.K.D.); (V.R.)
| | - Mostafa Fatemi
- Department of Physiology & Biomedical Engg., Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA;
| | - Azra Alizad
- Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA;
| | | | - Ferenc Nagy
- Internal Medicine Department, University of Szeged, 6725 Szeged, Hungary;
| | - Zoltan Ruzsa
- Invasive Cardiology Division, University of Szeged, 6725 Szeged, Hungary;
| | - Archna Gupta
- Radiology Department, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India;
| | - Subbaram Naidu
- Electrical Engineering Department, University of Minnesota, Duluth, MN 55812, USA;
| | | | - Mannudeep K. Kalra
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA;
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Bhardwaj P, Kaur A. A novel and efficient deep learning approach for COVID-19 detection using X-ray imaging modality. INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2021; 31:1775-1791. [PMID: 34518739 PMCID: PMC8426690 DOI: 10.1002/ima.22627] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 06/06/2021] [Accepted: 06/27/2021] [Indexed: 05/03/2023]
Abstract
With the exponential growth of COVID-19 cases, medical practitioners are searching for accurate and quick automated detection methods to prevent Covid from spreading while trying to reduce the computational requirement of devices. In this research article, a deep learning Convolutional Neural Network (CNN) based accurate and efficient ensemble model using deep learning is being proposed with 2161 COVID-19, 2022 pneumonia, and 5863 normal chest X-ray images that has been collected from previous publications and other online resources. To improve the detection accuracy contrast enhancement and image normalization have been done to produce better quality images at the pre-processing level. Further data augmentation methods are used by creating modified versions of images in the dataset to train the four efficient CNN models (Inceptionv3, DenseNet121, Xception, InceptionResNetv2) Experimental results provide 98.33% accuracy for binary class and 92.36% for multiclass. The performance evaluation metrics reveal that this tool can be very helpful for early disease diagnosis.
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Affiliation(s)
| | - Amanpreet Kaur
- Electronics and CommunicationThapar UniversityPatialaIndia
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Wong A, Lu J, Dorfman A, McInnis P, Famouri M, Manary D, Lee JRH, Lynch M. Fibrosis-Net: A Tailored Deep Convolutional Neural Network Design for Prediction of Pulmonary Fibrosis Progression From Chest CT Images. Front Artif Intell 2021; 4:764047. [PMID: 34805974 PMCID: PMC8596329 DOI: 10.3389/frai.2021.764047] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 10/11/2021] [Indexed: 01/02/2023] Open
Abstract
Pulmonary fibrosis is a devastating chronic lung disease that causes irreparable lung tissue scarring and damage, resulting in progressive loss in lung capacity and has no known cure. A critical step in the treatment and management of pulmonary fibrosis is the assessment of lung function decline, with computed tomography (CT) imaging being a particularly effective method for determining the extent of lung damage caused by pulmonary fibrosis. Motivated by this, we introduce Fibrosis-Net, a deep convolutional neural network design tailored for the prediction of pulmonary fibrosis progression from chest CT images. More specifically, machine-driven design exploration was leveraged to determine a strong architectural design for CT lung analysis, upon which we build a customized network design tailored for predicting forced vital capacity (FVC) based on a patient's CT scan, initial spirometry measurement, and clinical metadata. Finally, we leverage an explainability-driven performance validation strategy to study the decision-making behavior of Fibrosis-Net as to verify that predictions are based on relevant visual indicators in CT images. Experiments using a patient cohort from the OSIC Pulmonary Fibrosis Progression Challenge showed that the proposed Fibrosis-Net is able to achieve a significantly higher modified Laplace Log Likelihood score than the winning solutions on the challenge. Furthermore, explainability-driven performance validation demonstrated that the proposed Fibrosis-Net exhibits correct decision-making behavior by leveraging clinically-relevant visual indicators in CT images when making predictions on pulmonary fibrosis progress. Fibrosis-Net is able to achieve a significantly higher modified Laplace Log Likelihood score than the winning solutions on the OSIC Pulmonary Fibrosis Progression Challenge, and has been shown to exhibit correct decision-making behavior when making predictions. Fibrosis-Net is available to the general public in an open-source and open access manner as part of the OpenMedAI initiative. While Fibrosis-Net is not yet a production-ready clinical assessment solution, we hope that its release will encourage researchers, clinicians, and citizen data scientists alike to leverage and build upon it.
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Affiliation(s)
- Alexander Wong
- Vision and Image Processing Research Group, University of Waterloo, Waterloo, ON, Canada
- Waterloo Artificial Intelligence Institute, University of Waterloo, Waterloo, ON, Canada
- DarwinAI Corp., Waterloo, ON, Canada
| | - Jack Lu
- DarwinAI Corp., Waterloo, ON, Canada
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Khan MA, Alhaisoni M, Tariq U, Hussain N, Majid A, Damaševičius R, Maskeliūnas R. COVID-19 Case Recognition from Chest CT Images by Deep Learning, Entropy-Controlled Firefly Optimization, and Parallel Feature Fusion. SENSORS (BASEL, SWITZERLAND) 2021; 21:7286. [PMID: 34770595 PMCID: PMC8588229 DOI: 10.3390/s21217286] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Revised: 10/28/2021] [Accepted: 10/29/2021] [Indexed: 12/12/2022]
Abstract
In healthcare, a multitude of data is collected from medical sensors and devices, such as X-ray machines, magnetic resonance imaging, computed tomography (CT), and so on, that can be analyzed by artificial intelligence methods for early diagnosis of diseases. Recently, the outbreak of the COVID-19 disease caused many deaths. Computer vision researchers support medical doctors by employing deep learning techniques on medical images to diagnose COVID-19 patients. Various methods were proposed for COVID-19 case classification. A new automated technique is proposed using parallel fusion and optimization of deep learning models. The proposed technique starts with a contrast enhancement using a combination of top-hat and Wiener filters. Two pre-trained deep learning models (AlexNet and VGG16) are employed and fine-tuned according to target classes (COVID-19 and healthy). Features are extracted and fused using a parallel fusion approach-parallel positive correlation. Optimal features are selected using the entropy-controlled firefly optimization method. The selected features are classified using machine learning classifiers such as multiclass support vector machine (MC-SVM). Experiments were carried out using the Radiopaedia database and achieved an accuracy of 98%. Moreover, a detailed analysis is conducted and shows the improved performance of the proposed scheme.
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Affiliation(s)
- Muhammad Attique Khan
- Department of Computer Science, HITEC University, Taxila 47080, Pakistan; (M.A.K.); (N.H.); (A.M.)
| | - Majed Alhaisoni
- College of Computer Science and Engineering, University of Ha’il, Ha’il 55211, Saudi Arabia;
| | - Usman Tariq
- Information Systems Department, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al Khraj 11942, Saudi Arabia;
| | - Nazar Hussain
- Department of Computer Science, HITEC University, Taxila 47080, Pakistan; (M.A.K.); (N.H.); (A.M.)
| | - Abdul Majid
- Department of Computer Science, HITEC University, Taxila 47080, Pakistan; (M.A.K.); (N.H.); (A.M.)
