101
|
Peng Y, Zhang T, Guo Y. Cov-TransNet: Dual branch fusion network with transformer for COVID-19 infection segmentation. Biomed Signal Process Control 2022; 80:104366. [PMCID: PMC9671472 DOI: 10.1016/j.bspc.2022.104366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 09/06/2022] [Accepted: 10/30/2022] [Indexed: 11/09/2022]
Abstract
Segmentation of COVID-19 infection is a challenging task due to the blurred boundaries and low contrast between the infected and the non-infected areas in COVID-19 CT images, especially for small infection regions. COV-TransNet is presented to achieve high-precision segmentation of COVID-19 infection regions in this paper. The proposed segmentation network is composed of the auxiliary branch and the backbone branch. The auxiliary branch network adopts transformer to provide global information, helping the convolution layers in backbone branch to learn specific local features better. A multi-scale feature attention module is introduced to capture contextual information and adaptively enhance feature representations. Specially, a high internal resolution is maintained during the attention calculation process. Moreover, feature activation module can effectively reduce the loss of valid information during sampling. The proposed network can take full advantage of different depth and multi-scale features to achieve high sensitivity for identifying lesions of varied sizes and locations. We experiment on several datasets of the COVID-19 lesion segmentation task, including COVID-19-CT-Seg, UESTC-COVID-19, MosMedData and COVID-19-MedSeg. Comprehensive results demonstrate that COV-TransNet outperforms the existing state-of-the-art segmentation methods and achieves better segmentation performance for multi-scale lesions.
Collapse
|
102
|
Wan L, Ai Z, Chen J, Jiang Q, Chen H, Li Q, Lu Y, Chen L. Detection algorithm for pigmented skin disease based on classifier-level and feature-level fusion. Front Public Health 2022; 10:1034772. [PMID: 36339204 PMCID: PMC9632750 DOI: 10.3389/fpubh.2022.1034772] [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: 09/02/2022] [Accepted: 09/30/2022] [Indexed: 01/29/2023] Open
Abstract
Pigmented skin disease is caused by abnormal melanocyte and melanin production, which can be induced by genetic and environmental factors. It is also common among the various types of skin diseases. The timely and accurate diagnosis of pigmented skin disease is important for reducing mortality. Patients with pigmented dermatosis are generally diagnosed by a dermatologist through dermatoscopy. However, due to the current shortage of experts, this approach cannot meet the needs of the population, so a computer-aided system would help to diagnose skin lesions in remote areas containing insufficient experts. This paper proposes an algorithm based on a fusion network for the detection of pigmented skin disease. First, we preprocess the images in the acquired dataset, and then we perform image flipping and image style transfer to augment the images to alleviate the imbalance between the various categories in the dataset. Finally, two feature-level fusion optimization schemes based on deep features are compared with a classifier-level fusion scheme based on a classification layer to effectively determine the best fusion strategy for satisfying the pigmented skin disease detection requirements. Gradient-weighted Class Activation Mapping (Grad_CAM) and Grad_CAM++ are used for visualization purposes to verify the effectiveness of the proposed fusion network. The results show that compared with those of the traditional detection algorithm for pigmented skin disease, the accuracy and Area Under Curve (AUC) of the method in this paper reach 92.1 and 95.3%, respectively. The evaluation indices are greatly improved, proving the adaptability and accuracy of the proposed method. The proposed method can assist clinicians in screening and diagnosing pigmented skin disease and is suitable for real-world applications.
Collapse
Affiliation(s)
- Li Wan
- Dermatology Department, Wuhan No.1 Hospital, Hubei, China,Dermatology Hospital of Southern Medical University, Guangzhou, China
| | - Zhuang Ai
- Department of Research and Development, Sinopharm Genomics Technology Co., Ltd., Jiangsu, China
| | - Jinbo Chen
- Dermatology Department, Wuhan No.1 Hospital, Hubei, China
| | - Qian Jiang
- Dermatology Department, Wuhan No.1 Hospital, Hubei, China
| | - Hongying Chen
- Dermatology Department, Wuhan No.1 Hospital, Hubei, China
| | - Qi Li
- Department of Research and Development, Sinopharm Genomics Technology Co., Ltd., Jiangsu, China
| | - Yaping Lu
- Department of Research and Development, Sinopharm Genomics Technology Co., Ltd., Jiangsu, China,*Correspondence: Yaping Lu
| | - Liuqing Chen
- Dermatology Department, Wuhan No.1 Hospital, Hubei, China,Liuqing Chen
| |
Collapse
|
103
|
Naseem MT, Hussain T, Lee CS, Khan MA. Classification and Detection of COVID-19 and Other Chest-Related Diseases Using Transfer Learning. SENSORS (BASEL, SWITZERLAND) 2022; 22:7977. [PMID: 36298328 PMCID: PMC9610066 DOI: 10.3390/s22207977] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 09/17/2022] [Accepted: 10/14/2022] [Indexed: 06/16/2023]
Abstract
COVID-19 has infected millions of people worldwide over the past few years. The main technique used for COVID-19 detection is reverse transcription, which is expensive, sensitive, and requires medical expertise. X-ray imaging is an alternative and more accessible technique. This study aimed to improve detection accuracy to create a computer-aided diagnostic tool. Combining other artificial intelligence applications techniques with radiological imaging can help detect different diseases. This study proposes a technique for the automatic detection of COVID-19 and other chest-related diseases using digital chest X-ray images of suspected patients by applying transfer learning (TL) algorithms. For this purpose, two balanced datasets, Dataset-1 and Dataset-2, were created by combining four public databases and collecting images from recently published articles. Dataset-1 consisted of 6000 chest X-ray images with 1500 for each class. Dataset-2 consisted of 7200 images with 1200 for each class. To train and test the model, TL with nine pretrained convolutional neural networks (CNNs) was used with augmentation as a preprocessing method. The network was trained to classify using five classifiers: two-class classifier (normal and COVID-19); three-class classifier (normal, COVID-19, and viral pneumonia), four-class classifier (normal, viral pneumonia, COVID-19, and tuberculosis (Tb)), five-class classifier (normal, bacterial pneumonia, COVID-19, Tb, and pneumothorax), and six-class classifier (normal, bacterial pneumonia, COVID-19, viral pneumonia, Tb, and pneumothorax). For two, three, four, five, and six classes, our model achieved a maximum accuracy of 99.83, 98.11, 97.00, 94.66, and 87.29%, respectively.
Collapse
Affiliation(s)
- Muhammad Tahir Naseem
- Department of Electronic Engineering, Yeungnam University, Gyeongsan 38541, Korea
- Riphah School of Computing & Applied Sciences (RSCI), Riphah International University, Lahore 55150, Pakistan
| | - Tajmal Hussain
- Riphah School of Computing & Applied Sciences (RSCI), Riphah International University, Lahore 55150, Pakistan
| | - Chan-Su Lee
- Department of Electronic Engineering, Yeungnam University, Gyeongsan 38541, Korea
| | - Muhammad Adnan Khan
- Riphah School of Computing & Applied Sciences (RSCI), Riphah International University, Lahore 55150, Pakistan
| |
Collapse
|
104
|
Bhattacharjya U, Sarma KK, Medhi JP, Choudhury BK, Barman G. Automated diagnosis of COVID-19 using radiological modalities and Artificial Intelligence functionalities: A retrospective study based on chest HRCT database. Biomed Signal Process Control 2022; 80:104297. [PMID: 36275840 PMCID: PMC9576693 DOI: 10.1016/j.bspc.2022.104297] [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: 06/03/2022] [Revised: 09/12/2022] [Accepted: 10/08/2022] [Indexed: 11/16/2022]
Abstract
Background and Objective : The spread of coronavirus has been challenging for the healthcare system's proper management and diagnosis during the rapid spread and control of the infection. Real-time reverse transcription-polymerase chain reaction (RT-PCR), though considered the standard testing measure, has low sensitivity and is time-consuming, which restricts the fast screening of individuals. Therefore, computer tomography (CT) is used to complement the traditional approaches and provide fast and effective screening over other diagnostic methods. This work aims to appraise the importance of chest CT findings of COVID-19 and post-COVID in the diagnosis and prognosis of infected patients and to explore the ways and means to integrate CT findings for the development of advanced Artificial Intelligence (AI) tool-based predictive diagnostic techniques. Methods : The retrospective study includes a 188 patient database with COVID-19 infection confirmed by RT-PCR testing, including post-COVID patients. Patients underwent chest high-resolution computer tomography (HRCT), where the images were evaluated for common COVID-19 findings and involvement of the lung and its lobes based on the coverage region. The radiological modalities analyzed in this study may help the researchers in generating a predictive model based on AI tools for further classification with a high degree of reliability. Results : Mild to moderate ground glass opacities (GGO) with or without consolidation, crazy paving patterns, and halo signs were common COVID-19 related findings. A CT score is assigned to every patient based on the severity of lung lobe involvement. Conclusion : Typical multifocal, bilateral, and peripheral distributions of GGO are the main characteristics related to COVID-19 pneumonia. Chest HRCT can be considered a standard method for timely and efficient assessment of disease progression and management severity. With its fusion with AI tools, chest HRCT can be used as a one-stop platform for radiological investigation and automated diagnosis system.
Collapse
Affiliation(s)
- Upasana Bhattacharjya
- Department of Electronics and Communication Engineering Gauhati University, Guwahati 781014, Assam, India,Corresponding author
| | - Kandarpa Kumar Sarma
- Department of Electronics and Communication Engineering Gauhati University, Guwahati 781014, Assam, India
| | - Jyoti Prakash Medhi
- Department of Electronics and Communication Engineering Gauhati University, Guwahati 781014, Assam, India
| | - Binoy Kumar Choudhury
- Department of Radio Diagnosis and Imaging, Dr. Bhubaneswar Borooah Cancer Institute, Guwahati, Assam, India
| | - Geetanjali Barman
- Department of Radio Diagnosis and Imaging, Dr. Bhubaneswar Borooah Cancer Institute, Guwahati, Assam, India
| |
Collapse
|
105
|
Guhan B, Almutairi L, Sowmiya S, Snekhalatha U, Rajalakshmi T, Aslam SM. Automated system for classification of COVID-19 infection from lung CT images based on machine learning and deep learning techniques. Sci Rep 2022; 12:17417. [PMID: 36257964 PMCID: PMC9579174 DOI: 10.1038/s41598-022-20804-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Accepted: 09/19/2022] [Indexed: 01/12/2023] Open
Abstract
The objectives of our proposed study were as follows: First objective is to segment the CT images using a k-means clustering algorithm for extracting the region of interest and to extract textural features using gray level co-occurrence matrix (GLCM). Second objective is to implement machine learning classifiers such as Naïve bayes, bagging and Reptree to classify the images into two image classes namely COVID and non-COVID and to compare the performance of the three pre-trained CNN models such as AlexNet, ResNet50 and SqueezeNet with that of the proposed machine learning classifiers. Our dataset consists of 100 COVID and non-COVID images which are pre-processed and segmented with our proposed algorithm. Following the feature extraction process, three machine learning classifiers (Naive Bayes, Bagging, and REPTree) were used to classify the normal and covid patients. We had implemented the three pre-trained CNN models such as AlexNet, ResNet50 and SqueezeNet for comparing their performance with machine learning classifiers. In machine learning, the Naive Bayes classifier achieved the highest accuracy of 97%, whereas the ResNet50 CNN model attained the highest accuracy of 99%. Hence the deep learning networks outperformed well compared to the machine learning techniques in the classification of Covid-19 images.
Collapse
Affiliation(s)
- Bhargavee Guhan
- grid.412742.60000 0004 0635 5080Department of Biomedical Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu 603203 India
| | - Laila Almutairi
- grid.449051.d0000 0004 0441 5633Department of Computer Engineering, College of Computer and Information Sciences, Majmaah University, Al Majmaah, 11952 Saudi Arabia
| | - S. Sowmiya
- grid.412742.60000 0004 0635 5080Department of Biomedical Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu 603203 India
| | - U. Snekhalatha
- grid.412742.60000 0004 0635 5080Department of Biomedical Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu 603203 India
| | - T. Rajalakshmi
- grid.412742.60000 0004 0635 5080Department of Electronics and Communication Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, India
| | - Shabnam Mohamed Aslam
- grid.449051.d0000 0004 0441 5633Department of Information Technology, College of Computer and Information Sciences, Majmaah University, Al Majmaah, 11952 Saudi Arabia
| |
Collapse
|
106
|
Deep Transfer Learning for COVID-19 Detection and Lesion Recognition Using Chest CT Images. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:4509394. [PMID: 36285284 PMCID: PMC9588382 DOI: 10.1155/2022/4509394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 09/11/2022] [Accepted: 09/24/2022] [Indexed: 12/15/2022]
Abstract
Starting from December 2019, the global pandemic of coronavirus disease 2019 (COVID-19) is continuously expanding and has caused several millions of deaths worldwide. Fast and accurate diagnostic methods for COVID-19 detection play a vital role in containing the plague. Chest computed tomography (CT) is one of the most commonly used diagnosis methods. However, a complete CT-scan has hundreds of slices, and it is time-consuming for radiologists to check each slice to diagnose COVID-19. This study introduces a novel method for fast and automated COVID-19 diagnosis using the chest CT scans. The proposed models are based on the state-of-the-art deep convolutional neural network (CNN) architecture, and a 2D global max pooling (globalMaxPool2D) layer is used to improve the performance. We compare the proposed models to the existing state-of-the-art deep learning models such as CNN based models and vision transformer (ViT) models. Based off of metric such as area under curve (AUC), sensitivity, specificity, accuracy, and false discovery rate (FDR), experimental results show that the proposed models outperform the previous methods, and the best model achieves an area under curve of 0.9744 and accuracy 94.12% on our test datasets. It is also shown that the accuracy is improved by around 1% by using the 2D global max pooling layer. Moreover, a heatmap method to highlight the lesion area on COVID-19 chest CT images is introduced in the paper. This heatmap method is helpful for a radiologist to identify the abnormal pattern of COVID-19 on chest CT images. In addition, we also developed a freely accessible online simulation software for automated COVID-19 detection using CT images. The proposed deep learning models and software tool can be used by radiologist to diagnose COVID-19 more accurately and efficiently.
