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Hussein AM, Sharifai AG, Alia OM, Abualigah L, Almotairi KH, Abujayyab SKM, Gandomi AH. Auto-detection of the coronavirus disease by using deep convolutional neural networks and X-ray photographs. Sci Rep 2024; 14:534. [PMID: 38177156 PMCID: PMC10766625 DOI: 10.1038/s41598-023-47038-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 11/08/2023] [Indexed: 01/06/2024] Open
Abstract
The most widely used method for detecting Coronavirus Disease 2019 (COVID-19) is real-time polymerase chain reaction. However, this method has several drawbacks, including high cost, lengthy turnaround time for results, and the potential for false-negative results due to limited sensitivity. To address these issues, additional technologies such as computed tomography (CT) or X-rays have been employed for diagnosing the disease. Chest X-rays are more commonly used than CT scans due to the widespread availability of X-ray machines, lower ionizing radiation, and lower cost of equipment. COVID-19 presents certain radiological biomarkers that can be observed through chest X-rays, making it necessary for radiologists to manually search for these biomarkers. However, this process is time-consuming and prone to errors. Therefore, there is a critical need to develop an automated system for evaluating chest X-rays. Deep learning techniques can be employed to expedite this process. In this study, a deep learning-based method called Custom Convolutional Neural Network (Custom-CNN) is proposed for identifying COVID-19 infection in chest X-rays. The Custom-CNN model consists of eight weighted layers and utilizes strategies like dropout and batch normalization to enhance performance and reduce overfitting. The proposed approach achieved a classification accuracy of 98.19% and aims to accurately classify COVID-19, normal, and pneumonia samples.
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Affiliation(s)
- Ahmad MohdAziz Hussein
- Department of Computer Science, Faculty of Information Technology, Middle East University, Amman, Jordan.
| | - Abdulrauf Garba Sharifai
- Department of Computer Sciences, Yusuf Maitama Sule University, Kofar Nassarawa, Kano, 700222, Nigeria
| | - Osama Moh'd Alia
- Department of Computer Science, Faculty of Computes and Information Technology, University of Tabuk, 71491, Tabuk, Saudi Arabia
| | - Laith Abualigah
- Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al Al-Bayt University, Mafraq, 25113, Jordan
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos, 13-5053, Lebanon
- Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328, Jordan
- Applied Science Research Center, Applied Science Private University, Amman, 11931, Jordan
- School of Engineering and Technology, Sunway University Malaysia, 27500, Petaling Jaya, Malaysia
- School of Computer Sciences, Universiti Sains Malaysia, 11800, Pulau Pinang, Malaysia
| | - Khaled H Almotairi
- Computer Engineering Department, Computer and Information Systems College, Umm Al-Qura University, 21955, Makkah, Saudi Arabia
| | | | - Amir H Gandomi
- Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, 2007, Australia.
- University Research and Innovation Center (EKIK), Óbuda University, Budapest, 1034, Hungary.
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Asif S, Zhao M, Chen X, Zhu Y. StoneNet: An Efficient Lightweight Model Based on Depthwise Separable Convolutions for Kidney Stone Detection from CT Images. Interdiscip Sci 2023; 15:633-652. [PMID: 37452930 DOI: 10.1007/s12539-023-00578-8] [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: 03/15/2023] [Revised: 06/27/2023] [Accepted: 06/30/2023] [Indexed: 07/18/2023]
Abstract
Kidney stone disease is one of the most common and serious health problems in much of the world, leading to many hospitalizations with severe pain. Detecting small stones is difficult and time-consuming, so an early diagnosis of kidney disease is needed to prevent the loss of kidney failure. Recent advances in artificial intelligence (AI) found to be very successful in the diagnosis of various diseases in the biomedical field. However, existing models using deep networks have several problems, such as high computational cost, long training time, and huge parameters. Providing a low-cost solution for diagnosing kidney stones in a medical decision support system is of paramount importance. Therefore, in this study, we propose "StoneNet", a lightweight and high-performance model for the detection of kidney stones based on MobileNet using depthwise separable convolution. The proposed model includes a combination of global average pooling (GAP), batch normalization, dropout layer, and dense layers. Our study shows that using GAP instead of flattening layers greatly improves the robustness of the model by significantly reducing the parameters. The developed model is benchmarked against four pre-trained models as well as the state-of-the-art heavy model. The results show that the proposed model can achieve the highest accuracy of 97.98%, and only requires training and testing time of 996.88 s and 14.62 s. Several parameters, such as different batch sizes and optimizers, were considered to validate the proposed model. The proposed model is computationally faster and provides optimal performance than other considered models. Experiments on a large kidney dataset of 1799 CT images show that StoneNet has superior performance in terms of higher accuracy and lower complexity. The proposed model can assist the radiologist in faster diagnosis of kidney stones and has great potential for deployment in real-time applications.