| | - Robertas Damaševičius
- Faculty of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland
| | - Rytis Maskeliūnas
- Department of Multimedia Engineering, Kaunas University of Technology, 51368 Kaunas, Lithuania;
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Suri JS, Agarwal S, Gupta SK, Puvvula A, Viskovic K, Suri N, Alizad A, El-Baz A, Saba L, Fatemi M, Naidu DS. Systematic Review of Artificial Intelligence in Acute Respiratory Distress Syndrome for COVID-19 Lung Patients: A Biomedical Imaging Perspective. IEEE J Biomed Health Inform 2021; 25:4128-4139. [PMID: 34379599 PMCID: PMC8843049 DOI: 10.1109/jbhi.2021.3103839] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Revised: 05/24/2021] [Accepted: 08/06/2021] [Indexed: 12/15/2022]
Abstract
SARS-CoV-2 has infected over ∼165 million people worldwide causing Acute Respiratory Distress Syndrome (ARDS) and has killed ∼3.4 million people. Artificial Intelligence (AI) has shown to benefit in the biomedical image such as X-ray/Computed Tomography in diagnosis of ARDS, but there are limited AI-based systematic reviews (aiSR). The purpose of this study is to understand the Risk-of-Bias (RoB) in a non-randomized AI trial for handling ARDS using novel AtheroPoint-AI-Bias (AP(ai)Bias). Our hypothesis for acceptance of a study to be in low RoB must have a mean score of 80% in a study. Using the PRISMA model, 42 best AI studies were analyzed to understand the RoB. Using the AP(ai)Bias paradigm, the top 19 studies were then chosen using the raw-cutoff of 1.9. This was obtained using the intersection of the cumulative plot of "mean score vs. study" and score distribution. Finally, these studies were benchmarked against ROBINS-I and PROBAST paradigm. Our observation showed that AP(ai)Bias, ROBINS-I, and PROBAST had only 32%, 16%, and 26% studies, respectively in low-moderate RoB (cutoff>2.5), however none of them met the RoB hypothesis. Further, the aiSR analysis recommends six primary and six secondary recommendations for the non-randomized AI for ARDS. The primary recommendations for improvement in AI-based ARDS design inclusive of (i) comorbidity, (ii) inter-and intra-observer variability studies, (iii) large data size, (iv) clinical validation, (v) granularity of COVID-19 risk, and (vi) cross-modality scientific validation. The AI is an important component for diagnosis of ARDS and the recommendations must be followed to lower the RoB.
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Affiliation(s)
- Jasjit S. Suri
- Stroke Diagnosis and Monitoring DivisionAtheroPoint LLCRosevilleCA95661USA
| | - Sushant Agarwal
- Advanced Knowledge Engineering CentreGBTIRosevilleCA95661USA
- Department of Computer Science EngineeringPranveer Singh Institute of Technology (PSIT)Kanpur209305India
| | - Suneet K. Gupta
- Department of Computer Science EngineeringBennett UniversityNoida524101India
| | - Anudeep Puvvula
- Stroke Diagnosis and Monitoring DivisionAtheroPoint LLCRosevilleCA95661USA
- Annu's Hospitals for Skin and DiabetesNellore524101India
| | | | - Neha Suri
- Mira Loma High SchoolSacramentoCA95821USA
| | - Azra Alizad
- Department of RadiologyMayo Clinic College of Medicine and ScienceRochesterMN55905USA
| | - Ayman El-Baz
- Department of BioengineeringUniversity of LouisvilleLouisvilleKY40292USA
| | - Luca Saba
- Department of RadiologyAzienda Ospedaliero Universitaria (AOU)09124CagliariItaly
| | - Mostafa Fatemi
- Department of Physiology and Biomedical EngineeringMayo Clinic College of Medicine and ScienceRochesterMN55905USA
| | - D. Subbaram Naidu
- Electrical Engineering DepartmentUniversity of MinnesotaDuluthMN55812USA
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69
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Suri JS, Agarwal S, Elavarthi P, Pathak R, Ketireddy V, Columbu M, Saba L, Gupta SK, Faa G, Singh IM, Turk M, Chadha PS, Johri AM, Khanna NN, Viskovic K, Mavrogeni S, Laird JR, Pareek G, Miner M, Sobel DW, Balestrieri A, Sfikakis PP, Tsoulfas G, Protogerou A, Misra DP, Agarwal V, Kitas GD, Teji JS, Al-Maini M, Dhanjil SK, Nicolaides A, Sharma A, Rathore V, Fatemi M, Alizad A, Krishnan PR, Ferenc N, Ruzsa Z, Gupta A, Naidu S, Kalra MK. Inter-Variability Study of COVLIAS 1.0: Hybrid Deep Learning Models for COVID-19 Lung Segmentation in Computed Tomography. Diagnostics (Basel) 2021; 11:2025. [PMID: 34829372 PMCID: PMC8625039 DOI: 10.3390/diagnostics11112025] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 10/26/2021] [Accepted: 10/27/2021] [Indexed: 02/05/2023] Open
Abstract
Background: For COVID-19 lung severity, segmentation of lungs on computed tomography (CT) is the first crucial step. Current deep learning (DL)-based Artificial Intelligence (AI) models have a bias in the training stage of segmentation because only one set of ground truth (GT) annotations are evaluated. We propose a robust and stable inter-variability analysis of CT lung segmentation in COVID-19 to avoid the effect of bias. Methodology: The proposed inter-variability study consists of two GT tracers for lung segmentation on chest CT. Three AI models, PSP Net, VGG-SegNet, and ResNet-SegNet, were trained using GT annotations. We hypothesized that if AI models are trained on the GT tracings from multiple experience levels, and if the AI performance on the test data between these AI models is within the 5% range, one can consider such an AI model robust and unbiased. The K5 protocol (training to testing: 80%:20%) was adapted. Ten kinds of metrics were used for performance evaluation. Results: The database consisted of 5000 CT chest images from 72 COVID-19-infected patients. By computing the coefficient of correlations (CC) between the output of the two AI models trained corresponding to the two GT tracers, computing their differences in their CC, and repeating the process for all three AI-models, we show the differences as 0%, 0.51%, and 2.04% (all < 5%), thereby validating the hypothesis. The performance was comparable; however, it had the following order: ResNet-SegNet > PSP Net > VGG-SegNet. Conclusions: The AI models were clinically robust and stable during the inter-variability analysis on the CT lung segmentation on COVID-19 patients.
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Affiliation(s)
- Jasjit S. Suri
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA; (I.M.S.); (P.S.C.)
- Advanced Knowledge Engineering Centre, GBTI, Roseville, CA 95661, USA; (S.A.); (P.E.)
| | - Sushant Agarwal
- Advanced Knowledge Engineering Centre, GBTI, Roseville, CA 95661, USA; (S.A.); (P.E.)
- Department of Computer Science Engineering, PSIT, Kanpur 209305, India
| | - Pranav Elavarthi
- Advanced Knowledge Engineering Centre, GBTI, Roseville, CA 95661, USA; (S.A.); (P.E.)
- Thomas Jefferson High School for Science and Technology, Alexandria, VA 22312, USA
| | - Rajesh Pathak
- Department of Computer Science Engineering, Rawatpura Sarkar University, Raipur 492001, India;
| | | | - Marta Columbu
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 10015 Cagliari, Italy; (M.C.); (L.S.); (A.B.)
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 10015 Cagliari, Italy; (M.C.); (L.S.); (A.B.)
| | - Suneet K. Gupta
- Department of Computer Science, Bennett University, Noida 201310, India;
| | - Gavino Faa
- Department of Pathology, Azienda Ospedaliero Universitaria (A.O.U.), 10015 Cagliari, Italy;
| | - Inder M. Singh
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA; (I.M.S.); (P.S.C.)
| | - Monika Turk
- The Hanse-Wissenschaftskolleg Institute for Advanced Study, 27753 Delmenhorst, Germany;
| | - Paramjit S. Chadha
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA; (I.M.S.); (P.S.C.)
| | - Amer M. Johri
- Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, Canada;
| | - Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110076, India;
| | | | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Center, 10558 Athens, Greece;
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA 94574, USA;
| | - Gyan Pareek
- Minimally Invasive Urology Institute, Brown University, Providence, RI 02912, USA; (G.P.); (D.W.S.)
| | - Martin Miner
- Men’s Health Center, Miriam Hospital, Providence, RI 02906, USA;
| | - David W. Sobel
- Minimally Invasive Urology Institute, Brown University, Providence, RI 02912, USA; (G.P.); (D.W.S.)