Collapse
|
107
|
Sinwar D, Dhaka VS, Tesfaye BA, Raghuwanshi G, Kumar A, Maakar SK, Agrawal S. Artificial Intelligence and Deep Learning Assisted Rapid Diagnosis of COVID-19 from Chest Radiographical Images: A Survey. CONTRAST MEDIA & MOLECULAR IMAGING 2022; 2022:1306664. [PMID: 36304775 PMCID: PMC9581633 DOI: 10.1155/2022/1306664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 09/06/2022] [Accepted: 09/27/2022] [Indexed: 01/26/2023]
Abstract
Artificial Intelligence (AI) has been applied successfully in many real-life domains for solving complex problems. With the invention of Machine Learning (ML) paradigms, it becomes convenient for researchers to predict the outcome based on past data. Nowadays, ML is acting as the biggest weapon against the COVID-19 pandemic by detecting symptomatic cases at an early stage and warning people about its futuristic effects. It is observed that COVID-19 has blown out globally so much in a short period because of the shortage of testing facilities and delays in test reports. To address this challenge, AI can be effectively applied to produce fast as well as cost-effective solutions. Plenty of researchers come up with AI-based solutions for preliminary diagnosis using chest CT Images, respiratory sound analysis, voice analysis of symptomatic persons with asymptomatic ones, and so forth. Some AI-based applications claim good accuracy in predicting the chances of being COVID-19-positive. Within a short period, plenty of research work is published regarding the identification of COVID-19. This paper has carefully examined and presented a comprehensive survey of more than 110 papers that came from various reputed sources, that is, Springer, IEEE, Elsevier, MDPI, arXiv, and medRxiv. Most of the papers selected for this survey presented candid work to detect and classify COVID-19, using deep-learning-based models from chest X-Rays and CT scan images. We hope that this survey covers most of the work and provides insights to the research community in proposing efficient as well as accurate solutions for fighting the pandemic.
Collapse
Affiliation(s)
- Deepak Sinwar
- Department of Computer and Communication Engineering, Manipal University Jaipur, Jaipur, India
| | - Vijaypal Singh Dhaka
- Department of Computer and Communication Engineering, Manipal University Jaipur, Jaipur, India
| | - Biniyam Alemu Tesfaye
- Department of Computer Science, College of Informatics, Bule Hora University, Bule Hora, Ethiopia
| | - Ghanshyam Raghuwanshi
- Department of Computer and Communication Engineering, Manipal University Jaipur, Jaipur, India
| | - Ashish Kumar
- Department of Mathematics and Statistics, Manipal University Jaipur, Jaipur, India
| | - Sunil Kr. Maakar
- School of Computing Science & Engineering, Galgotias University, Greater Noida, India
| | - Sanjay Agrawal
- Department of Electrical Engineering, Rajkiya Engineering College, Akbarpur, Ambedkar Nagar, India
| |
Collapse
|
108
|
Karthik R, Menaka R, Hariharan M, Kathiresan GS. AI for COVID-19 Detection from Radiographs: Incisive Analysis of State of the Art Techniques, Key Challenges and Future Directions. Ing Rech Biomed 2022; 43:486-510. [PMID: 34336141 PMCID: PMC8312058 DOI: 10.1016/j.irbm.2021.07.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 06/14/2021] [Accepted: 07/19/2021] [Indexed: 12/24/2022]
Abstract
Background and objective In recent years, Artificial Intelligence has had an evident impact on the way research addresses challenges in different domains. It has proven to be a huge asset, especially in the medical field, allowing for time-efficient and reliable solutions. This research aims to spotlight the impact of deep learning and machine learning models in the detection of COVID-19 from medical images. This is achieved by conducting a review of the state-of-the-art approaches proposed by the recent works in this field. Methods The main focus of this study is the recent developments of classification and segmentation approaches to image-based COVID-19 detection. The study reviews 140 research papers published in different academic research databases. These papers have been screened and filtered based on specified criteria, to acquire insights prudent to image-based COVID-19 detection. Results The methods discussed in this review include different types of imaging modality, predominantly X-rays and CT scans. These modalities are used for classification and segmentation tasks as well. This review seeks to categorize and discuss the different deep learning and machine learning architectures employed for these tasks, based on the imaging modality utilized. It also hints at other possible deep learning and machine learning architectures that can be proposed for better results towards COVID-19 detection. Along with that, a detailed overview of the emerging trends and breakthroughs in Artificial Intelligence-based COVID-19 detection has been discussed as well. Conclusion This work concludes by stipulating the technical and non-technical challenges faced by researchers and illustrates the advantages of image-based COVID-19 detection with Artificial Intelligence techniques.
Collapse
Affiliation(s)
- R Karthik
- Centre for Cyber Physical Systems, Vellore Institute of Technology, Chennai, India
| | - R Menaka
- Centre for Cyber Physical Systems, Vellore Institute of Technology, Chennai, India
| | - M Hariharan
- School of Computing Sciences and Engineering, Vellore Institute of Technology, Chennai, India
| | - G S Kathiresan
- School of Electronics Engineering, Vellore Institute of Technology, Chennai, India
| |
Collapse
|
109
|
de Laat-Kremers R, De Jongh R, Ninivaggi M, Fiolet A, Fijnheer R, Remijn J, de Laat B. Coagulation parameters predict COVID-19-related thrombosis in a neural network with a positive predictive value of 98%. Front Immunol 2022; 13:977443. [PMID: 36248875 PMCID: PMC9554597 DOI: 10.3389/fimmu.2022.977443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 08/30/2022] [Indexed: 01/08/2023] Open
Abstract
Thrombosis is a major clinical complication of COVID-19 infection. COVID-19 patients show changes in coagulation factors that indicate an important role for the coagulation system in the pathogenesis of COVID-19. However, the multifactorial nature of thrombosis complicates the prediction of thrombotic events based on a single hemostatic variable. We developed and validated a neural net for the prediction of COVID-19-related thrombosis. The neural net was developed based on the hemostatic and general (laboratory) variables of 149 confirmed COVID-19 patients from two cohorts: at the time of hospital admission (cohort 1 including 133 patients) and at ICU admission (cohort 2 including 16 patients). Twenty-six patients suffered from thrombosis during their hospital stay: 19 patients in cohort 1 and 7 patients in cohort 2. The neural net predicts COVID-19 related thrombosis based on C-reactive protein (relative importance 14%), sex (10%), thrombin generation (TG) time-to-tail (10%), α2-Macroglobulin (9%), TG curve width (9%), thrombin-α2-Macroglobulin complexes (9%), plasmin generation lag time (8%), serum IgM (8%), TG lag time (7%), TG time-to-peak (7%), thrombin-antithrombin complexes (5%), and age (5%). This neural net can predict COVID-19-thrombosis at the time of hospital admission with a positive predictive value of 98%-100%.
Collapse
Affiliation(s)
- Romy de Laat-Kremers
- Department of Data Analysis and Artificial Intelligence, Synapse Research Institute, Maastricht, Netherlands
- *Correspondence: Romy de Laat-Kremers,
| | - Raf De Jongh
- Department of Anesthesiology, Ziekenhuis Oost Limburg, Genk, Belgium
- Department of Anesthesiology, Fondation Hopale, Berck-sur-Mer, France
| | - Marisa Ninivaggi
- Department of Functional Coagulation, Synapse Research Institute, Maastricht, Netherlands
| | - Aernoud Fiolet
- Department of Internal Medicine, Meander Medical Center, Amersfoort, Netherlands
| | - Rob Fijnheer
- Department of Internal Medicine, Meander Medical Center, Amersfoort, Netherlands
| | - Jasper Remijn
- Department of Clinical Chemistry, Meander Medical Center, Amersfoort, Netherlands
| | - Bas de Laat
- Department of Data Analysis and Artificial Intelligence, Synapse Research Institute, Maastricht, Netherlands
- Department of Functional Coagulation, Synapse Research Institute, Maastricht, Netherlands
| |
Collapse
|
110
|
Costa YMG, Silva SA, Teixeira LO, Pereira RM, Bertolini D, Britto AS, Oliveira LS, Cavalcanti GDC. COVID-19 Detection on Chest X-ray and CT Scan: A Review of the Top-100 Most Cited Papers. SENSORS (BASEL, SWITZERLAND) 2022; 22:7303. [PMID: 36236402 PMCID: PMC9570662 DOI: 10.3390/s22197303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 09/13/2022] [Accepted: 09/19/2022] [Indexed: 06/16/2023]
Abstract
Since the beginning of the COVID-19 pandemic, many works have been published proposing solutions to the problems that arose in this scenario. In this vein, one of the topics that attracted the most attention is the development of computer-based strategies to detect COVID-19 from thoracic medical imaging, such as chest X-ray (CXR) and computerized tomography scan (CT scan). By searching for works already published on this theme, we can easily find thousands of them. This is partly explained by the fact that the most severe worldwide pandemic emerged amid the technological advances recently achieved, and also considering the technical facilities to deal with the large amount of data produced in this context. Even though several of these works describe important advances, we cannot overlook the fact that others only use well-known methods and techniques without a more relevant and critical contribution. Hence, differentiating the works with the most relevant contributions is not a trivial task. The number of citations obtained by a paper is probably the most straightforward and intuitive way to verify its impact on the research community. Aiming to help researchers in this scenario, we present a review of the top-100 most cited papers in this field of investigation according to the Google Scholar search engine. We evaluate the distribution of the top-100 papers taking into account some important aspects, such as the type of medical imaging explored, learning settings, segmentation strategy, explainable artificial intelligence (XAI), and finally, the dataset and code availability.
Collapse
Affiliation(s)
- Yandre M. G. Costa
- Departamento de Informática, Universidade Estadual de Maringá, Maringá 87020-900, Brazil
| | - Sergio A. Silva
- Departamento de Informática, Universidade Estadual de Maringá, Maringá 87020-900, Brazil
| | - Lucas O. Teixeira
- Departamento de Informática, Universidade Estadual de Maringá, Maringá 87020-900, Brazil
| | | | - Diego Bertolini
- Departamento Acadêmico de Ciência da Computação, Universidade Tecnológica Federal do Paraná, Campo Mourão 87301-899, Brazil
| | - Alceu S. Britto
- Departmento de Ciência da Computação, Pontifícia Universidade Católica do Paraná, Curitiba 80215-901, Brazil
| | - Luiz S. Oliveira
- Departamento de Informática, Universidade Federal do Paraná, Curitiba 81531-980, Brazil
| | | |
Collapse
|
111
|
Huang HN, Zhang T, Yang CT, Sheen YJ, Chen HM, Chen CJ, Tseng MW. Image segmentation using transfer learning and Fast R-CNN for diabetic foot wound treatments. Front Public Health 2022; 10:969846. [PMID: 36203688 PMCID: PMC9530356 DOI: 10.3389/fpubh.2022.969846] [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: 06/25/2022] [Accepted: 08/15/2022] [Indexed: 01/25/2023] Open
Abstract
Diabetic foot ulcers (DFUs) are considered the most challenging forms of chronic ulcerations to handle their multifactorial nature. It is necessary to establish a comprehensive treatment plan, accurate, and systematic evaluation of a patient with a DFU. This paper proposed an image recognition of diabetic foot wounds to support the effective execution of the treatment plan. In the severity of a diabetic foot ulcer, we refer to the current qualitative evaluation method commonly used in clinical practice, developed by the International Working Group on the Diabetic Foot: PEDIS index, and the evaluation made by physicians. The deep neural network, convolutional neural network, object recognition, and other technologies are applied to analyze the classification, location, and size of wounds by image analysis technology. The image features are labeled with the help of the physician. The Object Detection Fast R-CNN method is applied to these wound images to build and train machine learning modules and evaluate their effectiveness. In the assessment accuracy, it can be indicated that the wound image detection data can be as high as 90%.