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Affiliation(s)
- Sohaib Asif
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Ming Zhao
- School of Computer Science and Engineering, Central South University, Changsha, China.
| | - Xuehan Chen
- School of Computer Science and Engineering, Central South University, Changsha, China.
| | - Yusen Zhu
- School of Mathematics, Hunan University, Changsha, China
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Sharma A, Mishra PK. Covid-MANet: Multi-task attention network for explainable diagnosis and severity assessment of COVID-19 from CXR images. PATTERN RECOGNITION 2022; 131:108826. [PMID: 35698723 PMCID: PMC9170279 DOI: 10.1016/j.patcog.2022.108826] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 04/24/2022] [Accepted: 06/02/2022] [Indexed: 05/17/2023]
Abstract
The devastating outbreak of Coronavirus Disease (COVID-19) cases in early 2020 led the world to face health crises. Subsequently, the exponential reproduction rate of COVID-19 disease can only be reduced by early diagnosis of COVID-19 infection cases correctly. The initial research findings reported that radiological examinations using CT and CXR modality have successfully reduced false negatives by RT-PCR test. This research study aims to develop an explainable diagnosis system for the detection and infection region quantification of COVID-19 disease. The existing research studies successfully explored deep learning approaches with higher performance measures but lacked generalization and interpretability for COVID-19 diagnosis. In this study, we address these issues by the Covid-MANet network, an automated end-to-end multi-task attention network that works for 5 classes in three stages for COVID-19 infection screening. The first stage of the Covid-MANet network localizes attention of the model to the relevant lungs region for disease recognition. The second stage of the Covid-MANet network differentiates COVID-19 cases from bacterial pneumonia, viral pneumonia, normal and tuberculosis cases, respectively. To improve the interpretation and explainability, three experiments have been conducted in exploration of the most coherent and appropriate classification approach. Moreover, the multi-scale attention model MA-DenseNet201 proposed for the classification of COVID-19 cases. The final stage of the Covid-MANet network quantifies the proportion of infection and severity of COVID-19 in the lungs. The COVID-19 cases are graded into more specific severity levels such as mild, moderate, severe, and critical as per the score assigned by the RALE scoring system. The MA-DenseNet201 classification model outperforms eight state-of-the-art CNN models, in terms of sensitivity and interpretation with lung localization network. The COVID-19 infection segmentation by UNet with DenseNet121 encoder achieves dice score of 86.15% outperforming UNet, UNet++, AttentionUNet, R2UNet, with VGG16, ResNet50 and DenseNet201 encoder. The proposed network not only classifies images based on the predicted label but also highlights the infection by segmentation/localization of model-focused regions to support explainable decisions. MA-DenseNet201 model with a segmentation-based cropping approach achieves maximum interpretation of 96% with COVID-19 sensitivity of 97.75%. Finally, based on class-varied sensitivity analysis Covid-MANet ensemble network of MA-DenseNet201, ResNet50 and MobileNet achieve 95.05% accuracy and 98.75% COVID-19 sensitivity. The proposed model is externally validated on an unseen dataset, yields 98.17% COVID-19 sensitivity.