| | - Antonella Balestrieri
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 10015 Cagliari, Italy; (M.C.); (L.S.); (A.B.)
| | - Petros P. Sfikakis
- Rheumatology Unit, National & Kapodistrian University of Athens, 10679 Athens, Greece;
| | - George Tsoulfas
- Aristoteleion University of Thessaloniki, 54636 Thessaloniki, Greece;
| | | | - Durga Prasanna Misra
- Department of Immunology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India; (D.P.M.); (V.A.)
| | - Vikas Agarwal
- Department of Immunology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India; (D.P.M.); (V.A.)
| | - George D. Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley DY1 2HQ, UK;
- Arthritis Research UK Epidemiology Unit, Manchester University, Manchester M13 9PT, UK
| | - Jagjit S. Teji
- Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA;
| | - Mustafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON L4Z 4C4, Canada;
| | | | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre, University of Nicosia Medical School, Nicosia 2368, Cyprus;
| | - Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA 22904, USA;
| | - Vijay Rathore
- AtheroPoint LLC, Roseville, CA 95611, USA; (S.K.D.); (V.R.)
| | - Mostafa Fatemi
- Department of Physiology & Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA;
| | - Azra Alizad
- Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA;
| | | | - Nagy Ferenc
- Internal Medicine Department, University of Szeged, 6725 Szeged, Hungary;
| | - Zoltan Ruzsa
- Zoltan Invasive Cardiology Division, University of Szeged, 6725 Szeged, Hungary;
| | - Archna Gupta
- Radiology Department, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India;
| | - Subbaram Naidu
- Electrical Engineering Department, University of Minnesota, Duluth, MN 55812, USA;
| | - Mannudeep K. Kalra
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA;
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70
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Horry MJ, Chakraborty S, Pradhan B, Fallahpoor M, Chegeni H, Paul M. Factors determining generalization in deep learning models for scoring COVID-CT images. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:9264-9293. [PMID: 34814345 DOI: 10.3934/mbe.2021456] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The COVID-19 pandemic has inspired unprecedented data collection and computer vision modelling efforts worldwide, focused on the diagnosis of COVID-19 from medical images. However, these models have found limited, if any, clinical application due in part to unproven generalization to data sets beyond their source training corpus. This study investigates the generalizability of deep learning models using publicly available COVID-19 Computed Tomography data through cross dataset validation. The predictive ability of these models for COVID-19 severity is assessed using an independent dataset that is stratified for COVID-19 lung involvement. Each inter-dataset study is performed using histogram equalization, and contrast limited adaptive histogram equalization with and without a learning Gabor filter. We show that under certain conditions, deep learning models can generalize well to an external dataset with F1 scores up to 86%. The best performing model shows predictive accuracy of between 75% and 96% for lung involvement scoring against an external expertly stratified dataset. From these results we identify key factors promoting deep learning generalization, being primarily the uniform acquisition of training images, and secondly diversity in CT slice position.
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Affiliation(s)
- Michael James Horry
- Center for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, Australia
| | - Subrata Chakraborty
- Center for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, Australia
| | - Biswajeet Pradhan
- Center for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, Australia
- Center of Excellence for Climate Change Research, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Earth Observation Center, Institute of Climate Change, Universiti Kebangsaan Malaysia, Selangor 43600, Malaysia
| | - Maryam Fallahpoor
- Center for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, Australia
| | - Hossein Chegeni
- Fellowship of Interventional Radiology Imaging Center, IranMehr General Hospital, Iran
| | - Manoranjan Paul
- Machine Vision and Digital Health (MaViDH), School of Computing, Mathematics, and Engineering, Charles Sturt University, Australia
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71
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BARF: A new direct and cross-based binary residual feature fusion with uncertainty-aware module for medical image classification. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2021.07.024] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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72
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Liu J, Malekzadeh M, Mirian N, Song TA, Liu C, Dutta J. Artificial Intelligence-Based Image Enhancement in PET Imaging: Noise Reduction and Resolution Enhancement. PET Clin 2021; 16:553-576. [PMID: 34537130 PMCID: PMC8457531 DOI: 10.1016/j.cpet.2021.06.005] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
High noise and low spatial resolution are two key confounding factors that limit the qualitative and quantitative accuracy of PET images. Artificial intelligence models for image denoising and deblurring are becoming increasingly popular for the postreconstruction enhancement of PET images. We present a detailed review of recent efforts for artificial intelligence-based PET image enhancement with a focus on network architectures, data types, loss functions, and evaluation metrics. We also highlight emerging areas in this field that are quickly gaining popularity, identify barriers to large-scale adoption of artificial intelligence models for PET image enhancement, and discuss future directions.
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Affiliation(s)
- Juan Liu
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Masoud Malekzadeh
- Department of Electrical and Computer Engineering, University of Massachusetts Lowell, 1 University Avenue, Ball 301, Lowell, MA 01854, USA
| | - Niloufar Mirian
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Tzu-An Song
- Department of Electrical and Computer Engineering, University of Massachusetts Lowell, 1 University Avenue, Ball 301, Lowell, MA 01854, USA
| | - Chi Liu
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.
| | - Joyita Dutta
- Department of Electrical and Computer Engineering, University of Massachusetts Lowell, 1 University Avenue, Ball 301, Lowell, MA 01854, USA; Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
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73
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A Histogram-Based Low-Complexity Approach for the Effective Detection of COVID-19 Disease from CT and X-ray Images. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11198867] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The global COVID-19 pandemic certainly has posed one of the more difficult challenges for researchers in the current century. The development of an automatic diagnostic tool, able to detect the disease in its early stage, could undoubtedly offer a great advantage to the battle against the pandemic. In this regard, most of the research efforts have been focused on the application of Deep Learning (DL) techniques to chest images, including traditional chest X-rays (CXRs) and Computed Tomography (CT) scans. Although these approaches have demonstrated their effectiveness in detecting the COVID-19 disease, they are of huge computational complexity and require large datasets for training. In addition, there may not exist a large amount of COVID-19 CXRs and CT scans available to researchers. To this end, in this paper, we propose an approach based on the evaluation of the histogram from a common class of images that is considered as the target. A suitable inter-histogram distance measures how this target histogram is far from the histogram evaluated on a test image: if this distance is greater than a threshold, the test image is labeled as anomaly, i.e., the scan belongs to a patient affected by COVID-19 disease. Extensive experimental results and comparisons with some benchmark state-of-the-art methods support the effectiveness of the developed approach, as well as demonstrate that, at least when the images of the considered datasets are homogeneous enough (i.e., a few outliers are present), it is not really needed to resort to complex-to-implement DL techniques, in order to attain an effective detection of the COVID-19 disease. Despite the simplicity of the proposed approach, all the considered metrics (i.e., accuracy, precision, recall, and F-measure) attain a value of 1.0 under the selected datasets, a result comparable to the corresponding state-of-the-art DNN approaches, but with a remarkable computational simplicity.
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74
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Singh G, Yow KC. Object or Background: An Interpretable Deep Learning Model for COVID-19 Detection from CT-Scan Images. Diagnostics (Basel) 2021; 11:1732. [PMID: 34574073 PMCID: PMC8465137 DOI: 10.3390/diagnostics11091732] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 09/09/2021] [Accepted: 09/14/2021] [Indexed: 11/17/2022] Open
Abstract
The new strains of the pandemic COVID-19 are still looming. It is important to develop multiple approaches for timely and accurate detection of COVID-19 and its variants. Deep learning techniques are well proved for their efficiency in providing solutions to many social and economic problems. However, the transparency of the reasoning process of a deep learning model related to a high stake decision is a necessity. In this work, we propose an interpretable deep learning model Ps-ProtoPNet to detect COVID-19 from the medical images. Ps-ProtoPNet classifies the images by recognizing the objects rather than their background in the images. We demonstrate our model on the dataset of the chest CT-scan images. The highest accuracy that our model achieves is 99.29%.