Collapse
Affiliation(s)
- Huang-Nan Huang
- Department of Applied Mathematics, Tunghai University, Taichung, Taiwan
| | - Tianyi Zhang
- Department of Computer Science, Tunghai University, Taichung, Taiwan
| | - Chao-Tung Yang
- Department of Computer Science, Tunghai University, Taichung, Taiwan,Research Center for Smart Sustainable Circular Economy, Tunghai University, Taichung, Taiwan,*Correspondence: Chao-Tung Yang
| | - Yi-Jing Sheen
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan,Yi-Jing Sheen
| | - Hsian-Min Chen
- Department of Medical Research, Center for Quantitative Imaging in Medicine (CQUIM), Taichung Veterans General Hospital, Taichung, Taiwan,Hsian-Min Chen
| | - Chur-Jen Chen
- Department of Applied Mathematics, Tunghai University, Taichung, Taiwan
| | - Meng-Wen Tseng
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
| |
Collapse
|
112
|
Chen J, Li Y, Guo L, Zhou X, Zhu Y, He Q, Han H, Feng Q. Machine learning techniques for CT imaging diagnosis of novel coronavirus pneumonia: a review. Neural Comput Appl 2022; 36:1-19. [PMID: 36159188 PMCID: PMC9483435 DOI: 10.1007/s00521-022-07709-0] [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: 04/13/2022] [Accepted: 08/04/2022] [Indexed: 11/20/2022]
Abstract
Since 2020, novel coronavirus pneumonia has been spreading rapidly around the world, bringing tremendous pressure on medical diagnosis and treatment for hospitals. Medical imaging methods, such as computed tomography (CT), play a crucial role in diagnosing and treating COVID-19. A large number of CT images (with large volume) are produced during the CT-based medical diagnosis. In such a situation, the diagnostic judgement by human eyes on the thousands of CT images is inefficient and time-consuming. Recently, in order to improve diagnostic efficiency, the machine learning technology is being widely used in computer-aided diagnosis and treatment systems (i.e., CT Imaging) to help doctors perform accurate analysis and provide them with effective diagnostic decision support. In this paper, we comprehensively review these frequently used machine learning methods applied in the CT Imaging Diagnosis for the COVID-19, discuss the machine learning-based applications from the various kinds of aspects including the image acquisition and pre-processing, image segmentation, quantitative analysis and diagnosis, and disease follow-up and prognosis. Moreover, we also discuss the limitations of the up-to-date machine learning technology in the context of CT imaging computer-aided diagnosis.
Collapse
Affiliation(s)
- Jingjing Chen
- Zhejiang University City College, Hangzhou, China
- Zhijiang College of Zhejiang University of Technology, Shaoxing, China
| | - Yixiao Li
- Faculty of Science, Zhejiang University of Technology, Hangzhou, China
| | - Lingling Guo
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Xiaokang Zhou
- Faculty of Data Science, Shiga University, Hikone, Japan
- RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
| | - Yihan Zhu
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Qingfeng He
- School of Pharmacy, Fudan University, Shanghai, China
| | - Haijun Han
- School of Medicine, Zhejiang University City College, Hangzhou, China
| | - Qilong Feng
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, China
| |
Collapse
|
113
|
Rashed BM, Popescu N. Critical Analysis of the Current Medical Image-Based Processing Techniques for Automatic Disease Evaluation: Systematic Literature Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:7065. [PMID: 36146414 PMCID: PMC9501859 DOI: 10.3390/s22187065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Revised: 09/06/2022] [Accepted: 09/14/2022] [Indexed: 06/16/2023]
Abstract
Medical image processing and analysis techniques play a significant role in diagnosing diseases. Thus, during the last decade, several noteworthy improvements in medical diagnostics have been made based on medical image processing techniques. In this article, we reviewed articles published in the most important journals and conferences that used or proposed medical image analysis techniques to diagnose diseases. Starting from four scientific databases, we applied the PRISMA technique to efficiently process and refine articles until we obtained forty research articles published in the last five years (2017-2021) aimed at answering our research questions. The medical image processing and analysis approaches were identified, examined, and discussed, including preprocessing, segmentation, feature extraction, classification, evaluation metrics, and diagnosis techniques. This article also sheds light on machine learning and deep learning approaches. We also focused on the most important medical image processing techniques used in these articles to establish the best methodologies for future approaches, discussing the most efficient ones and proposing in this way a comprehensive reference source of methods of medical image processing and analysis that can be very useful in future medical diagnosis systems.
Collapse
|
114
|
Srivastava G, Chauhan A, Jangid M, Chaurasia S. CoviXNet: A novel and efficient deep learning model for detection of COVID-19 using chest X-Ray images. Biomed Signal Process Control 2022; 78:103848. [PMID: 35694696 PMCID: PMC9174225 DOI: 10.1016/j.bspc.2022.103848] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 05/19/2022] [Accepted: 05/27/2022] [Indexed: 11/29/2022]
Abstract
The Coronavirus (COVID-19) pandemic has created havoc on humanity by causing millions of deaths and adverse physical and mental health effects. To prepare humankind for the fast and efficient detection of the virus and its variants shortly, COVID-19 detection using Artificial Intelligence and Computer-Aided Diagnosis has been the subject of several studies. To detect COVID-19, there are numerous publicly accessible datasets of Chest X-rays that the researchers have combined to solve the problem of inadequate data. The cause for concern here is that in combining two or more datasets, some of the images might be duplicates, so a curated dataset has been used in this study, taken from an author’s paper. This dataset consists of 1281 COVID-19, 3270 Normal X-rays, and 1656 viral-pneumonia infected Chest X-ray images. Dataset has been pre-processed and divided carefully to ensure that there are no duplicate images. A comparative study on many traditional pre-trained models was performed, analyzing top-performing models. Fine-tuned InceptionV3, Modified EfficientNet B0&B1 produced an accuracy of 99.78% on binary classification, i.e., covid-19 infected and normal Chest X-ray image. ResNetV2 had a classification accuracy of 97.90% for 3-class classification i.e., covid-19 infected, normal, and pneumonia. Furthermore, a trailblazing custom CNN-based model, CoviXNet, has been proposed consisting of 15 layers that take efficiency into account. The proposed model CoviXNet exhibited a 10-fold accuracy of 99.47% on binary classification and 96.61% on 3-class. CoviXNet has shown phenomenal performance with exceptional accuracy and minimum computational cost. We anticipate that this comparative study, along with the proposed model CoviXNet, can assist medical centers with the efficient real-life detection of Coronavirus.
Collapse
Affiliation(s)
- Gaurav Srivastava
- Department of Computer Science and Engineering, Manipal University Jaipur, 303007, Rajasthan, India
| | - Aninditaa Chauhan
- Department of Computer Science and Engineering, Manipal University Jaipur, 303007, Rajasthan, India
| | - Mahesh Jangid
- Department of Computer Science and Engineering, Manipal University Jaipur, 303007, Rajasthan, India
| | - Sandeep Chaurasia
- Department of Computer Science and Engineering, Manipal University Jaipur, 303007, Rajasthan, India
| |
Collapse
|
115
|
Oğuz Ç, Yağanoğlu M. Detection of COVID-19 using deep learning techniques and classification methods. Inf Process Manag 2022; 59:103025. [PMID: 35821878 PMCID: PMC9263717 DOI: 10.1016/j.ipm.2022.103025] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 07/01/2022] [Accepted: 07/02/2022] [Indexed: 01/07/2023]
Abstract
Since the patient is not quarantined during the conclusion of the Polymerase Chain Reaction (PCR) test used in the diagnosis of COVID-19, the disease continues to spread. In this study, it was aimed to reduce the duration and amount of transmission of the disease by shortening the diagnosis time of COVID-19 patients with the use of Computed Tomography (CT). In addition, it is aimed to provide a decision support system to radiologists in the diagnosis of COVID-19. In this study, deep features were extracted with deep learning models such as ResNet-50, ResNet-101, AlexNet, Vgg-16, Vgg-19, GoogLeNet, SqueezeNet, Xception on 1345 CT images obtained from the radiography database of Siirt Education and Research Hospital. These deep features are given to classification methods such as Support Vector Machine (SVM), k Nearest Neighbor (kNN), Random Forest (RF), Decision Trees (DT), Naive Bayes (NB), and their performance is evaluated with test images. Accuracy value, F1-score and ROC curve were considered as success criteria. According to the data obtained as a result of the application, the best performance was obtained with ResNet-50 and SVM method. The accuracy was 96.296%, the F1-score was 95.868%, and the AUC value was 0.9821. The deep learning model and classification method examined in this study and found to be high performance can be used as an auxiliary decision support system by preventing unnecessary tests for COVID-19 disease.
Collapse
Affiliation(s)
- Çinare Oğuz
- Department of Computer Engineering, Faculty of Engineering, Ataturk University, Erzurum, Turkey
| | - Mete Yağanoğlu
- Department of Computer Engineering, Faculty of Engineering, Ataturk University, Erzurum, Turkey
| |
Collapse
|
116
|
Gupta S, Shabaz M, Vyas S. Artificial intelligence and IoT based prediction of Covid-19 using chest X-ray images. SMART HEALTH 2022; 25:100299. [PMID: 35783463 PMCID: PMC9233885 DOI: 10.1016/j.smhl.2022.100299] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 05/31/2022] [Accepted: 06/15/2022] [Indexed: 11/30/2022]
Abstract
Coronavirus illness (COVID-19), discovered in late 2019, has spread rapidly worldwide, resulting in significant mortality. This study analyzed the performance of studies that employed machines and DL on chest X-ray pictures and CT scans for COVID-19 diagnosis. ML approaches on CT and X-ray images aided incorrectly in identifying COVID-19. The fast spread of COVID-19 worldwide and the growing number of deaths necessitates an immediate response from all sectors. Authorities will be able to deal with the effects more efficiently if such illnesses can be predicted in the future. Furthermore, it is crucial to maintain track of the number of infected persons through regular check-ups, and it is frequently required to confine affected people and implement medical treatments. In addition, various additional elements, such as environmental influences and commonalities among the most afflicted places, should be considered to slow the spread of COVID-19, and precautions should be taken. AI-based approaches for the prediction and diagnosis of COVID-19 were suggested in this paper. This Review Article discusses current advances in AI technology and its biological applications, particularly the coronavirus.
Collapse
|
117
|
Sarv Ahrabi S, Momenzadeh A, Baccarelli E, Scarpiniti M, Piazzo L. How much BiGAN and CycleGAN-learned hidden features are effective for COVID-19 detection from CT images? A comparative study. THE JOURNAL OF SUPERCOMPUTING 2022; 79:2850-2881. [PMID: 36042937 PMCID: PMC9411851 DOI: 10.1007/s11227-022-04775-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 08/10/2022] [Indexed: 06/15/2023]
Abstract
Bidirectional generative adversarial networks (BiGANs) and cycle generative adversarial networks (CycleGANs) are two emerging machine learning models that, up to now, have been used as generative models, i.e., to generate output data sampled from a target probability distribution. However, these models are also equipped with encoding modules, which, after weakly supervised training, could be, in principle, exploited for the extraction of hidden features from the input data. At the present time, how these extracted features could be effectively exploited for classification tasks is still an unexplored field. Hence, motivated by this consideration, in this paper, we develop and numerically test the performance of a novel inference engine that relies on the exploitation of BiGAN and CycleGAN-learned hidden features for the detection of COVID-19 disease from other lung diseases in computer tomography (CT) scans. In this respect, the main contributions of the paper are twofold. First, we develop a kernel density estimation (KDE)-based inference method, which, in the training phase, leverages the hidden features extracted by BiGANs and CycleGANs for estimating the (a priori unknown) probability density function (PDF) of the CT scans of COVID-19 patients and, then, in the inference phase, uses it as a target COVID-PDF for the detection of COVID diseases. As a second major contribution, we numerically evaluate and compare the classification accuracies of the implemented BiGAN and CycleGAN models against the ones of some state-of-the-art methods, which rely on the unsupervised training of convolutional autoencoders (CAEs) for attaining feature extraction. The performance comparisons are carried out by considering a spectrum of different training loss functions and distance metrics. The obtained classification accuracies of the proposed CycleGAN-based (resp., BiGAN-based) models outperform the corresponding ones of the considered benchmark CAE-based models of about 16% (resp., 14%).
Collapse
Affiliation(s)
- Sima Sarv Ahrabi
- Department of Information Engineering, Electronics and Telecommunications, Sapienza University or Rome, Via Eudossiana, 18, 00184 Roma, Italy
| | - Alireza Momenzadeh
- Department of Information Engineering, Electronics and Telecommunications, Sapienza University or Rome, Via Eudossiana, 18, 00184 Roma, Italy
| | - Enzo Baccarelli
- Department of Information Engineering, Electronics and Telecommunications, Sapienza University or Rome, Via Eudossiana, 18, 00184 Roma, Italy
| | - Michele Scarpiniti
- Department of Information Engineering, Electronics and Telecommunications, Sapienza University or Rome, Via Eudossiana, 18, 00184 Roma, Italy
| | - Lorenzo Piazzo
- Department of Information Engineering, Electronics and Telecommunications, Sapienza University or Rome, Via Eudossiana, 18, 00184 Roma, Italy
| |
Collapse
|
118
|
Karpiel I, Starcevic A, Urzeniczok M. Database and AI Diagnostic Tools Improve Understanding of Lung Damage, Correlation of Pulmonary Disease and Brain Damage in COVID-19. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22166312. [PMID: 36016071 PMCID: PMC9414394 DOI: 10.3390/s22166312] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 08/17/2022] [Accepted: 08/18/2022] [Indexed: 05/02/2023]
Abstract
The COVID-19 pandemic caused a sharp increase in the interest in artificial intelligence (AI) as a tool supporting the work of doctors in difficult conditions and providing early detection of the implications of the disease. Recent studies have shown that AI has been successfully applied in the healthcare sector. The objective of this paper is to perform a systematic review to summarize the electroencephalogram (EEG) findings in patients with coronavirus disease (COVID-19) and databases and tools used in artificial intelligence algorithms, supporting the diagnosis and correlation between lung disease and brain damage, and lung damage. Available search tools containing scientific publications, such as PubMed and Google Scholar, were comprehensively evaluated and searched with open databases and tools used in AI algorithms. This work aimed to collect papers from the period of January 2019-May 2022 including in their resources the database from which data necessary for further development of algorithms supporting the diagnosis of the respiratory system can be downloaded and the correlation between lung disease and brain damage can be evaluated. The 10 articles which show the most interesting AI algorithms, trained by using open databases and associated with lung diseases, were included for review with 12 articles related to EEGs, which have/or may be related with lung diseases.