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Affiliation(s)
- Ajay Sharma
- Department of Computer Science, Institute of Science, Banaras Hindu University, Varanasi 221005, India
| | - Pramod Kumar Mishra
- Department of Computer Science, Institute of Science, Banaras Hindu University, Varanasi 221005, India
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Owais M, Baek NR, Park KR. DMDF-Net: Dual multiscale dilated fusion network for accurate segmentation of lesions related to COVID-19 in lung radiographic scans. EXPERT SYSTEMS WITH APPLICATIONS 2022; 202:117360. [PMID: 35529253 PMCID: PMC9057951 DOI: 10.1016/j.eswa.2022.117360] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 01/24/2022] [Accepted: 04/25/2022] [Indexed: 05/14/2023]
Abstract
The recent disaster of COVID-19 has brought the whole world to the verge of devastation because of its highly transmissible nature. In this pandemic, radiographic imaging modalities, particularly, computed tomography (CT), have shown remarkable performance for the effective diagnosis of this virus. However, the diagnostic assessment of CT data is a human-dependent process that requires sufficient time by expert radiologists. Recent developments in artificial intelligence have substituted several personal diagnostic procedures with computer-aided diagnosis (CAD) methods that can make an effective diagnosis, even in real time. In response to COVID-19, various CAD methods have been developed in the literature, which can detect and localize infectious regions in chest CT images. However, most existing methods do not provide cross-data analysis, which is an essential measure for assessing the generality of a CAD method. A few studies have performed cross-data analysis in their methods. Nevertheless, these methods show limited results in real-world scenarios without addressing generality issues. Therefore, in this study, we attempt to address generality issues and propose a deep learning-based CAD solution for the diagnosis of COVID-19 lesions from chest CT images. We propose a dual multiscale dilated fusion network (DMDF-Net) for the robust segmentation of small lesions in a given CT image. The proposed network mainly utilizes the strength of multiscale deep features fusion inside the encoder and decoder modules in a mutually beneficial manner to achieve superior segmentation performance. Additional pre- and post-processing steps are introduced in the proposed method to address the generality issues and further improve the diagnostic performance. Mainly, the concept of post-region of interest (ROI) fusion is introduced in the post-processing step, which reduces the number of false-positives and provides a way to accurately quantify the infected area of lung. Consequently, the proposed framework outperforms various state-of-the-art methods by accomplishing superior infection segmentation results with an average Dice similarity coefficient of 75.7%, Intersection over Union of 67.22%, Average Precision of 69.92%, Sensitivity of 72.78%, Specificity of 99.79%, Enhance-Alignment Measure of 91.11%, and Mean Absolute Error of 0.026.
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Affiliation(s)
- Muhammad Owais
- Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, South Korea
| | - Na Rae Baek
- Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, South Korea
| | - Kang Ryoung Park
- Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, South Korea
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Heidari A, Toumaj S, Navimipour NJ, Unal M. A privacy-aware method for COVID-19 detection in chest CT images using lightweight deep conventional neural network and blockchain. Comput Biol Med 2022; 145:105461. [PMID: 35366470 PMCID: PMC8958272 DOI: 10.1016/j.compbiomed.2022.105461] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 03/13/2022] [Accepted: 03/24/2022] [Indexed: 12/16/2022]
Abstract
With the global spread of the COVID-19 epidemic, a reliable method is required for identifying COVID-19 victims. The biggest issue in detecting the virus is a lack of testing kits that are both reliable and affordable. Due to the virus's rapid dissemination, medical professionals have trouble finding positive patients. However, the next real-life issue is sharing data with hospitals around the world while considering the organizations' privacy concerns. The primary worries for training a global Deep Learning (DL) model are creating a collaborative platform and personal confidentiality. Another challenge is exchanging data with health care institutions while protecting the organizations' confidentiality. The primary concerns for training a universal DL model are creating a collaborative platform and preserving privacy. This paper provides a model that receives a small quantity of data from various sources, like organizations or sections of hospitals, and trains a global DL model utilizing blockchain-based Convolutional Neural Networks (CNNs). In addition, we use the Transfer Learning (TL) technique to initialize layers rather than initialize randomly and discover which layers should be removed before selection. Besides, the blockchain system verifies the data, and the DL method trains the model globally while keeping the institution's confidentiality. Furthermore, we gather the actual and novel COVID-19 patients. Finally, we run extensive experiments utilizing Python and its libraries, such as Scikit-Learn and TensorFlow, to assess the proposed method. We evaluated works using five different datasets, including Boukan Dr. Shahid Gholipour hospital, Tabriz Emam Reza hospital, Mahabad Emam Khomeini hospital, Maragheh Dr.Beheshti hospital, and Miandoab Abbasi hospital datasets, and our technique outperform state-of-the-art methods on average in terms of precision (2.7%), recall (3.1%), F1 (2.9%), and accuracy (2.8%).