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Affiliation(s)
| | - Kin-Choong Yow
- Faculty of Engineering and Applied Science, University of Regina, Regina, SK S4S 0A2, Canada;
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75
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Densely connected attention network for diagnosing COVID-19 based on chest CT. Comput Biol Med 2021; 137:104857. [PMID: 34520988 PMCID: PMC8427919 DOI: 10.1016/j.compbiomed.2021.104857] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 09/05/2021] [Accepted: 09/06/2021] [Indexed: 12/31/2022]
Abstract
BACKGROUND To fully enhance the feature extraction capabilities of deep learning models, so as to accurately diagnose coronavirus disease 2019 (COVID-19) based on chest CT images, a densely connected attention network (DenseANet) was constructed by utilizing the self-attention mechanism in deep learning. METHODS During the construction of the DenseANet, we not only densely connected attention features within and between the feature extraction blocks with the same scale, but also densely connected attention features with different scales at the end of the deep model, thereby further enhancing the high-order features. In this way, as the depth of the deep model increases, the spatial attention features generated by different layers can be densely connected and gradually transferred to deeper layers. The DenseANet takes CT images of the lung fields segmented by an improved U-Net as inputs and outputs the probability of the patients suffering from COVID-19. RESULTS Compared with exiting attention networks, DenseANet can maximize the utilization of self-attention features at different depths in the model. A five-fold cross-validation experiment was performed on a dataset containing 2993 CT scans of 2121 patients, and experiments showed that the DenseANet can effectively locate the lung lesions of patients infected with SARS-CoV-2, and distinguish COVID-19, common pneumonia and normal controls with an average of 96.06% Acc and 0.989 AUC. CONCLUSIONS The DenseANet we proposed can generate strong attention features and achieve the best diagnosis results. In addition, the proposed method of densely connecting attention features can be easily extended to other advanced deep learning methods to improve their performance in related tasks.
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76
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Al-Ali A, Elharrouss O, Qidwai U, Al-Maaddeed S. ANFIS-Net for automatic detection of COVID-19. Sci Rep 2021; 11:17318. [PMID: 34453082 PMCID: PMC8397755 DOI: 10.1038/s41598-021-96601-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 08/04/2021] [Indexed: 12/24/2022] Open
Abstract
Among the most leading causes of mortality across the globe are infectious diseases which have cost tremendous lives with the latest being coronavirus (COVID-19) that has become the most recent challenging issue. The extreme nature of this infectious virus and its ability to spread without control has made it mandatory to find an efficient auto-diagnosis system to assist the people who work in touch with the patients. As fuzzy logic is considered a powerful technique for modeling vagueness in medical practice, an Adaptive Neuro-Fuzzy Inference System (ANFIS) was proposed in this paper as a key rule for automatic COVID-19 detection from chest X-ray images based on the characteristics derived by texture analysis using gray level co-occurrence matrix (GLCM) technique. Unlike the proposed method, especially deep learning-based approaches, the proposed ANFIS-based method can work on small datasets. The results were promising performance accuracy, and compared with the other state-of-the-art techniques, the proposed method gives the same performance as the deep learning with complex architectures using many backbone.
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Affiliation(s)
- Afnan Al-Ali
- Department of Computer Science and Engineering, Qatar University, Doha, Qatar.
| | - Omar Elharrouss
- Department of Computer Science and Engineering, Qatar University, Doha, Qatar
| | - Uvais Qidwai
- Department of Computer Science and Engineering, Qatar University, Doha, Qatar
| | - Somaya Al-Maaddeed
- Department of Computer Science and Engineering, Qatar University, Doha, Qatar
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77
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COVID-CGAN: Efficient Deep Learning Approach for COVID-19 Detection Based on CXR Images Using Conditional GANs. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11167174] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
COVID-19, a novel coronavirus infectious disease, has spread around the world, resulting in a large number of deaths. Due to a lack of physicians, emergency facilities, and equipment, medical systems have been unable to treat all patients in many countries. Deep learning is a promising approach for providing solutions to COVID-19 based on patients’ medical images. As COVID-19 is a new disease, its related dataset is still being collected and published. Small COVID-19 datasets may not be sufficient to build powerful deep learning detection models. Such models are often over-fitted, and their prediction results cannot be generalized. To fill this gap, we propose a deep learning approach for accurately detecting COVID-19 cases based on chest X-ray (CXR) images. For the proposed approach, named COVID-CGAN, we first generated a larger dataset using generative adversarial networks (GANs). Specifically, a customized conditional GAN (CGAN) was designed to generate the target COVID-19 CXR images. The expanded dataset, which contains 84.8% generated images and 15.2% original images, was then used for training five deep detection models: InceptionResNetV2, Xception, SqueezeNet, VGG16, and AlexNet. The results show that the use of the synthetic CXR images, which were generated by the customized CGAN, helped all deep learning models to achieve high detection accuracies. In particular, the highest accuracy was achieved by the InceptionResNetV2 model, which was 99.72% accurate with only ten epochs. All five models achieved kappa coefficients between 0.81 and 1, which is interpreted as an almost perfect agreement between the actual labels and the detected labels. Furthermore, the experiment showed that some models were faster yet smaller compared to the others but could still achieve high accuracy. For instance, SqueezeNet, which is a small network, required only three minutes and achieved comparable accuracy to larger networks such as InceptionResNetV2, which needed about 143 min. Our proposed approach can be applied to other fields with scarce datasets.
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78
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Suri JS, Agarwal S, Pathak R, Ketireddy V, Columbu M, Saba L, Gupta SK, Faa G, Singh IM, Turk M, Chadha PS, Johri AM, Khanna NN, Viskovic K, Mavrogeni S, Laird JR, Pareek G, Miner M, Sobel DW, Balestrieri A, Sfikakis PP, Tsoulfas G, Protogerou A, Misra DP, Agarwal V, Kitas GD, Teji JS, Al-Maini M, Dhanjil SK, Nicolaides A, Sharma A, Rathore V, Fatemi M, Alizad A, Krishnan PR, Frence N, Ruzsa Z, Gupta A, Naidu S, Kalra M. COVLIAS 1.0: Lung Segmentation in COVID-19 Computed Tomography Scans Using Hybrid Deep Learning Artificial Intelligence Models. Diagnostics (Basel) 2021; 11:1405. [PMID: 34441340 PMCID: PMC8392426 DOI: 10.3390/diagnostics11081405] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 07/28/2021] [Accepted: 07/29/2021] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND COVID-19 lung segmentation using Computed Tomography (CT) scans is important for the diagnosis of lung severity. The process of automated lung segmentation is challenging due to (a) CT radiation dosage and (b) ground-glass opacities caused by COVID-19. The lung segmentation methodologies proposed in 2020 were semi- or automated but not reliable, accurate, and user-friendly. The proposed study presents a COVID Lung Image Analysis System (COVLIAS 1.0, AtheroPoint™, Roseville, CA, USA) consisting of hybrid deep learning (HDL) models for lung segmentation. METHODOLOGY The COVLIAS 1.0 consists of three methods based on solo deep learning (SDL) or hybrid deep learning (HDL). SegNet is proposed in the SDL category while VGG-SegNet and ResNet-SegNet are designed under the HDL paradigm. The three proposed AI approaches were benchmarked against the National Institute of Health (NIH)-based conventional segmentation model using fuzzy-connectedness. A cross-validation protocol with a 40:60 ratio between training and testing was designed, with 10% validation data. The ground truth (GT) was manually traced by a radiologist trained personnel. For performance evaluation, nine different criteria were selected to perform the evaluation of SDL or HDL lung segmentation regions and lungs long axis against GT. RESULTS Using the database of 5000 chest CT images (from 72 patients), COVLIAS 1.0 yielded AUC of ~0.96, ~0.97, ~0.98, and ~0.96 (p-value < 0.001), respectively within 5% range of GT area, for SegNet, VGG-SegNet, ResNet-SegNet, and NIH. The mean Figure of Merit using four models (left and right lung) was above 94%. On benchmarking against the National Institute of Health (NIH) segmentation method, the proposed model demonstrated a 58% and 44% improvement in ResNet-SegNet, 52% and 36% improvement in VGG-SegNet for lung area, and lung long axis, respectively. The PE statistics performance was in the following order: ResNet-SegNet > VGG-SegNet > NIH > SegNet. The HDL runs in <1 s on test data per image. CONCLUSIONS The COVLIAS 1.0 system can be applied in real-time for radiology-based clinical settings.