Collapse
Affiliation(s)
- Ilona Karpiel
- Łukasiewicz Research Network—Institute of Medical Technology and Equipment, 41-800 Zabrze, Poland
- Correspondence:
| | - Ana Starcevic
- Laboratory for Multimodal Neuroimaging, Institute of Anatomy, Medical Faculty, University of Belgrade, 11000 Belgrade, Serbia
| | - Mirella Urzeniczok
- Łukasiewicz Research Network—Institute of Medical Technology and Equipment, 41-800 Zabrze, Poland
| |
Collapse
|
119
|
Initial experience of a deep learning application for the differentiation of Kikuchi-Fujimoto’s disease from tuberculous lymphadenitis on neck CECT. Sci Rep 2022; 12:14184. [PMID: 35986073 PMCID: PMC9391448 DOI: 10.1038/s41598-022-18535-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 08/16/2022] [Indexed: 11/14/2022] Open
Abstract
Neck contrast-enhanced CT (CECT) is a routine tool used to evaluate patients with cervical lymphadenopathy. This study aimed to evaluate the ability of convolutional neural networks (CNNs) to classify Kikuchi-Fujimoto’s disease (KD) and cervical tuberculous lymphadenitis (CTL) on neck CECT in patients with benign cervical lymphadenopathy. A retrospective analysis of consecutive patients with biopsy-confirmed KD and CTL in a single center, from January 2012 to June 2020 was performed. This study included 198 patients of whom 125 patients (mean age, 25.1 years ± 8.7, 31 men) had KD and 73 patients (mean age, 41.0 years ± 16.8, 34 men) had CTL. A neuroradiologist manually labelled the enlarged lymph nodes on the CECT images. Using these labels as the reference standard, a CNNs was developed to classify the findings as KD or CTL. The CT images were divided into training (70%), validation (10%), and test (20%) subsets. As a supervised augmentation method, the Cut&Remain method was applied to improve performance. The best area under the receiver operating characteristic curve for classifying KD from CTL for the test set was 0.91. This study shows that the differentiation of KD from CTL on neck CECT using a CNNs is feasible with high diagnostic performance.
Collapse
|
120
|
Ortiz-Vilchis P, Ramirez-Arellano A. An Entropy-Based Measure of Complexity: An Application in Lung-Damage. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1119. [PMID: 36010783 PMCID: PMC9407132 DOI: 10.3390/e24081119] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Revised: 07/23/2022] [Accepted: 08/12/2022] [Indexed: 06/15/2023]
Abstract
The computed tomography (CT) chest is a tool for diagnostic tests and the early evaluation of lung infections, pulmonary interstitial damage, and complications caused by common pneumonia and COVID-19. Additionally, computer-aided diagnostic systems and methods based on entropy, fractality, and deep learning have been implemented to analyse lung CT images. This article aims to introduce an Entropy-based Measure of Complexity (EMC). In addition, derived from EMC, a Lung Damage Measure (LDM) is introduced to show a medical application. CT scans of 486 healthy subjects, 263 diagnosed with COVID-19, and 329 with pneumonia were analysed using the LDM. The statistical analysis shows a significant difference in LDM between healthy subjects and those suffering from COVID-19 and common pneumonia. The LDM of common pneumonia was the highest, followed by COVID-19 and healthy subjects. Furthermore, LDM increased as much as clinical classification and CO-RADS scores. Thus, LDM is a measure that could be used to determine or confirm the scored severity. On the other hand, the d-summable information model best fits the information obtained by the covering of the CT; thus, it can be the cornerstone for formulating a fractional LDM.
Collapse
|
121
|
Gupta V, Jain N, Sachdeva J, Gupta M, Mohan S, Bajuri MY, Ahmadian A. Improved COVID-19 detection with chest x-ray images using deep learning. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:37657-37680. [PMID: 35968409 PMCID: PMC9361266 DOI: 10.1007/s11042-022-13509-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 10/18/2021] [Accepted: 07/13/2022] [Indexed: 06/15/2023]
Abstract
The novel coronavirus disease, which originated in Wuhan, developed into a severe public health problem worldwide. Immense stress in the society and health department was advanced due to the multiplying numbers of COVID carriers and deaths. This stress can be lowered by performing a high-speed diagnosis for the disease, which can be a crucial stride for opposing the deadly virus. A good large amount of time is consumed in the diagnosis. Some applications that use medical images like X-Rays or CT-Scans can pace up the time used in diagnosis. Hence, this paper aims to create a computer-aided-design system that will use the chest X-Ray as input and further classify it into one of the three classes, namely COVID-19, viral Pneumonia, and healthy. Since the COVID-19 positive chest X-Rays dataset was low, we have exploited four pre-trained deep neural networks (DNNs) to find the best for this system. The dataset consisted of 2905 images with 219 COVID-19 cases, 1341 healthy cases, and 1345 viral pneumonia cases. Out of these images, the models were evaluated on 30 images of each class for the testing, while the rest of them were used for training. It is observed that AlexNet attained an accuracy of 97.6% with an average precision, recall, and F1 score of 0.98, 0.97, and 0.98, respectively.
Collapse
Affiliation(s)
- Vedika Gupta
- Jindal Global Business School, O.P. Jindal Global University, Haryana, India
| | - Nikita Jain
- Bharati Vidyapeeth’s College of Engineering, Delhi, India
| | - Jatin Sachdeva
- Bharati Vidyapeeth’s College of Engineering, Delhi, India
| | - Mudit Gupta
- Bharati Vidyapeeth’s College of Engineering, Delhi, India
| | - Senthilkumar Mohan
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
| | - Mohd Yazid Bajuri
- Department of Orthopaedics and Traumatology, Faculty of Medicine, Universiti Kebangsaan Malaysia (UKM), Kuala Lumpur, Malaysia
| | - Ali Ahmadian
- Decision Lab, Mediterranea University of Reggio Calabria, 89124 Reggio Calabria, Italy
- Department of Mathematics, Near East University, Nicosia, TRNC, Mersin 10, Turkey
| |
Collapse
|
122
|
Akinyelu AA, Blignaut P. COVID-19 diagnosis using deep learning neural networks applied to CT images. Front Artif Intell 2022; 5:919672. [PMID: 35990616 PMCID: PMC9389263 DOI: 10.3389/frai.2022.919672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 07/13/2022] [Indexed: 11/25/2022] Open
Abstract
COVID-19, a deadly and highly contagious virus, caused the deaths of millions of individuals around the world. Early detection of the virus can reduce the virus transmission and fatality rate. Many deep learning (DL) based COVID-19 detection methods have been proposed, but most are trained on either small, incomplete, noisy, or imbalanced datasets. Many are also trained on a small number of COVID-19 samples. This study tackles these concerns by introducing DL-based solutions for COVID-19 diagnosis using computerized tomography (CT) images and 12 cutting-edge DL pre-trained models with acceptable Top-1 accuracy. All the models are trained on 9,000 COVID-19 samples and 5,000 normal images, which is higher than the COVID-19 images used in most studies. In addition, while most of the research used X-ray images for training, this study used CT images. CT scans capture blood arteries, bones, and soft tissues more effectively than X-Ray. The proposed techniques were evaluated, and the results show that NASNetLarge produced the best classification accuracy, followed by InceptionResNetV2 and DenseNet169. The three models achieved an accuracy of 99.86, 99.79, and 99.71%, respectively. Moreover, DenseNet121 and VGG16 achieved the best sensitivity, while InceptionV3 and InceptionResNetV2 achieved the best specificity. DenseNet121 and VGG16 attained a sensitivity of 99.94%, while InceptionV3 and InceptionResNetV2 achieved a specificity of 100%. The models are compared to those designed in three existing studies, and they produce better results. The results show that deep neural networks have the potential for computer-assisted COVID-19 diagnosis. We hope this study will be valuable in improving the decisions and accuracy of medical practitioners when diagnosing COVID-19. This study will assist future researchers in minimizing the repetition of analysis and identifying the ideal network for their tasks.
Collapse
|
123
|
CovMnet–Deep Learning Model for classifying Coronavirus (COVID-19). HEALTH AND TECHNOLOGY 2022; 12:1009-1024. [PMID: 35966170 PMCID: PMC9362573 DOI: 10.1007/s12553-022-00688-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 07/25/2022] [Indexed: 12/15/2022]
Abstract
Diagnosing COVID-19, current pandemic disease using Chest X-ray images is widely used to evaluate the lung disorders. As the spread of the disease is enormous many medical camps are being conducted to screen the patients and Chest X-ray is a simple imaging modality to detect presence of lung disorders. Manual lung disorder detection using Chest X-ray by radiologist is a tedious process and may lead to inter and intra-rate errors. Various deep convolution neural network techniques were tested for detecting COVID-19 abnormalities in lungs using Chest X-ray images. This paper proposes deep learning model to classify COVID-19 and normal chest X-ray images. Experiments are carried out for deep feature extraction, fine-tuning of convolutional neural networks (CNN) hyper parameters, and end-to-end training of four variants of the CNN model. The proposed CovMnet provide better classification accuracy of 97.4% for COVID-19 /normal than those reported in the previous studies. The proposed CovMnet model has potential to aid radiologist to monitor COVID-19 disease and proves to be an efficient non-invasive COVID-19 diagnostic tool for lung disorders.
Collapse
|
124
|
Siddiqui S, Arifeen M, Hopgood A, Good A, Gegov A, Hossain E, Rahman W, Hossain S, Al Jannat S, Ferdous R, Masum S. Deep Learning Models for the Diagnosis and Screening of COVID-19: A Systematic Review. SN COMPUTER SCIENCE 2022; 3:397. [PMID: 35911439 PMCID: PMC9312319 DOI: 10.1007/s42979-022-01326-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 04/11/2022] [Indexed: 10/29/2022]
Abstract
COVID-19, caused by SARS-CoV-2, has been declared as a global pandemic by WHO. Early diagnosis of COVID-19 patients may reduce the impact of coronavirus using modern computational methods like deep learning. Various deep learning models based on CT and chest X-ray images are studied and compared in this study as an alternative solution to reverse transcription-polymerase chain reactions. This study consists of three stages: planning, conduction, and analysis/reporting. In the conduction stage, inclusion and exclusion criteria are applied to the literature searching and identification. Then, we have implemented quality assessment rules, where over 75 scored articles in the literature were included. Finally, in the analysis/reporting stage, all the papers are reviewed and analysed. After the quality assessment of the individual papers, this study adopted 57 articles for the systematic literature review. From these reviews, the critical analysis of each paper, including the represented matrix for the model evaluation, existing contributions, and motivation, has been tracked with suitable illustrations. We have also interpreted several insights of each paper with appropriate annotation. Further, a set of comparisons has been enumerated with suitable discussion. Convolutional neural networks are the most commonly used deep learning architecture for COVID-19 disease classification and identification from X-ray and CT images. Various prior studies did not include data from a hospital setting nor did they consider data preprocessing before training a deep learning model.
Collapse
Affiliation(s)
- Shah Siddiqui
- Faculty of Technology, The University of Portsmouth (UoP), Portland Building, Portland Street, Portsmouth, PO1 3AH UK.,School of Computing, University of Portsmouth (UoP), Lion Terrace, Portsmouth, PO1 3HE UK
| | - Murshedul Arifeen
- Time Research and Innovation (TRI), 189 Foundry Lane, Southampton, SO15 3JZ UK.,336/7, TV Road East Rampura, Khilgaon, Dhaka 1219 Bangladesh
| | - Adrian Hopgood
- Faculty of Technology, The University of Portsmouth (UoP), Portland Building, Portland Street, Portsmouth, PO1 3AH UK
| | - Alice Good
- Faculty of Technology, The University of Portsmouth (UoP), Portland Building, Portland Street, Portsmouth, PO1 3AH UK
| | - Alexander Gegov
- Faculty of Technology, The University of Portsmouth (UoP), Portland Building, Portland Street, Portsmouth, PO1 3AH UK
| | - Elias Hossain
- Time Research and Innovation (TRI), 189 Foundry Lane, Southampton, SO15 3JZ UK.,336/7, TV Road East Rampura, Khilgaon, Dhaka 1219 Bangladesh
| | - Wahidur Rahman
- Time Research and Innovation (TRI), 189 Foundry Lane, Southampton, SO15 3JZ UK.,336/7, TV Road East Rampura, Khilgaon, Dhaka 1219 Bangladesh
| | - Shazzad Hossain
- Time Research and Innovation (TRI), 189 Foundry Lane, Southampton, SO15 3JZ UK.,336/7, TV Road East Rampura, Khilgaon, Dhaka 1219 Bangladesh
| | - Sabila Al Jannat
- Time Research and Innovation (TRI), 189 Foundry Lane, Southampton, SO15 3JZ UK.,336/7, TV Road East Rampura, Khilgaon, Dhaka 1219 Bangladesh
| | - Rezowan Ferdous
- Time Research and Innovation (TRI), 189 Foundry Lane, Southampton, SO15 3JZ UK.,336/7, TV Road East Rampura, Khilgaon, Dhaka 1219 Bangladesh
| | - Shamsul Masum
- Faculty of Technology, The University of Portsmouth (UoP), Portland Building, Portland Street, Portsmouth, PO1 3AH UK
| |
Collapse
|
125
|
Ozdemir MA, Kisa DH, Guren O, Akan A. Hand gesture classification using time–frequency images and transfer learning based on CNN. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103787] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
126
|
Emin Sahin M. Deep learning-based approach for detecting COVID-19 in chest X-rays. Biomed Signal Process Control 2022; 78:103977. [PMID: 35855833 PMCID: PMC9279305 DOI: 10.1016/j.bspc.2022.103977] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 06/16/2022] [Accepted: 07/11/2022] [Indexed: 12/15/2022]
Abstract
Today, 2019 Coronavirus (COVID-19) infections are a major health concern worldwide. Therefore, detecting COVID-19 in X-ray images is crucial for diagnosis, evaluation, and treatment. Furthermore, expressing diagnostic uncertainty in a report is a challenging duty but unavoidable task for radiologists. This study proposes a novel CNN (Convolutional Neural Network) model for automatic COVID-19 identification utilizing chest X-ray images. The proposed CNN model is designed to be a reliable diagnostic tool for two-class categorization (COVID and Normal). In addition to the proposed model, different architectures, including the pre-trained MobileNetv2 and ResNet50 models, are evaluated for this COVID-19 dataset (13,824 X-ray images) and our suggested model is compared to these existing COVID-19 detection algorithms in terms of accuracy. Experimental results show that our proposed model identifies patients with COVID-19 disease with 96.71 percent accuracy, 91.89 percent F1-score. Our proposed approach CNN’s experimental results show that it outperforms the most advanced algorithms currently available. This model can assist clinicians in making informed judgments on how to diagnose COVID-19, as well as make test kits more accessible.
Collapse
Affiliation(s)
- M Emin Sahin
- Department of Computer Engineering, Yozgat Bozok University, Turkey
| |
Collapse
|
127
|
Xu E, Nemati S, Tremoulet AH. A deep convolutional neural network for Kawasaki disease diagnosis. Sci Rep 2022; 12:11438. [PMID: 35794205 PMCID: PMC9259696 DOI: 10.1038/s41598-022-15495-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 06/24/2022] [Indexed: 11/24/2022] Open
Abstract
Kawasaki disease (KD), the most common cause of acquired heart disease in children, can be easily missed as it shares clinical findings with other pediatric illnesses, leading to risk of myocardial infarction or death. KD remains a clinical diagnosis for which there is no diagnostic test, yet there are classic findings on exam that can be captured in a photograph. This study aimed to develop a deep convolutional neural network, KD-CNN, to differentiate photographs of KD clinical signs from those of other pediatric illnesses. To create the dataset, we used an innovative combination of crowdsourcing images and downloading from public domains on the Internet. KD-CNN was then pretrained using transfer learning from VGG-16 and fine-tuned on the KD dataset, and methods to compensate for limited data were explored to improve model performance and generalizability. KD-CNN achieved a median AUC of 0.90 (IQR 0.10 from tenfold cross validation), with a sensitivity of 0.80 (IQR 0.18) and specificity of 0.85 (IQR 0.19) to distinguish between children with and without clinical manifestations of KD. KD-CNN is a novel application of CNN in medicine, with the potential to assist clinicians in differentiating KD from other pediatric illnesses and thus reduce KD morbidity and mortality.
Collapse
Affiliation(s)
- Ellen Xu
- Department of Pediatrics, University of California San Diego and Rady Children's Hospital, San Diego, CA, USA
| | - Shamim Nemati
- Department of Biomedical Informatics, UC San Diego Health, University of California San Diego, La Jolla, CA, USA
| | - Adriana H Tremoulet
- Department of Pediatrics, University of California San Diego and Rady Children's Hospital, San Diego, CA, USA.
| |
Collapse
|
128
|
|
129
|
Basu S, Agarwal R, Srivastava V. Deep discriminative learning model with calibrated attention map for the automated diagnosis of diffuse large B-cell lymphoma. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
130
|
AI-Based Chest CT Analysis for Rapid COVID-19 Diagnosis and Prognosis: A Practical Tool to Flag High-Risk Patients and Lower Healthcare Costs. Diagnostics (Basel) 2022; 12:diagnostics12071608. [PMID: 35885513 PMCID: PMC9324628 DOI: 10.3390/diagnostics12071608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 06/27/2022] [Accepted: 06/29/2022] [Indexed: 11/17/2022] Open
Abstract
Early diagnosis of COVID-19 is required to provide the best treatment to our patients, to prevent the epidemic from spreading in the community, and to reduce costs associated with the aggravation of the disease. We developed a decision tree model to evaluate the impact of using an artificial intelligence-based chest computed tomography (CT) analysis software (icolung, icometrix) to analyze CT scans for the detection and prognosis of COVID-19 cases. The model compared routine practice where patients receiving a chest CT scan were not screened for COVID-19, with a scenario where icolung was introduced to enable COVID-19 diagnosis. The primary outcome was to evaluate the impact of icolung on the transmission of COVID-19 infection, and the secondary outcome was the in-hospital length of stay. Using EUR 20000 as a willingness-to-pay threshold, icolung is cost-effective in reducing the risk of transmission, with a low prevalence of COVID-19 infections. Concerning the hospitalization cost, icolung is cost-effective at a higher value of COVID-19 prevalence and risk of hospitalization. This model provides a framework for the evaluation of AI-based tools for the early detection of COVID-19 cases. It allows for making decisions regarding their implementation in routine practice, considering both costs and effects.
Collapse
|
131
|
Attallah O, Samir A. A wavelet-based deep learning pipeline for efficient COVID-19 diagnosis via CT slices. Appl Soft Comput 2022; 128:109401. [PMID: 35919069 PMCID: PMC9335861 DOI: 10.1016/j.asoc.2022.109401] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 05/20/2022] [Accepted: 07/25/2022] [Indexed: 12/30/2022]
Abstract
The quick diagnosis of the novel coronavirus (COVID-19) disease is vital to prevent its propagation and improve therapeutic outcomes. Computed tomography (CT) is believed to be an effective tool for diagnosing COVID-19, however, the CT scan contains hundreds of slices that are complex to be analyzed and could cause delays in diagnosis. Artificial intelligence (AI) especially deep learning (DL), could facilitate and speed up COVID-19 diagnosis from such scans. Several studies employed DL approaches based on 2D CT images from a single view, nevertheless, 3D multiview CT slices demonstrated an excellent ability to enhance the efficiency of COVID-19 diagnosis. The majority of DL-based studies utilized the spatial information of the original CT images to train their models, though, using spectral–temporal information could improve the detection of COVID-19. This article proposes a DL-based pipeline called CoviWavNet for the automatic diagnosis of COVID-19. CoviWavNet uses a 3D multiview dataset called OMNIAHCOV. Initially, it analyzes the CT slices using multilevel discrete wavelet decomposition (DWT) and then uses the heatmaps of the approximation levels to train three ResNet CNN models. These ResNets use the spectral–temporal information of such images to perform classification. Subsequently, it investigates whether the combination of spatial information with spectral–temporal information could improve the diagnostic accuracy of COVID-19. For this purpose, it extracts deep spectral–temporal features from such ResNets using transfer learning and integrates them with deep spatial features extracted from the same ResNets trained with the original CT slices. Then, it utilizes a feature selection step to reduce the dimension of such integrated features and use them as inputs to three support vector machine (SVM) classifiers. To further validate the performance of CoviWavNet, a publicly available benchmark dataset called SARS-COV-2-CT-Scan is employed. The results of CoviWavNet have demonstrated that using the spectral–temporal information of the DWT heatmap images to train the ResNets is superior to utilizing the spatial information of the original CT images. Furthermore, integrating deep spectral–temporal features with deep spatial features has enhanced the classification accuracy of the three SVM classifiers reaching a final accuracy of 99.33% and 99.7% for the OMNIAHCOV and SARS-COV-2-CT-Scan datasets respectively. These accuracies verify the outstanding performance of CoviWavNet compared to other related studies. Thus, CoviWavNet can help radiologists in the rapid and accurate diagnosis of COVID-19 diagnosis.
Collapse
Affiliation(s)
- Omneya Attallah
- Department of Electronics and Communications Engineering, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Alexandria 1029, Egypt
| | - Ahmed Samir
- Department of Radiodiagnosis, Faculty of Medicine, University of Alexandria, Egypt
| |
Collapse
|
132
|
Mori M, Alborghetti L, Palumbo D, Broggi S, Raspanti D, Rovere Querini P, Del Vecchio A, De Cobelli F, Fiorino C. Atlas-Based Lung Segmentation Combined With Automatic Densitometry Characterization In COVID-19 Patients: Training, Validation And First Application In A Longitudinal Study. Phys Med 2022; 100:142-152. [PMID: 35839667 PMCID: PMC9250926 DOI: 10.1016/j.ejmp.2022.06.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 06/15/2022] [Accepted: 06/29/2022] [Indexed: 11/16/2022] Open
Abstract
Purpose To develop and validate an automated segmentation tool for COVID-19 lung CTs. To combine it with densitometry information in identifying Aerated, Intermediate and Consolidated Volumes in admission (CT1) and follow up CT (CT3). Materials and Methods An Atlas was trained on manually segmented CT1 of 250 patients and validated on 10 CT1 of the training group, 10 new CT1 and 10 CT3, by comparing DICE index between automatic (AUTO), automatic-corrected (AUTOMAN) and manual (MAN) contours. A previously developed automatic method was applied on HU lung density histograms to quantify Aerated, Intermediate and Consolidated Volumes. Volumes of subregions in validation CT1 and CT3 were quantified for each method. Results In validation CT1/CT3, manual correction of automatic contours was not necessary in 40% of cases. Mean DICE values for both lungs were 0.94 for AUTOVsMAN and 0.96 for AUTOMANVsMAN. Differences between Aerated and Intermediate Volumes quantified with AUTOVsMAN contours were always < 6%. Consolidated Volumes showed larger differences (mean: −95 ± 72 cc). If considering AUTOMANVsMAN volumes, differences got further smaller for Aerated and Intermediate, and were drastically reduced for consolidated Volumes (mean: −36 ± 25 cc). The average time for manual correction of automatic lungs contours on CT1 was 5 ± 2 min. Conclusions An Atlas for automatic segmentation of lungs in COVID-19 patients was developed and validated. Combined with a previously developed method for lung densitometry characterization, it provides a fast, operator-independent way to extract relevant quantitative parameters with minimal manual intervention.
Collapse
Affiliation(s)
- Martina Mori
- Medical Physics, San Raffaele Scientific Institute, Milano, Italy.
| | - Lisa Alborghetti
- Medical Physics, San Raffaele Scientific Institute, Milano, Italy
| | - Diego Palumbo
- Radiology, San Raffaele Scientific Institute, Milano, Italy
| | - Sara Broggi
- Medical Physics, San Raffaele Scientific Institute, Milano, Italy
| | | | - Patrizia Rovere Querini
- Internal Medecine, San Raffaele Scientific Institute, Milano, Italy; Faculty of Medicine and Surgery, Vita-Salute San Raffaele University, Milano, Italy
| | | | - Francesco De Cobelli
- Radiology, San Raffaele Scientific Institute, Milano, Italy; Faculty of Medicine and Surgery, Vita-Salute San Raffaele University, Milano, Italy
| | - Claudio Fiorino
- Medical Physics, San Raffaele Scientific Institute, Milano, Italy
| |
Collapse
|
133
|
Li M, Li X, Jiang Y, Zhang J, Luo H, Yin S. Explainable multi-instance and multi-task learning for COVID-19 diagnosis and lesion segmentation in CT images. Knowl Based Syst 2022; 252:109278. [PMID: 35783000 PMCID: PMC9235304 DOI: 10.1016/j.knosys.2022.109278] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 06/12/2022] [Accepted: 06/13/2022] [Indexed: 11/16/2022]
Abstract
Coronavirus Disease 2019 (COVID-19) still presents a pandemic trend globally. Detecting infected individuals and analyzing their status can provide patients with proper healthcare while protecting the normal population. Chest CT (computed tomography) is an effective tool for screening of COVID-19. It displays detailed pathology-related information. To achieve automated COVID-19 diagnosis and lung CT image segmentation, convolutional neural networks (CNNs) have become mainstream methods. However, most of the previous works consider automated diagnosis and image segmentation as two independent tasks, in which some focus on lung fields segmentation and the others focus on single-lesion segmentation. Moreover, lack of clinical explainability is a common problem for CNN-based methods. In such context, we develop a multi-task learning framework in which the diagnosis of COVID-19 and multi-lesion recognition (segmentation of CT images) are achieved simultaneously. The core of the proposed framework is an explainable multi-instance multi-task network. The network learns task-related features adaptively with learnable weights, and gives explicable diagnosis results by suggesting local CT images with lesions as additional evidence. Then, severity assessment of COVID-19 and lesion quantification are performed to analyze patient status. Extensive experimental results on real-world datasets show that the proposed framework outperforms all the compared approaches for COVID-19 diagnosis and multi-lesion segmentation.