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Affiliation(s)
- Jasjit S. Suri
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA; (I.M.S.); (P.S.C.)
- Advanced Knowledge Engineering Centre, GBTI, Roseville, CA 95661, USA;
| | - Sushant Agarwal
- Advanced Knowledge Engineering Centre, GBTI, Roseville, CA 95661, USA;
- Department of Computer Science Engineering, PSIT, Kanpur 209305, India
| | - Rajesh Pathak
- Department of Computer Science Engineering, Rawatpura Sarkar University, Raipur 492015, India;
| | | | - Marta Columbu
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy; (M.C.); (L.S.); (A.B.)
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy; (M.C.); (L.S.); (A.B.)
| | - Suneet K. Gupta
- Department of Computer Science, Bennett University, Noida 201310, India;
| | - Gavino Faa
- Department of Pathology—AOU of Cagliari, 09124 Cagliari, Italy;
| | - Inder M. Singh
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA; (I.M.S.); (P.S.C.)
| | - Monika Turk
- The Hanse-Wissenschaftskolleg Institute for Advanced Study, 27753 Delmenhorst, Germany;
| | - Paramjit S. Chadha
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA; (I.M.S.); (P.S.C.)
| | - Amer M. Johri
- Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, Canada;
| | - Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 208011, India;
| | - Klaudija Viskovic
- Department of Radiology, University Hospital for Infectious Diseases, 10000 Zagreb, Croatia;
| | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Center, 176 74 Athens, Greece;
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA 94574, USA;
| | - Gyan Pareek
- Minimally Invasive Urology Institute, Brown University, Providence City, RI 02912, USA; (G.P.); (D.W.S.)
| | - Martin Miner
- Men’s Health Center, Miriam Hospital Providence, Providence, RI 02906, USA;
| | - David W. Sobel
- Minimally Invasive Urology Institute, Brown University, Providence City, RI 02912, USA; (G.P.); (D.W.S.)
| | - Antonella Balestrieri
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy; (M.C.); (L.S.); (A.B.)
| | - Petros P. Sfikakis
- Rheumatology Unit, National Kapodistrian University of Athens, 157 72 Athens, Greece;
| | - George Tsoulfas
- Department of Transplantation Surgery, Aristoteleion University of Thessaloniki, 541 24 Thessaloniki, Greece;
| | | | - Durga Prasanna Misra
- Department of Immunology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India; (D.P.M.); (V.A.)
| | - Vikas Agarwal
- Department of Immunology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India; (D.P.M.); (V.A.)
| | - George D. Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley DY1 2HQ, UK;
- Arthritis Research UK Epidemiology Unit, Manchester University, Manchester M13 9PL, UK
| | - Jagjit S. Teji
- Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA;
| | - Mustafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON M5G 1N8, Canada;
| | | | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre, University of Nicosia Medical School, Nicosia 2408, Cyprus;
| | - Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA 22904, USA;
| | - Vijay Rathore
- Athero Point LLC, Roseville, CA 95611, USA; (S.K.D.); (V.R.)
| | - Mostafa Fatemi
- Department of Physiology & Biomedical Engg., Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA;
| | - Azra Alizad
- Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA;
| | | | - Nagy Frence
- Department of Internal Medicines, Invasive Cardiology Division, University of Szeged, 6720 Szeged, Hungary; (N.F.); (Z.R.)
| | - Zoltan Ruzsa
- Department of Internal Medicines, Invasive Cardiology Division, University of Szeged, 6720 Szeged, Hungary; (N.F.); (Z.R.)
| | - Archna Gupta
- Radiology Department, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India;
| | - Subbaram Naidu
- Electrical Engineering Department, University of Minnesota, Duluth, MN 55455, USA;
| | - Mannudeep Kalra
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA;
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Alshazly H, Linse C, Abdalla M, Barth E, Martinetz T. COVID-Nets: deep CNN architectures for detecting COVID-19 using chest CT scans. PeerJ Comput Sci 2021; 7:e655. [PMID: 34401477 PMCID: PMC8330434 DOI: 10.7717/peerj-cs.655] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 07/09/2021] [Indexed: 05/10/2023]
Abstract
In this paper we propose two novel deep convolutional network architectures, CovidResNet and CovidDenseNet, to diagnose COVID-19 based on CT images. The models enable transfer learning between different architectures, which might significantly boost the diagnostic performance. Whereas novel architectures usually suffer from the lack of pretrained weights, our proposed models can be partly initialized with larger baseline models like ResNet50 and DenseNet121, which is attractive because of the abundance of public repositories. The architectures are utilized in a first experimental study on the SARS-CoV-2 CT-scan dataset, which contains 4173 CT images for 210 subjects structured in a subject-wise manner into three different classes. The models differentiate between COVID-19, non-COVID-19 viral pneumonia, and healthy samples. We also investigate their performance under three binary classification scenarios where we distinguish COVID-19 from healthy, COVID-19 from non-COVID-19 viral pneumonia, and non-COVID-19 from healthy, respectively. Our proposed models achieve up to 93.87% accuracy, 99.13% precision, 92.49% sensitivity, 97.73% specificity, 95.70% F1-score, and 96.80% AUC score for binary classification, and up to 83.89% accuracy, 80.36% precision, 82.04% sensitivity, 92.07% specificity, 81.05% F1-score, and 94.20% AUC score for the three-class classification tasks. We also validated our models on the COVID19-CT dataset to differentiate COVID-19 and other non-COVID-19 viral infections, and our CovidDenseNet model achieved the best performance with 81.77% accuracy, 79.05% precision, 84.69% sensitivity, 79.05% specificity, 81.77% F1-score, and 87.50% AUC score. The experimental results reveal the effectiveness of the proposed networks in automated COVID-19 detection where they outperform standard models on the considered datasets while being more efficient.
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Affiliation(s)
- Hammam Alshazly
- Institut für Neuro- und Bioinformatik, University of Lübeck, Lübeck, Germany
- Faculty of Computers and Information, South Valley University, Qena, Egypt
| | - Christoph Linse
- Institut für Neuro- und Bioinformatik, University of Lübeck, Lübeck, Germany
| | - Mohamed Abdalla
- Mathematics Department, Faculty of Science, King Khalid University, Abha, Saudi Arabia
- Mathematics Department, Faculty of Science, South Valley University, Qena, Egypt
| | - Erhardt Barth
- Institut für Neuro- und Bioinformatik, University of Lübeck, Lübeck, Germany
| | - Thomas Martinetz
- Institut für Neuro- und Bioinformatik, University of Lübeck, Lübeck, Germany
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80
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Yousefi B, Kawakita S, Amini A, Akbari H, Advani SM, Akhloufi M, Maldague XPV, Ahadian S. Impartially Validated Multiple Deep-Chain Models to Detect COVID-19 in Chest X-ray Using Latent Space Radiomics. J Clin Med 2021; 10:3100. [PMID: 34300266 PMCID: PMC8304336 DOI: 10.3390/jcm10143100] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Revised: 07/01/2021] [Accepted: 07/07/2021] [Indexed: 12/31/2022] Open
Abstract
The COVID-19 pandemic continues to spread globally at a rapid pace, and its rapid detection remains a challenge due to its rapid infectivity and limited testing availability. One of the simply available imaging modalities in clinical routine involves chest X-ray (CXR), which is often used for diagnostic purposes. Here, we proposed a computer-aided detection of COVID-19 in CXR imaging using deep and conventional radiomic features. First, we used a 2D U-Net model to segment the lung lobes. Then, we extracted deep latent space radiomics by applying deep convolutional autoencoder (ConvAE) with internal dense layers to extract low-dimensional deep radiomics. We used Johnson-Lindenstrauss (JL) lemma, Laplacian scoring (LS), and principal component analysis (PCA) to reduce dimensionality in conventional radiomics. The generated low-dimensional deep and conventional radiomics were integrated to classify COVID-19 from pneumonia and healthy patients. We used 704 CXR images for training the entire model (i.e., U-Net, ConvAE, and feature selection in conventional radiomics). Afterward, we independently validated the whole system using a study cohort of 1597 cases. We trained and tested a random forest model for detecting COVID-19 cases through multivariate binary-class and multiclass classification. The maximal (full multivariate) model using a combination of the two radiomic groups yields performance in classification cross-validated accuracy of 72.6% (69.4-74.4%) for multiclass and 89.6% (88.4-90.7%) for binary-class classification.