Collapse
Affiliation(s)
- Minglei Li
- Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, 150001, Heilongjiang, China
| | - Xiang Li
- Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, 150001, Heilongjiang, China
| | - Yuchen Jiang
- Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, 150001, Heilongjiang, China
| | - Jiusi Zhang
- Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, 150001, Heilongjiang, China
| | - Hao Luo
- Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, 150001, Heilongjiang, China
| | - Shen Yin
- Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology, Trondheim, 7034, Norway
| |
Collapse
|
134
|
Federated Learning Approach with Pre-Trained Deep Learning Models for COVID-19 Detection from Unsegmented CT images. LIFE (BASEL, SWITZERLAND) 2022; 12:life12070958. [PMID: 35888048 PMCID: PMC9316900 DOI: 10.3390/life12070958] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 06/21/2022] [Accepted: 06/23/2022] [Indexed: 12/17/2022]
Abstract
(1) Background: Coronavirus disease 2019 (COVID-19) is an infectious disease caused by SARS-CoV-2. Reverse transcription polymerase chain reaction (RT-PCR) remains the current gold standard for detecting SARS-CoV-2 infections in nasopharyngeal swabs. In Romania, the first reported patient to have contracted COVID-19 was officially declared on 26 February 2020. (2) Methods: This study proposes a federated learning approach with pre-trained deep learning models for COVID-19 detection. Three clients were locally deployed with their own dataset. The goal of the clients was to collaborate in order to obtain a global model without sharing samples from the dataset. The algorithm we developed was connected to our internal picture archiving and communication system and, after running backwards, it encountered chest CT changes suggestive for COVID-19 in a patient investigated in our medical imaging department on the 28 January 2020. (4) Conclusions: Based on our results, we recommend using an automated AI-assisted software in order to detect COVID-19 based on the lung imaging changes as an adjuvant diagnostic method to the current gold standard (RT-PCR) in order to greatly enhance the management of these patients and also limit the spread of the disease, not only to the general population but also to healthcare professionals.
Collapse
|
135
|
A New Approach to COVID-19 Detection: An ANN Proposal Optimized through Tree-Seed Algorithm. Symmetry (Basel) 2022. [DOI: 10.3390/sym14071310] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Coronavirus disease (COVID-19), which affects the whole world, continues to spread. This disease has infected and killed millions of people worldwide. To limit the rate of spread of the disease, early detection should be provided and then the infected person should be quarantined. This paper proposes a Deep Learning-based application for early and accurate diagnosis of COVID-19. Compared to other studies, this application’s biggest difference and contribution are that it uses Tree Seed Algorithm (TSA)-optimized Artificial Neural Networks (ANN) to classify deep architectural features. Previous studies generally use fully connected layers for end-to-end learning classification. However, this study proves that even relatively simple AlexNet features can be classified more accurately with the TSA-ANN structure. The proposed hybrid model provides diagnosis with 98.54% accuracy for COVID-19 disease, which shows asymmetric distribution on Computed Tomography (CT) images. As a result, it is shown that using the proposed classification strategy, the features of end-to-end architectures can be classified more accurately.
Collapse
|
136
|
Minaei SE, Khoei S, Khoee S, Mahdavi SR. Sensitization of glioblastoma cancer cells to radiotherapy and magnetic hyperthermia by targeted temozolomide-loaded magnetite tri-block copolymer nanoparticles as a nanotheranostic agent. Life Sci 2022; 306:120729. [PMID: 35753439 DOI: 10.1016/j.lfs.2022.120729] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 06/09/2022] [Accepted: 06/20/2022] [Indexed: 11/27/2022]
Abstract
AIMS Recently, the development of new strategies in the treatment and diagnosis of cancer cells such as thermo-radiation-sensitizer and theranostic agents have received a great deal of attention. In this work, folic acid-conjugated temozolomide-loaded SPION@PEG-PBA-PEG nanoparticles (TMZ-MNP-FA NPs) were proposed for use as magnetic resonance imaging (MRI) contrast agents and to enhance the cytotoxic effects of hyperthermia and radiotherapy. MAIN METHODS Nanoparticles were synthesized by the Nano-precipitation method and their characteristics were determined by dynamic light scattering (DLS), scanning electron microscopy (SEM) and X-ray powder diffraction (XRD). To evaluate the thermo-radio-sensitization effects of NPs, C6 cells were treated with nanoparticles for 24 h and then exposed to 6-MV X-ray radiation. After radiotherapy, the cells were subjected to an alternating magnetic field (AMF) hyperthermia. The therapeutic potential was assessed using clonogenic assay, ROS generation measurement, flow cytometry assay, and qRT-PCR analysis. Also, the diagnostic properties of the nanoparticles were assessed by MRI. KEY FINDINGS MRI scanning indicated that nanoparticles accumulated in C6 cells could be tracked by T2-weighted MR imaging. Colony formation assay proved that TMZ-MNP-FA NPs enhanced the anti-proliferation effects of AMF by 1.94-fold compared to AMF alone (P < 0.0001). Moreover, these NPs improved the radiation effects with a dose enhancement factor of 1.65. All results showed that the combination of carrier-based chemotherapy with hyperthermia and radiotherapy caused a higher anticancer efficacy than single- or two-modality treatments. SIGNIFICANCE The nanoparticles advanced in this study can be proposed as the promising theranostic and thermo-radio-sensitizer platform for the diagnosis and tri-modal synergistic cancer therapy.
Collapse
Affiliation(s)
- Soraya Emamgholizadeh Minaei
- Department of Medical Physics and Imaging, School of Allied Medical Sciences, Urmia University of Medical Sciences, Urmia, Iran
| | - Samideh Khoei
- Finetech in Medicine Research Center, Iran University of Medical Sciences, Tehran, Iran; Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran.
| | - Sepideh Khoee
- Department of Polymer Chemistry, School of Chemistry, College of Science, University of Tehran, Tehran, Iran
| | - Seied Rabi Mahdavi
- Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| |
Collapse
|
137
|
Band SS, Ardabili S, Yarahmadi A, Pahlevanzadeh B, Kiani AK, Beheshti A, Alinejad-Rokny H, Dehzangi I, Chang A, Mosavi A, Moslehpour M. A Survey on Machine Learning and Internet of Medical Things-Based Approaches for Handling COVID-19: Meta-Analysis. Front Public Health 2022; 10:869238. [PMID: 35812486 PMCID: PMC9260273 DOI: 10.3389/fpubh.2022.869238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 04/20/2022] [Indexed: 11/13/2022] Open
Abstract
Early diagnosis, prioritization, screening, clustering, and tracking of patients with COVID-19, and production of drugs and vaccines are some of the applications that have made it necessary to use a new style of technology to involve, manage, and deal with this epidemic. Strategies backed by artificial intelligence (A.I.) and the Internet of Things (IoT) have been undeniably effective to understand how the virus works and prevent it from spreading. Accordingly, the main aim of this survey is to critically review the ML, IoT, and the integration of IoT and ML-based techniques in the applications related to COVID-19, from the diagnosis of the disease to the prediction of its outbreak. According to the main findings, IoT provided a prompt and efficient approach to tracking the disease spread. On the other hand, most of the studies developed by ML-based techniques aimed at the detection and handling of challenges associated with the COVID-19 pandemic. Among different approaches, Convolutional Neural Network (CNN), Support Vector Machine, Genetic CNN, and pre-trained CNN, followed by ResNet have demonstrated the best performances compared to other methods.
Collapse
Affiliation(s)
- Shahab S. Band
- Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, Douliou, Taiwan
| | - Sina Ardabili
- Department of Informatics, J. Selye University, Komárom, Slovakia
| | - Atefeh Yarahmadi
- Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, Douliou, Taiwan
| | - Bahareh Pahlevanzadeh
- Department of Design and System Operations, Regional Information Center for Science and Technology (R.I.C.E.S.T.), Shiraz, Iran
| | - Adiqa Kausar Kiani
- Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, Douliou, Taiwan
| | - Amin Beheshti
- Department of Computing, Macquarie University, Sydney, NSW, Australia
| | - Hamid Alinejad-Rokny
- BioMedical Machine Learning Lab, The Graduate School of Biomedical Engineering, U.N.S.W. Sydney, Sydney, NSW, Australia
- U.N.S.W. Data Science Hub, The University of New South Wales (U.N.S.W. Sydney), Sydney, NSW, Australia
- Health Data Analytics Program, AI-enabled Processes (A.I.P.) Research Centre, Macquarie University, Sydney, NSW, Australia
| | - Iman Dehzangi
- Department of Computer Science, Rutgers University, Camden, NJ, United States
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, United States
| | - Arthur Chang
- Bachelor Program in Interdisciplinary Studies, National Yunlin University of Science and Technology, Douliu, Taiwan
| | - Amir Mosavi
- John von Neumann Faculty of Informatics, Obuda University, Budapest, Hungary
- Institute of Information Engineering, Automation and Mathematics, Slovak University of Technology in Bratislava, Bratislava, Slovakia
| | - Massoud Moslehpour
- Department of Business Administration, College of Management, Asia University, Taichung, Taiwan
- Department of Management, California State University, San Bernardino, CA, United States
| |
Collapse
|
138
|
Kanwar VS, Sharma A, Rinku, Kanwar M, Srivastav AL, Soni DK. An overview for biomedical waste management during pandemic like COVID-19. INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY : IJEST 2022; 20:8025-8040. [PMID: 35694150 PMCID: PMC9167668 DOI: 10.1007/s13762-022-04287-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 02/01/2022] [Accepted: 05/10/2022] [Indexed: 06/12/2023]
Abstract
Amid COVID-19, world has gone under environmental reformation in terms of clean rivers and blue skies, whereas, generation of biomedical waste management has emerged as a big threat for the whole world, especially in the developing nations. Appropriate biomedical waste management has become a prime concern worldwide in the pandemic era of COVID-19 as it may affect environment and living organisms up to a great extent. The problem has been increased many folds because of unexpected generations of hazardous biomedical waste which needs extraordinary attentions. In this paper, the impacts and future challenges of solid waste management especially the biomedical waste management on environment and human beings have been discussed amid COVID-19 pandemic. The paper also recommends some guidelines to manage the bulk of medical wastes for the protection of human health and environment. The paper summarizes better management practices for the wastes including optimizing the decision process, infrastructure, upgrading treatment methods and other activities related with the biological disasters like COVID-19. As achieved in the past for viral disinfection, use of UV- rays with proper precautions can also be explored for COVID-19 disinfection. For biomedical waste management, thermal treatment of waste can be an alternative, as it can generate energy along with reducing waste volume by 80-95%. The Asian Development Bank observed that additional biomedical waste was generated ranged from 154 to 280 tons/day during the peak of COVID-19 pandemic in Asian megacities such as Manila, Jakarta, Wuhan, Bangkok, Hanoi, Kuala Lumpur.
Collapse
Affiliation(s)
- V. S. Kanwar
- Chitkara University School of Engineering and Technology, Chitkara University, Solan, Himachal Pradesh India
| | - A. Sharma
- Chitkara University School of Engineering and Technology, Chitkara University, Solan, Himachal Pradesh India
| | - Rinku
- Chitkara University School of Computer Applications, Chitkara University, Solan, Himachal Pradesh India
| | - M. Kanwar
- Chitkara University School of Engineering and Technology, Chitkara University, Solan, Himachal Pradesh India
- Chitkara University School of Computer Applications, Chitkara University, Solan, Himachal Pradesh India
- Central Pollution Control Board, Lucknow, India
| | - A. L. Srivastav
- Chitkara University School of Engineering and Technology, Chitkara University, Solan, Himachal Pradesh India
| | - D. K. Soni
- Central Pollution Control Board, Lucknow, India
| |
Collapse
|
139
|
Intracerebral hemorrhage detection on computed tomography images using a residual neural network. Phys Med 2022; 99:113-119. [PMID: 35671679 DOI: 10.1016/j.ejmp.2022.05.015] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Revised: 04/23/2022] [Accepted: 05/26/2022] [Indexed: 01/31/2023] Open
Abstract
Intracerebral hemorrhage (ICH) is a high mortality rate, critical medical injury, produced by the rupture of a blood vessel of the vascular system inside the skull. ICH can lead to paralysis and even death. Therefore, it is considered a clinically dangerous disease that needs to be treated quickly. Thanks to the advancement in machine learning and the computing power of today's microprocessors, deep learning has become an unbelievably valuable tool for detecting diseases, in particular from medical images. In this work, we are interested in differentiating computer tomography (CT) images of healthy brains and ICH using a ResNet-18, a deep residual convolutional neural network. In addition, the gradient-weighted class activation mapping (Grad-CAM) technique was employed to visually explore and understand the network's decisions. The generalizability of the detector was assessed through a 100-iteration Monte Carlo cross-validation (80% of the data for training and 20% for test). In a database with 200 CT images of brains (100 with ICH and 100 without ICH), the detector yielded, on average, 95.93%accuracy, 96.20% specificity, 95.65% sensitivity, 96.40% precision, and 95.91% F1-core, with an average computing time of 165.90 s to train the network (on 160 images) and 1.17 s to test it with 40 CT images. These results are comparable with the state of the art with a simpler and lower computational load approach. Our detector could assist physicians in their medical decision, in resource optimization and in reducing the time and error in the diagnosis of ICH.
Collapse
|
140
|
Yao MMS, Du H, Hartman M, Chan WP, Feng M. End-to-End Calcification Distribution Pattern Recognition for Mammograms: An Interpretable Approach with GNN. Diagnostics (Basel) 2022; 12:diagnostics12061376. [PMID: 35741186 PMCID: PMC9222096 DOI: 10.3390/diagnostics12061376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 05/21/2022] [Accepted: 05/30/2022] [Indexed: 12/09/2022] Open
Abstract
Purpose: We aimed to develop a novel interpretable artificial intelligence (AI) model algorithm focusing on automatic detection and classification of various patterns of calcification distribution in mammographic images using a unique graph convolution approach. Materials and methods: Images from 292 patients, which showed calcifications according to the mammographic reports and diagnosed breast cancers, were collected. The calcification distributions were classified as diffuse, segmental, regional, grouped, or linear. Excluded were mammograms with (1) breast cancer with multiple lexicons such as mass, asymmetry, or architectural distortion without calcifications; (2) hidden calcifications that were difficult to mark; or (3) incomplete medical records. Results: A graph-convolutional-network-based model was developed. A total of 581 mammographic images from 292 cases of breast cancer were divided based on the calcification distribution pattern: diffuse (n = 67), regional (n = 115), group (n = 337), linear (n = 8), or segmental (n = 54). The classification performances were measured using metrics including precision, recall, F1 score, accuracy, and multi-class area under the receiver operating characteristic curve. The proposed model achieved a precision of 0.522 ± 0.028, sensitivity of 0.643 ± 0.017, specificity of 0.847 ± 0.009, F1 score of 0.559 ± 0.018, accuracy of 64.325 ± 1.694%, and area under the curve of 0.745 ± 0.030; thus, the method was found to be superior compared to all baseline models. The predicted linear and diffuse classifications were highly similar to the ground truth, and the predicted grouped and regional classifications were also superior compared to baseline models. The prediction results are interpretable using visualization methods to highlight the important calcification nodes in graphs. Conclusions: The proposed deep neural network framework is an AI solution that automatically detects and classifies calcification distribution patterns on mammographic images highly suspected of showing breast cancers. Further study of the AI model in an actual clinical setting and additional data collection will improve its performance.
Collapse
Affiliation(s)
- Melissa Min-Szu Yao
- Department of Radiology, Wan Fang Hospital, Taipei Medical University, Taipei 116, Taiwan; (M.M.-S.Y.); (M.F.)
- Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan
| | - Hao Du
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore 117549, Singapore;
- National University Health System, Singapore 119228, Singapore
- Correspondence: (H.D.); (W.P.C.); Tel.: +65-9681-2766 (H.D.); +886-2930-7930 (ext. 1300) (W.P.C.)
| | - Mikael Hartman
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore 117549, Singapore;
- National University Health System, Singapore 119228, Singapore
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117549, Singapore
| | - Wing P. Chan
- Department of Radiology, Wan Fang Hospital, Taipei Medical University, Taipei 116, Taiwan; (M.M.-S.Y.); (M.F.)
- Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan
- Medical Innovation Development Center, Wan Fang Hospital, Taipei Medical University, Taipei 116, Taiwan
- Correspondence: (H.D.); (W.P.C.); Tel.: +65-9681-2766 (H.D.); +886-2930-7930 (ext. 1300) (W.P.C.)
| | - Mengling Feng
- Department of Radiology, Wan Fang Hospital, Taipei Medical University, Taipei 116, Taiwan; (M.M.-S.Y.); (M.F.)
- National University Health System, Singapore 119228, Singapore
- Institute of Data Science, National University of Singapore, Singapore 117602, Singapore
| |
Collapse
|
141
|
Garg A, Salehi S, Rocca ML, Garner R, Duncan D. Efficient and visualizable convolutional neural networks for COVID-19 classification using Chest CT. EXPERT SYSTEMS WITH APPLICATIONS 2022; 195:116540. [PMID: 35075334 PMCID: PMC8769906 DOI: 10.1016/j.eswa.2022.116540] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 08/17/2021] [Accepted: 01/10/2022] [Indexed: 05/02/2023]
Abstract
With coronavirus disease 2019 (COVID-19) cases rising rapidly, deep learning has emerged as a promising diagnosis technique. However, identifying the most accurate models to characterize COVID-19 patients is challenging because comparing results obtained with different types of data and acquisition processes is non-trivial. In this paper we designed, evaluated, and compared the performance of 20 convolutional neutral networks in classifying patients as COVID-19 positive, healthy, or suffering from other pulmonary lung infections based on chest computed tomography (CT) scans, serving as the first to consider the EfficientNet family for COVID-19 diagnosis and employ intermediate activation maps for visualizing model performance. All models are trained and evaluated in Python using 4173 chest CT images from the dataset entitled "A COVID multiclass dataset of CT scans," with 2168, 758, and 1247 images of patients that are COVID-19 positive, healthy, or suffering from other pulmonary infections, respectively. EfficientNet-B5 was identified as the best model with an F1 score of 0.9769 ± 0.0046, accuracy of 0.9759 ± 0.0048, sensitivity of 0.9788 ± 0.0055, specificity of 0.9730 ± 0.0057, and precision of 0.9751 ± 0.0051. On an alternate 2-class dataset, EfficientNetB5 obtained an accuracy of 0.9845 ± 0.0109, F1 score of 0.9599 ± 0.0251, sensitivity of 0.9682 ± 0.0099, specificity of 0.9883 ± 0.0150, and precision of 0.9526 ± 0.0523. Intermediate activation maps and Gradient-weighted Class Activation Mappings offered human-interpretable evidence of the model's perception of ground-class opacities and consolidations, hinting towards a promising use-case of artificial intelligence-assisted radiology tools. With a prediction speed of under 0.1 s on GPUs and 0.5 s on CPUs, our proposed model offers a rapid, scalable, and accurate diagnostic for COVID-19.
Collapse
Affiliation(s)
- Aksh Garg
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, 2025 Zonal Avenue, Los Angeles, CA, USA
- Stanford University, 450 Serra Mall, Stanford, California, USA
| | - Sana Salehi
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, 2025 Zonal Avenue, Los Angeles, CA, USA
| | - Marianna La Rocca
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, 2025 Zonal Avenue, Los Angeles, CA, USA
- Dipartimento Interateneo di Fisica, Università di Bari, Bari, Italy
| | - Rachael Garner
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, 2025 Zonal Avenue, Los Angeles, CA, USA
| | - Dominique Duncan
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, 2025 Zonal Avenue, Los Angeles, CA, USA
| |
Collapse
|
142
|
Generalizability assessment of COVID-19 3D CT data for deep learning-based disease detection. Comput Biol Med 2022; 145:105464. [PMID: 35390746 PMCID: PMC8971071 DOI: 10.1016/j.compbiomed.2022.105464] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 03/25/2022] [Accepted: 03/25/2022] [Indexed: 12/16/2022]
Abstract
BACKGROUND Artificial intelligence technologies in classification/detection of COVID-19 positive cases suffer from generalizability. Moreover, accessing and preparing another large dataset is not always feasible and time-consuming. Several studies have combined smaller COVID-19 CT datasets into "supersets" to maximize the number of training samples. This study aims to assess generalizability by splitting datasets into different portions based on 3D CT images using deep learning. METHOD Two large datasets, including 1110 3D CT images, were split into five segments of 20% each. Each dataset's first 20% segment was separated as a holdout test set. 3D-CNN training was performed with the remaining 80% from each dataset. Two small external datasets were also used to independently evaluate the trained models. RESULTS The total combination of 80% of each dataset has an accuracy of 91% on Iranmehr and 83% on Moscow holdout test datasets. Results indicated that 80% of the primary datasets are adequate for fully training a model. The additional fine-tuning using 40% of a secondary dataset helps the model generalize to a third, unseen dataset. The highest accuracy achieved through transfer learning was 85% on LDCT dataset and 83% on Iranmehr holdout test sets when retrained on 80% of Iranmehr dataset. CONCLUSION While the total combination of both datasets produced the best results, different combinations and transfer learning still produced generalizable results. Adopting the proposed methodology may help to obtain satisfactory results in the case of limited external datasets.
Collapse
|
143
|
Mehboob F, Rauf A, Jiang R, Saudagar AKJ, Malik KM, Khan MB, Hasnat MHA, AlTameem A, AlKhathami M. Towards robust diagnosis of COVID-19 using vision self-attention transformer. Sci Rep 2022; 12:8922. [PMID: 35618740 PMCID: PMC9134987 DOI: 10.1038/s41598-022-13039-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Accepted: 05/16/2022] [Indexed: 01/31/2023] Open
Abstract
The outbreak of COVID-19, since its appearance, has affected about 200 countries and endangered millions of lives. COVID-19 is extremely contagious disease, and it can quickly incapacitate the healthcare systems if infected cases are not handled timely. Several Conventional Neural Networks (CNN) based techniques have been developed to diagnose the COVID-19. These techniques require a large, labelled dataset to train the algorithm fully, but there are not too many labelled datasets. To mitigate this problem and facilitate the diagnosis of COVID-19, we developed a self-attention transformer-based approach having self-attention mechanism using CT slices. The architecture of transformer can exploit the ample unlabelled datasets using pre-training. The paper aims to compare the performances of self-attention transformer-based approach with CNN and Ensemble classifiers for diagnosis of COVID-19 using binary Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection and multi-class Hybrid-learning for UnbiaSed predicTion of COVID-19 (HUST-19) CT scan dataset. To perform this comparison, we have tested Deep learning-based classifiers and ensemble classifiers with proposed approach using CT scan images. Proposed approach is more effective in detection of COVID-19 with an accuracy of 99.7% on multi-class HUST-19, whereas 98% on binary class SARS-CoV-2 dataset. Cross corpus evaluation achieves accuracy of 93% by training the model with Hust19 dataset and testing using Brazilian COVID dataset.
Collapse
Affiliation(s)
| | | | - Richard Jiang
- LIRA Center, Lancaster University, Lancaster, LA1 4YW, UK
| | - Abdul Khader Jilani Saudagar
- Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia.
| | - Khalid Mahmood Malik
- Department of Computer Science and Engineering, Oakland University, Rochester, MI, USA
| | - Muhammad Badruddin Khan
- Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
| | - Mozaherul Hoque Abdul Hasnat
- Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
| | - Abdullah AlTameem
- Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
| | - Mohammed AlKhathami
- Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
| |
Collapse
|
144
|
Neural Networks to Recognize Patterns in Topographic Images of Cortical Electrical Activity of Patients with Neurological Diseases. Brain Topogr 2022; 35:464-480. [PMID: 35596851 DOI: 10.1007/s10548-022-00901-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Accepted: 04/25/2022] [Indexed: 11/02/2022]
Abstract
Software such as EEGLab has enabled the treatment and visualization of the tracing and cortical topography of the electroencephalography (EEG) signals. In particular, the topography of the cortical electrical activity is represented by colors, which make it possible to identify functional differences between cortical areas and to associate them with various diseases. The use of cortical topography with EEG origin in the investigation of diseases is often not used due to the representation of colors making it difficult to classify the disease. Thus, the analyses have been carried out, mainly, based on the EEG tracings. Therefore, a computer system that recognizes disease patterns through cortical topography can be a solution to the diagnostic aid. In view of this, this study compared five models of Convolutional Neural Networks (CNNs), namely: Inception v3, SqueezeNet, LeNet, VGG-16 and VGG-19, in order to know the patterns in cortical topography images obtained with EEG, in Parkinson's disease, Depression and Bipolar Disorder. SqueezeNet performed better in the 3 diseases analyzed, with Parkinson's disease being better evaluated for Accuracy (88.89%), Precison (86.36%), Recall (91.94%) and F1 Score (89.06%), the other CNNs had less performance. In the analysis of the values of the Area under ROC Curve (AUC), SqueezeNet reached (93.90%) for Parkinson's disease, (75.70%) for Depression and (72.10%) for Bipolar Disorder. We understand that there is the possibility of classifying neurological diseases from cortical topographies with the use of CNNs and, thus, creating a computational basis for the implementation of software for screening and possible diagnostic assistance.
Collapse
|
145
|
Meraihi Y, Gabis AB, Mirjalili S, Ramdane-Cherif A, Alsaadi FE. Machine Learning-Based Research for COVID-19 Detection, Diagnosis, and Prediction: A Survey. SN COMPUTER SCIENCE 2022; 3:286. [PMID: 35578678 PMCID: PMC9096341 DOI: 10.1007/s42979-022-01184-z] [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: 01/09/2022] [Accepted: 04/30/2022] [Indexed: 12/12/2022]
Abstract
The year 2020 experienced an unprecedented pandemic called COVID-19, which impacted the whole world. The absence of treatment has motivated research in all fields to deal with it. In Computer Science, contributions mainly include the development of methods for the diagnosis, detection, and prediction of COVID-19 cases. Data science and Machine Learning (ML) are the most widely used techniques in this area. This paper presents an overview of more than 160 ML-based approaches developed to combat COVID-19. They come from various sources like Elsevier, Springer, ArXiv, MedRxiv, and IEEE Xplore. They are analyzed and classified into two categories: Supervised Learning-based approaches and Deep Learning-based ones. In each category, the employed ML algorithm is specified and a number of used parameters is given. The parameters set for each of the algorithms are gathered in different tables. They include the type of the addressed problem (detection, diagnosis, or detection), the type of the analyzed data (Text data, X-ray images, CT images, Time series, Clinical data,...) and the evaluated metrics (accuracy, precision, sensitivity, specificity, F1-Score, and AUC). The study discusses the collected information and provides a number of statistics drawing a picture about the state of the art. Results show that Deep Learning is used in 79% of cases where 65% of them are based on the Convolutional Neural Network (CNN) and 17% use Specialized CNN. On his side, supervised learning is found in only 16% of the reviewed approaches and only Random Forest, Support Vector Machine (SVM) and Regression algorithms are employed.