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Affiliation(s)
- Bardia Yousefi
- Department of Electrical and Computer Engineering, Laval University, Quebec City, QC G1V 0A6, Canada
| | - Satoru Kawakita
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA 90024, USA; (S.K.); (S.M.A.)
| | - Arya Amini
- Department of Radiation Oncology, City of Hope Comprehensive Cancer Center, Duarte, CA 91010, USA;
| | - Hamed Akbari
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA;
| | - Shailesh M. Advani
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA 90024, USA; (S.K.); (S.M.A.)
| | - Moulay Akhloufi
- Department of Computer Science, Perception Robotics and Intelligent Machines (PRIME) Research Group, University of Moncton, New Brunswick, NB E1A 3E9, Canada;
| | - Xavier P. V. Maldague
- Department of Electrical and Computer Engineering, Laval University, Quebec City, QC G1V 0A6, Canada
| | - Samad Ahadian
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA 90024, USA; (S.K.); (S.M.A.)
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81
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Zhao W, Jiang W, Qiu X. Deep learning for COVID-19 detection based on CT images. Sci Rep 2021; 11:14353. [PMID: 34253822 PMCID: PMC8275612 DOI: 10.1038/s41598-021-93832-2] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 06/18/2021] [Indexed: 12/24/2022] Open
Abstract
COVID-19 has tremendously impacted patients and medical systems globally. Computed tomography images can effectively complement the reverse transcription-polymerase chain reaction testing. This study adopted a convolutional neural network for COVID-19 testing. We examined the performance of different pre-trained models on CT testing and identified that larger, out-of-field datasets boost the testing power of the models. This suggests that a priori knowledge of the models from out-of-field training is also applicable to CT images. The proposed transfer learning approach proves to be more successful than the current approaches described in literature. We believe that our approach has achieved the state-of-the-art performance in identification thus far. Based on experiments with randomly sampled training datasets, the results reveal a satisfactory performance by our model. We investigated the relevant visual characteristics of the CT images used by the model; these may assist clinical doctors in manual screening.
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Affiliation(s)
- Wentao Zhao
- College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, 310023, China
- School of Intelligent Transportation, Zhejiang Institute of Mechanical & Electrical Engineering, Hangzhou, 310053, China
| | - Wei Jiang
- College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, 310023, China
| | - Xinguo Qiu
- College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, 310023, China.
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82
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Shi W, Tong L, Zhu Y, Wang MD. COVID-19 Automatic Diagnosis With Radiographic Imaging: Explainable Attention Transfer Deep Neural Networks. IEEE J Biomed Health Inform 2021. [PMID: 33882010 DOI: 10.1109/jbhi.2021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
Researchers seek help from deep learning methods to alleviate the enormous burden of reading radiological images by clinicians during the COVID-19 pandemic. However, clinicians are often reluctant to trust deep models due to their black-box characteristics. To automatically differentiate COVID-19 and community-acquired pneumonia from healthy lungs in radiographic imaging, we propose an explainable attention-transfer classification model based on the knowledge distillation network structure. The attention transfer direction always goes from the teacher network to the student network. Firstly, the teacher network extracts global features and concentrates on the infection regions to generate attention maps. It uses a deformable attention module to strengthen the response of infection regions and to suppress noise in irrelevant regions with an expanded reception field. Secondly, an image fusion module combines attention knowledge transferred from teacher network to student network with the essential information in original input. While the teacher network focuses on global features, the student branch focuses on irregularly shaped lesion regions to learn discriminative features. Lastly, we conduct extensive experiments on public chest X-ray and CT datasets to demonstrate the explainability of the proposed architecture in diagnosing COVID-19.
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83
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Classification of COVID-19 chest X-Ray and CT images using a type of dynamic CNN modification method. Comput Biol Med 2021; 134:104425. [PMID: 33971427 PMCID: PMC8081579 DOI: 10.1016/j.compbiomed.2021.104425] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 04/17/2021] [Accepted: 04/17/2021] [Indexed: 12/16/2022]
Abstract
Understanding and classifying Chest X-Ray (CXR) and computerised tomography (CT) images are of great significance for COVID-19 diagnosis. The existing research on the classification for COVID-19 cases faces the challenges of data imbalance, insufficient generalisability, the lack of comparative study, etc. To address these problems, this paper proposes a type of modified MobileNet to classify COVID-19 CXR images and a modified ResNet architecture for CT image classification. In particular, a modification method of convolutional neural networks (CNN) is designed to solve the gradient vanishing problem and improve the classification performance through dynamically combining features in different layers of a CNN. The modified MobileNet is applied to the classification of COVID-19, Tuberculosis, viral pneumonia (with the exception of COVID-19), bacterial pneumonia and normal controls using CXR images. Also, the proposed modified ResNet is used for the classification of COVID-19, non-COVID-19 infections and normal controls using CT images. The results show that the proposed methods achieve 99.6% test accuracy on the five-category CXR image dataset and 99.3% test accuracy on the CT image dataset. Six advanced CNN architectures and two specific COVID-19 detection models, i.e., COVID-Net and COVIDNet-CT are used in comparative studies. Two benchmark datasets and a CXR image dataset which combines eight different CXR image sources are employed to evaluate the performance of the above models. The results show that the proposed methods outperform the comparative models in classification accuracy, sensitivity, and precision, which demonstrate their potential in computer-aided diagnosis for healthcare applications.
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84
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Tandel GS, Tiwari A, Kakde OG. Performance optimisation of deep learning models using majority voting algorithm for brain tumour classification. Comput Biol Med 2021; 135:104564. [PMID: 34217980 DOI: 10.1016/j.compbiomed.2021.104564] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Revised: 06/08/2021] [Accepted: 06/08/2021] [Indexed: 12/18/2022]
Abstract
BACKGROUND Although biopsy is the gold standard for tumour grading, being invasive, this procedure also proves fatal to the brain. Thus, non-invasive methods for brain tumour grading are urgently needed. Here, a magnetic resonance imaging (MRI)-based non-invasive brain tumour grading method has been proposed using deep learning (DL) and machine learning (ML) techniques. METHOD Four clinically applicable datasets were designed. The four datasets were trained and tested on five DL-based models (convolutional neural networks), AlexNet, VGG16, ResNet18, GoogleNet, and ResNet50, and five ML-based models, Support Vector Machine, K-Nearest Neighbours, Naïve Bayes, Decision Tree, and Linear Discrimination using five-fold cross-validation. A majority voting (MajVot)-based ensemble algorithm has been proposed to optimise the overall classification performance of five DL and five ML-based models. RESULTS The average accuracy improvement of four datasets using the DL-based MajVot algorithm against AlexNet, VGG16, ResNet18, GoogleNet, and ResNet50 models was 2.02%, 1.11%, 1.04%, 2.67%, and 1.65%, respectively. Further, a 10.12% improvement was seen in the average accuracy of four datasets using the DL method against ML. Furthermore, the proposed DL-based MajVot algorithm was validated on synthetic face data and improved the male versus female face image classification accuracy by 2.88%, 0.71%, 1.90%, 2.24%, and 0.35% against AlexNet, VGG16, ResNet18, GoogleNet, and ResNet50, respectively. CONCLUSION The proposed MajVot algorithm achieved promising results for brain tumour classification and is able to utilise the combined potential of multiple models.
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Affiliation(s)
- Gopal S Tandel
- Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology, Nagpur, 440010, India.
| | - Ashish Tiwari
- Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology, Nagpur, 440010, India.
| | - O G Kakde
- Indian Institute of Information Technology, Nagpur, 440006, India.
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85
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Deep supervised learning using self-adaptive auxiliary loss for COVID-19 diagnosis from imbalanced CT images. Neurocomputing 2021; 458:232-245. [PMID: 34121811 PMCID: PMC8180474 DOI: 10.1016/j.neucom.2021.06.012] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 05/02/2021] [Accepted: 06/04/2021] [Indexed: 12/21/2022]
Abstract
The outbreak and rapid spread of coronavirus disease 2019 (COVID-19) has had a huge impact on the lives and safety of people around the world. Chest CT is considered an effective tool for the diagnosis and follow-up of COVID-19. For faster examination, automatic COVID-19 diagnostic techniques using deep learning on CT images have received increasing attention. However, the number and category of existing datasets for COVID-19 diagnosis that can be used for training are limited, and the number of initial COVID-19 samples is much smaller than the normal’s, which leads to the problem of class imbalance. It makes the classification algorithms difficult to learn the discriminative boundaries since the data of some classes are rich while others are scarce. Therefore, training robust deep neural networks with imbalanced data is a fundamental challenging but important task in the diagnosis of COVID-19. In this paper, we create a challenging clinical dataset (named COVID19-Diag) with category diversity and propose a novel imbalanced data classification method using deep supervised learning with a self-adaptive auxiliary loss (DSN-SAAL) for COVID-19 diagnosis. The loss function considers both the effects of data overlap between CT slices and possible noisy labels in clinical datasets on a multi-scale, deep supervised network framework by integrating the effective number of samples and a weighting regularization item. The learning process jointly and automatically optimizes all parameters over the deep supervised network, making our model generally applicable to a wide range of datasets. Extensive experiments are conducted on COVID19-Diag and three public COVID-19 diagnosis datasets. The results show that our DSN-SAAL outperforms the state-of-the-art methods and is effective for the diagnosis of COVID-19 in varying degrees of data imbalance.
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86
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Sait U, K V GL, Shivakumar S, Kumar T, Bhaumik R, Prajapati S, Bhalla K, Chakrapani A. A deep-learning based multimodal system for Covid-19 diagnosis using breathing sounds and chest X-ray images. Appl Soft Comput 2021; 109:107522. [PMID: 34054379 PMCID: PMC8149173 DOI: 10.1016/j.asoc.2021.107522] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Revised: 04/20/2021] [Accepted: 05/21/2021] [Indexed: 12/23/2022]
Abstract
Covid-19 has become a deadly pandemic claiming more than three million lives worldwide. SARS-CoV-2 causes distinct pathomorphological alterations in the respiratory system, thereby acting as a biomarker to aid its diagnosis. A multimodal framework (Ai-CovScan) for Covid-19 detection using breathing sounds, chest X-ray (CXR) images, and rapid antigen test (RAnT) is proposed. Transfer Learning approach using existing deep-learning Convolutional Neural Network (CNN) based on Inception-v3 is combined with Multi-Layered Perceptron (MLP) to develop the CovScanNet model for reducing false-negatives. This model reports a preliminary accuracy of 80% for the breathing sound analysis, and 99.66% Covid-19 detection accuracy for the curated CXR image dataset. Based on Ai-CovScan, a smartphone app is conceptualised as a mass-deployable screening tool, which could alter the course of this pandemic. This app’s deployment could minimise the number of people accessing the limited and expensive confirmatory tests, thereby reducing the burden on the severely stressed healthcare infrastructure.
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Affiliation(s)
- Unais Sait
- Faculty of Architecture and Design, PES University, Bengaluru, India
| | - Gokul Lal K V
- East Point College of Engineering and Technology, Bengaluru, India
| | - Sanjana Shivakumar
- Department of Design and Computation Arts, Concordia University, Qc, Canada
| | - Tarun Kumar
- Centre for Product Design and Manufacturing, Indian Institute of Science, Bengaluru, India
| | - Rahul Bhaumik
- Faculty of Architecture and Design, PES University, Bengaluru, India
| | - Sunny Prajapati
- Faculty of Architecture and Design, PES University, Bengaluru, India
| | - Kriti Bhalla
- School of Architecture, Ramaiah Institute of Technology, Bengaluru, Karnataka, India
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87
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Wong A, Lin ZQ, Wang L, Chung AG, Shen B, Abbasi A, Hoshmand-Kochi M, Duong TQ. Towards computer-aided severity assessment via deep neural networks for geographic and opacity extent scoring of SARS-CoV-2 chest X-rays. Sci Rep 2021; 11:9315. [PMID: 33927239 PMCID: PMC8085167 DOI: 10.1038/s41598-021-88538-4] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 04/13/2021] [Indexed: 01/08/2023] Open
Abstract
A critical step in effective care and treatment planning for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause for the coronavirus disease 2019 (COVID-19) pandemic, is the assessment of the severity of disease progression. Chest x-rays (CXRs) are often used to assess SARS-CoV-2 severity, with two important assessment metrics being extent of lung involvement and degree of opacity. In this proof-of-concept study, we assess the feasibility of computer-aided scoring of CXRs of SARS-CoV-2 lung disease severity using a deep learning system. Data consisted of 396 CXRs from SARS-CoV-2 positive patient cases. Geographic extent and opacity extent were scored by two board-certified expert chest radiologists (with 20+ years of experience) and a 2nd-year radiology resident. The deep neural networks used in this study, which we name COVID-Net S, are based on a COVID-Net network architecture. 100 versions of the network were independently learned (50 to perform geographic extent scoring and 50 to perform opacity extent scoring) using random subsets of CXRs from the study, and we evaluated the networks using stratified Monte Carlo cross-validation experiments. The COVID-Net S deep neural networks yielded R[Formula: see text] of [Formula: see text] and [Formula: see text] between predicted scores and radiologist scores for geographic extent and opacity extent, respectively, in stratified Monte Carlo cross-validation experiments. The best performing COVID-Net S networks achieved R[Formula: see text] of 0.739 and 0.741 between predicted scores and radiologist scores for geographic extent and opacity extent, respectively. The results are promising and suggest that the use of deep neural networks on CXRs could be an effective tool for computer-aided assessment of SARS-CoV-2 lung disease severity, although additional studies are needed before adoption for routine clinical use.
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Affiliation(s)
- A Wong
- Systems Design Engineering, University of Waterloo, Waterloo, Canada.
- DarwinAI Corp., Waterloo, Canada.
| | - Z Q Lin
- Systems Design Engineering, University of Waterloo, Waterloo, Canada.