Collapse
Affiliation(s)
- Yassine Meraihi
- LIST Laboratory, University of M'Hamed Bougara Boumerdes, Avenue of Independence, 35000 Boumerdes, Algeria
| | - Asma Benmessaoud Gabis
- Ecole nationale Supérieure d'Informatique, Laboratoire des Méthodes de Conception des Systèmes, BP 68 M, 16309 Oued-Smar, Alger Algeria
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Fortitude Valley, Brisbane, QLD 4006 Australia.,Yonsei Frontier Lab, Yonsei University, Seoul, Korea
| | - Amar Ramdane-Cherif
- LISV Laboratory, University of Versailles St-Quentin-en-Yvelines, 10-12 Avenue of Europe, 78140 Velizy, France
| | - Fawaz E Alsaadi
- Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| |
Collapse
|
146
|
Jamali M, Mohajer S, Sheikhlary S, Ara MHM. Z-scan optical method complements the Thioflavin T assay for investigation of anti-Alzheimer's impact of polyphenols. Photodiagnosis Photodyn Ther 2022; 39:102914. [PMID: 35595186 DOI: 10.1016/j.pdpdt.2022.102914] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 04/26/2022] [Accepted: 05/16/2022] [Indexed: 12/20/2022]
Abstract
Polyphenols are tremendously effective in eliminating the amyloid-beta aggregations, the main hallmark of Alzheimer's disease. In recent years various nano drugs and biomaterials based on polyphenolic compounds have been synthetized to treat or prevent Alzheimer's disease, and the main in-vitro approach to investigate the anti-Alzheimer's properties of materials, is Thioflavin T assay. In spite of being very helpful, it has some drawbacks and cannot guarantee the accuracy of data, specifically in case of polyphenolic compounds; thus, rendering accurate results requires utilizing other assays along with Thioflavin T. In this experiment, we introduced Z-scan technique as a complementary test for Thioflavin T assay. In this study, the anti-Alzheimer's properties of two polyphenols quercetin and fulvic acid were assessed in the presence and absences of silver nanoparticles at various concentrations, both via Z-scan technique and Thioflavin T assay, after which the two tests were aligned with each other. The polyphenols' non-linear refractive indices obtained by the Z-scan technique correlated well with their related fluorescence intensities from the Thioflavin T assay in such a way that, the smaller the magnitude of the non-linear refractive indices, the stronger the anti-amyloidogenic impact. Our work shows that Z-scan could be used along with Thioflavin T for better investigation of polyphenols' anti-Alzheimer's properties.
Collapse
Affiliation(s)
- Mohammad Jamali
- Biophotonics Lab, Faculty of Physics, Kharazmi University, Karaj 31979-37551, Iran
| | - Salman Mohajer
- Biophotonics Lab, Faculty of Physics, Kharazmi University, Karaj 31979-37551, Iran; Applied Science Research Center, Kharazmi University, Karaj 31979-37551, Iran
| | - Sara Sheikhlary
- Faculty of Biological Sciences, Kharazmi University, Karaj 31979-37551, Iran
| | - Mohammad Hossien Majles Ara
- Biophotonics Lab, Faculty of Physics, Kharazmi University, Karaj 31979-37551, Iran; Applied Science Research Center, Kharazmi University, Karaj 31979-37551, Iran
| |
Collapse
|
147
|
Simon J, Grodecki K, Cadet S, Killekar A, Slomka P, Zara SJ, Zsarnóczay E, Nardocci C, Nagy N, Kristóf K, Vásárhelyi B, Müller V, Merkely B, Dey D, Maurovich-Horvat P. Radiomorphological signs and clinical severity of SARS-CoV-2 lineage B.1.1.7. BJR Open 2022; 4:20220016. [PMID: 36452055 PMCID: PMC9667478 DOI: 10.1259/bjro.20220016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 04/09/2022] [Indexed: 11/05/2022] Open
Abstract
Objective We aimed to assess the differences in the severity and chest-CT radiomorphological signs of SARS-CoV-2 B.1.1.7 and non-B.1.1.7 variants. Methods We collected clinical data of consecutive patients with laboratory-confirmed COVID-19 and chest-CT imaging who were admitted to the Emergency Department between September 1- November 13, 2020 (non-B.1.1.7 cases) and March 1-March 18, 2021 (B.1.1.7 cases). We also examined the differences in the severity and radiomorphological features associated with COVID-19 pneumonia. Total pneumonia burden (%), mean attenuation of ground-glass opacities and consolidation were quantified using deep-learning research software. Results The final population comprised 500 B.1.1.7 and 500 non-B.1.1.7 cases. Patients with B.1.1.7 infection were younger (58.5 ± 15.6 vs 64.8 ± 17.3; p < .001) and had less comorbidities. Total pneumonia burden was higher in the B.1.1.7 patient group (16.1% [interquartile range (IQR):6.0-34.2%] vs 6.6% [IQR:1.2-18.3%]; p < .001). In the age-specific analysis, in patients <60 years B.1.1.7 pneumonia had increased consolidation burden (0.1% [IQR:0.0-0.7%] vs 0.1% [IQR:0.0-0.2%]; p < .001), and severe COVID-19 was more prevalent (11.5% vs 4.9%; p = .032). Mortality rate was similar in all age groups. Conclusion Despite B.1.1.7 patients were younger and had fewer comorbidities, they experienced more severe disease than non-B.1.1.7 patients, however, the risk of death was the same between the two groups. Advances in knowledge Our study provides data on deep-learning based quantitative lung lesion burden and clinical outcomes of patients infected by B.1.1.7 VOC. Our findings might serve as a model for later investigations, as new variants are emerging across the globe.
Collapse
Affiliation(s)
| | | | - Sebastian Cadet
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, USA
| | - Aditya Killekar
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, USA
| | - Piotr Slomka
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, USA
| | | | | | - Chiara Nardocci
- Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Norbert Nagy
- Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Katalin Kristóf
- Department of Laboratory Medicine, Semmelweis University, Budapest, Hungary
| | - Barna Vásárhelyi
- Department of Laboratory Medicine, Semmelweis University, Budapest, Hungary
| | - Veronika Müller
- Department of Pulmonology, Semmelweis University, Budapest, Hungary
| | - Béla Merkely
- MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, Budapest, Hungary
| | - Damini Dey
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, USA
| | | |
Collapse
|
148
|
Galetsi P, Katsaliaki K, Kumar S. The medical and societal impact of big data analytics and artificial intelligence applications in combating pandemics: A review focused on Covid-19. Soc Sci Med 2022; 301:114973. [PMID: 35452893 PMCID: PMC9001170 DOI: 10.1016/j.socscimed.2022.114973] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 02/21/2022] [Accepted: 04/08/2022] [Indexed: 12/23/2022]
Abstract
With Covid-19 impacting communities in different ways, research has increasingly turned to big data analytics (BDA) and artificial intelligence (AI) tools to track and monitor the virus's spread and its effect on humanity and the global economy. The purpose of this study is to conduct an in-depth literature review to identify how BDA and AI were involved in the management of Covid-19 (while considering diversity, equity, and inclusion (DEI)). The rigorous search resulted in a portfolio of 607 articles, retrieved from the Web of Science database, where content analysis has been conducted. This study identifies the BDA and AI applications developed to deal with the initial Covid-19 outbreak and the containment of the pandemic, along with their benefits for the social good. Moreover, this study reveals the DEI challenges related to these applications, ways to mitigate the concerns, and how to develop viable techniques to deal with similar crises in the future. The article pool recognized the high presence of machine learning (ML) and the role of mobile technology, social media and telemedicine in the use of BDA and AI during Covid-19. This study offers a collective insight into many of the key issues and underlying complexities affecting public health and society from Covid-19, and the solutions offered from information systems and technological perspectives.
Collapse
Affiliation(s)
- Panagiota Galetsi
- School of Humanities, Social Sciences and Economics, International Hellenic University, 14th Km Thessaloniki-N.Moudania, Thessaloniki, 57001, Greece
| | - Korina Katsaliaki
- School of Humanities, Social Sciences and Economics, International Hellenic University, 14th Km Thessaloniki-N.Moudania, Thessaloniki, 57001, Greece
| | - Sameer Kumar
- Opus College of Business, University of St. Thomas Minneapolis Campus 1000 LaSalle Ave, Schulze Hall 333, Minneapolis, MN, 55403, USA.
| |
Collapse
|
149
|
Hassan H, Ren Z, Zhou C, Khan MA, Pan Y, Zhao J, Huang B. Supervised and weakly supervised deep learning models for COVID-19 CT diagnosis: A systematic review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 218:106731. [PMID: 35286874 PMCID: PMC8897838 DOI: 10.1016/j.cmpb.2022.106731] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 01/28/2022] [Accepted: 03/03/2022] [Indexed: 05/05/2023]
Abstract
Artificial intelligence (AI) and computer vision (CV) methods become reliable to extract features from radiological images, aiding COVID-19 diagnosis ahead of the pathogenic tests and saving critical time for disease management and control. Thus, this review article focuses on cascading numerous deep learning-based COVID-19 computerized tomography (CT) imaging diagnosis research, providing a baseline for future research. Compared to previous review articles on the topic, this study pigeon-holes the collected literature very differently (i.e., its multi-level arrangement). For this purpose, 71 relevant studies were found using a variety of trustworthy databases and search engines, including Google Scholar, IEEE Xplore, Web of Science, PubMed, Science Direct, and Scopus. We classify the selected literature in multi-level machine learning groups, such as supervised and weakly supervised learning. Our review article reveals that weak supervision has been adopted extensively for COVID-19 CT diagnosis compared to supervised learning. Weakly supervised (conventional transfer learning) techniques can be utilized effectively for real-time clinical practices by reusing the sophisticated features rather than over-parameterizing the standard models. Few-shot and self-supervised learning are the recent trends to address data scarcity and model efficacy. The deep learning (artificial intelligence) based models are mainly utilized for disease management and control. Therefore, it is more appropriate for readers to comprehend the related perceptive of deep learning approaches for the in-progress COVID-19 CT diagnosis research.
Collapse
Affiliation(s)
- Haseeb Hassan
- College of Big data and Internet, Shenzhen Technology University, Shenzhen, 518118, China; Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Health Science Center, Shenzhen, China; College of Applied Sciences, Shenzhen University, Shenzhen, 518060, China
| | - Zhaoyu Ren
- College of Big data and Internet, Shenzhen Technology University, Shenzhen, 518118, China
| | - Chengmin Zhou
- College of Big data and Internet, Shenzhen Technology University, Shenzhen, 518118, China
| | - Muazzam A Khan
- Department of Computer Sciences, Quaid-i-Azam University, Islamabad, Pakistan
| | - Yi Pan
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China
| | - Jian Zhao
- College of Big data and Internet, Shenzhen Technology University, Shenzhen, 518118, China.
| | - Bingding Huang
- College of Big data and Internet, Shenzhen Technology University, Shenzhen, 518118, China.
| |
Collapse
|
150
|
Aggarwal P, Mishra NK, Fatimah B, Singh P, Gupta A, Joshi SD. COVID-19 image classification using deep learning: Advances, challenges and opportunities. Comput Biol Med 2022; 144:105350. [PMID: 35305501 PMCID: PMC8890789 DOI: 10.1016/j.compbiomed.2022.105350] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 02/10/2022] [Accepted: 02/22/2022] [Indexed: 12/16/2022]
Abstract
Corona Virus Disease-2019 (COVID-19), caused by Severe Acute Respiratory Syndrome-Corona Virus-2 (SARS-CoV-2), is a highly contagious disease that has affected the lives of millions around the world. Chest X-Ray (CXR) and Computed Tomography (CT) imaging modalities are widely used to obtain a fast and accurate diagnosis of COVID-19. However, manual identification of the infection through radio images is extremely challenging because it is time-consuming and highly prone to human errors. Artificial Intelligence (AI)-techniques have shown potential and are being exploited further in the development of automated and accurate solutions for COVID-19 detection. Among AI methodologies, Deep Learning (DL) algorithms, particularly Convolutional Neural Networks (CNN), have gained significant popularity for the classification of COVID-19. This paper summarizes and reviews a number of significant research publications on the DL-based classification of COVID-19 through CXR and CT images. We also present an outline of the current state-of-the-art advances and a critical discussion of open challenges. We conclude our study by enumerating some future directions of research in COVID-19 imaging classification.
Collapse
Affiliation(s)
| | | | - Binish Fatimah
- The Department of ECE, CMR Institute of Technology, Bengaluru, India
| | - Pushpendra Singh
- The Department of ECE, National Institute of Technology Hamirpur, HP, India,Corresponding author
| | - Anubha Gupta
- The Department of ECE, IIIT-Delhi, Delhi, 110020, India
| | - Shiv Dutt Joshi
- The Department of EE, Indian Institute of Technology Delhi, Delhi 110016, India
| |
Collapse
|