- DarwinAI Corp., Waterloo, Canada.
| | - L Wang
- Systems Design Engineering, University of Waterloo, Waterloo, Canada
- DarwinAI Corp., Waterloo, Canada
| | | | - B Shen
- Department of Radiology, Stony Brook School of Medicine, Stony Brook, NY, USA
| | - A Abbasi
- Department of Radiology, Stony Brook School of Medicine, Stony Brook, NY, USA
| | - M Hoshmand-Kochi
- Department of Radiology, Stony Brook School of Medicine, Stony Brook, NY, USA
| | - T Q Duong
- Department of Radiology, Stony Brook School of Medicine, Stony Brook, NY, USA
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88
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Suri JS, Agarwal S, Gupta SK, Puvvula A, Biswas M, Saba L, Bit A, Tandel GS, Agarwal M, Patrick A, Faa G, Singh IM, Oberleitner R, Turk M, Chadha PS, Johri AM, Miguel Sanches J, Khanna NN, Viskovic K, Mavrogeni S, Laird JR, Pareek G, Miner M, Sobel DW, Balestrieri A, Sfikakis PP, Tsoulfas G, Protogerou A, Misra DP, Agarwal V, Kitas GD, Ahluwalia P, Teji J, Al-Maini M, Dhanjil SK, Sockalingam M, Saxena A, Nicolaides A, Sharma A, Rathore V, Ajuluchukwu JNA, Fatemi M, Alizad A, Viswanathan V, Krishnan PK, Naidu S. A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence. Comput Biol Med 2021; 130:104210. [PMID: 33550068 PMCID: PMC7813499 DOI: 10.1016/j.compbiomed.2021.104210] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 01/03/2021] [Accepted: 01/03/2021] [Indexed: 02/06/2023]
Abstract
COVID-19 has infected 77.4 million people worldwide and has caused 1.7 million fatalities as of December 21, 2020. The primary cause of death due to COVID-19 is Acute Respiratory Distress Syndrome (ARDS). According to the World Health Organization (WHO), people who are at least 60 years old or have comorbidities that have primarily been targeted are at the highest risk from SARS-CoV-2. Medical imaging provides a non-invasive, touch-free, and relatively safer alternative tool for diagnosis during the current ongoing pandemic. Artificial intelligence (AI) scientists are developing several intelligent computer-aided diagnosis (CAD) tools in multiple imaging modalities, i.e., lung computed tomography (CT), chest X-rays, and lung ultrasounds. These AI tools assist the pulmonary and critical care clinicians through (a) faster detection of the presence of a virus, (b) classifying pneumonia types, and (c) measuring the severity of viral damage in COVID-19-infected patients. Thus, it is of the utmost importance to fully understand the requirements of for a fast and successful, and timely lung scans analysis. This narrative review first presents the pathological layout of the lungs in the COVID-19 scenario, followed by understanding and then explains the comorbid statistical distributions in the ARDS framework. The novelty of this review is the approach to classifying the AI models as per the by school of thought (SoTs), exhibiting based on segregation of techniques and their characteristics. The study also discusses the identification of AI models and its extension from non-ARDS lungs (pre-COVID-19) to ARDS lungs (post-COVID-19). Furthermore, it also presents AI workflow considerations of for medical imaging modalities in the COVID-19 framework. Finally, clinical AI design considerations will be discussed. We conclude that the design of the current existing AI models can be improved by considering comorbidity as an independent factor. Furthermore, ARDS post-processing clinical systems must involve include (i) the clinical validation and verification of AI-models, (ii) reliability and stability criteria, and (iii) easily adaptable, and (iv) generalization assessments of AI systems for their use in pulmonary, critical care, and radiological settings.
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Affiliation(s)
- Jasjit S Suri
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA, USA.
| | - Sushant Agarwal
- Advanced Knowledge Engineering Centre, GBTI, Roseville, CA, USA; Department of Computer Science Engineering, PSIT, Kanpur, India
| | - Suneet K Gupta
- Department of Computer Science Engineering, Bennett University, India
| | - Anudeep Puvvula
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA, USA; Annu's Hospitals for Skin and Diabetes, Nellore, AP, India
| | - Mainak Biswas
- Department of Computer Science Engineering, JIS University, Kolkata, India
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria, Cagliari, Italy
| | - Arindam Bit
- Department of Biomedical Engineering, NIT, Raipur, India
| | - Gopal S Tandel
- Department of Computer Science Engineering, VNIT, Nagpur, India
| | - Mohit Agarwal
- Department of Computer Science Engineering, Bennett University, India
| | | | - Gavino Faa
- Department of Pathology - AOU of Cagliari, Italy
| | - Inder M Singh
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA, USA
| | | | - Monika Turk
- The Hanse-Wissenschaftskolleg Institute for Advanced Study, Delmenhorst, Germany
| | - Paramjit S Chadha
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA, USA
| | - Amer M Johri
- Department of Medicine, Division of Cardiology, Queen's University, Kingston, Ontario, Canada
| | - J Miguel Sanches
- Institute of Systems and Robotics, Instituto Superior Tecnico, Lisboa, Portugal
| | - Narendra N Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India
| | | | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Center, Athens, Greece
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA, USA
| | - Gyan Pareek
- Minimally Invasive Urology Institute, Brown University, Providence, RI, USA
| | - Martin Miner
- Men's Health Center, Miriam Hospital Providence, Rhode Island, USA
| | - David W Sobel
- Minimally Invasive Urology Institute, Brown University, Providence, RI, USA
| | | | - Petros P Sfikakis
- Rheumatology Unit, National Kapodistrian University of Athens, Greece
| | - George Tsoulfas
- Aristoteleion University of Thessaloniki, Thessaloniki, Greece
| | | | | | - Vikas Agarwal
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley, UK
| | - George D Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley, UK; Arthritis Research UK Epidemiology Unit, Manchester University, Manchester, UK
| | - Puneet Ahluwalia
- Max Institute of Cancer Care, Max Superspeciality Hospital, New Delhi, India
| | - Jagjit Teji
- Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, USA
| | - Mustafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, Canada
| | | | | | - Ajit Saxena
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre and University of Nicosia Medical School, Cyprus
| | - Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA, USA
| | - Vijay Rathore
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA, USA
| | | | - Mostafa Fatemi
- Dept. of Physiology & Biomedical Engg., Mayo Clinic College of Medicine and Science, MN, USA
| | - Azra Alizad
- Dept. of Radiology, Mayo Clinic College of Medicine and Science, MN, USA
| | - Vijay Viswanathan
- MV Hospital for Diabetes and Professor M Viswanathan Diabetes Research Centre, Chennai, India
| | - P K Krishnan
- Neurology Department, Fortis Hospital, Bangalore, India
| | - Subbaram Naidu
- Electrical Engineering Department, University of Minnesota, Duluth, MN, USA
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89
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Does Two-Class Training Extract Real Features? A COVID-19 Case Study. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11041424] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Diagnosis aid systems that use image analysis are currently very useful due to the large workload of health professionals involved in making diagnoses. In recent years, Convolutional Neural Networks (CNNs) have been used to help in these tasks. For this reason, multiple studies that analyze the detection precision for several diseases have been developed. However, many of these works distinguish between only two classes: healthy and with a specific disease. Based on this premise, in this work, we try to answer the questions: When training an image classification system with only two classes (healthy and sick), does this system extract the specific features of this disease, or does it only obtain the features that differentiate it from a healthy patient? Trying to answer these questions, we analyze the particular case of COVID-19 detection. Many works that classify this disease using X-ray images have been published; some of them use two classes (with and without COVID-19), while others include more classes (pneumonia, SARS, influenza, etc.). In this work, we carry out several classification studies with two classes, using test images that do not belong to those classes, in order to try to answer the previous questions. The first studies indicate problems in these two-class systems when using a third class as a test, being classified inconsistently. Deeper studies show that deep learning systems trained with two classes do not correctly extract the characteristics of pathologies, but rather differentiate the classes based on the physical characteristics of the images. After the discussion, we conclude that these two-class trained deep learning systems are not valid if there are other diseases that cause similar symptoms.
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90
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Podder S, Bhattacharjee S, Roy A. An efficient method of detection of COVID-19 using Mask R-CNN on chest X-Ray images. AIMS BIOPHYSICS 2021. [DOI: 10.3934/biophy.2021022] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
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