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Affiliation(s)
- Arash Heidari
- Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran,Department of Computer Engineering, Shabestar Branch, Islamic Azad University, Shabestar, Iran
| | - Shiva Toumaj
- Urmia University of Medical Sciences, Urmia, Iran
| | - Nima Jafari Navimipour
- Department of Computer Engineering, Kadir Has University, Istanbul, Turkey,Corresponding author
| | - Mehmet Unal
- Department of Computer Engineering, Nisantasi University, Istanbul, Turkey
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Sultan H, Owais M, Choi J, Mahmood T, Haider A, Ullah N, Park KR. Artificial Intelligence-Based Solution in Personalized Computer-Aided Arthroscopy of Shoulder Prostheses. J Pers Med 2022; 12:jpm12010109. [PMID: 35055427 PMCID: PMC8780458 DOI: 10.3390/jpm12010109] [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: 12/01/2021] [Revised: 12/30/2021] [Accepted: 01/07/2022] [Indexed: 01/01/2023] Open
Abstract
Background: Early recognition of prostheses before reoperation can reduce perioperative morbidity and mortality. Because of the intricacy of the shoulder biomechanics, accurate classification of implant models before surgery is fundamental for planning the correct medical procedure and setting apparatus for personalized medicine. Expert surgeons usually use X-ray images of prostheses to set the patient-specific apparatus. However, this subjective method is time-consuming and prone to errors. Method: As an alternative, artificial intelligence has played a vital role in orthopedic surgery and clinical decision-making for accurate prosthesis placement. In this study, three different deep learning-based frameworks are proposed to identify different types of shoulder implants in X-ray scans. We mainly propose an efficient ensemble network called the Inception Mobile Fully-Connected Convolutional Network (IMFC-Net), which is comprised of our two designed convolutional neural networks and a classifier. To evaluate the performance of the IMFC-Net and state-of-the-art models, experiments were performed with a public data set of 597 de-identified patients (597 shoulder implants). Moreover, to demonstrate the generalizability of IMFC-Net, experiments were performed with two augmentation techniques and without augmentation, in which our model ranked first, with a considerable difference from the comparison models. A gradient-weighted class activation map technique was also used to find distinct implant characteristics needed for IMFC-Net classification decisions. Results: The results confirmed that the proposed IMFC-Net model yielded an average accuracy of 89.09%, a precision rate of 89.54%, a recall rate of 86.57%, and an F1.score of 87.94%, which were higher than those of the comparison models. Conclusion: The proposed model is efficient and can minimize the revision complexities of implants.
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Owais M, Baek NR, Park KR. Domain-Adaptive Artificial Intelligence-Based Model for Personalized Diagnosis of Trivial Lesions Related to COVID-19 in Chest Computed Tomography Scans. J Pers Med 2021; 11:1008. [PMID: 34683149 PMCID: PMC8537687 DOI: 10.3390/jpm11101008] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Revised: 10/01/2021] [Accepted: 10/02/2021] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND Early and accurate detection of COVID-19-related findings (such as well-aerated regions, ground-glass opacity, crazy paving and linear opacities, and consolidation in lung computed tomography (CT) scan) is crucial for preventive measures and treatment. However, the visual assessment of lung CT scans is a time-consuming process particularly in case of trivial lesions and requires medical specialists. METHOD A recent breakthrough in deep learning methods has boosted the diagnostic capability of computer-aided diagnosis (CAD) systems and further aided health professionals in making effective diagnostic decisions. In this study, we propose a domain-adaptive CAD framework, namely the dilated aggregation-based lightweight network (DAL-Net), for effective recognition of trivial COVID-19 lesions in CT scans. Our network design achieves a fast execution speed (inference time is 43 ms on a single image) with optimal memory consumption (almost 9 MB). To evaluate the performances of the proposed and state-of-the-art models, we considered two publicly accessible datasets, namely COVID-19-CT-Seg (comprising a total of 3520 images of 20 different patients) and MosMed (including a total of 2049 images of 50 different patients). RESULTS Our method exhibits average area under the curve (AUC) up to 98.84%, 98.47%, and 95.51% for COVID-19-CT-Seg, MosMed, and cross-dataset, respectively, and outperforms various state-of-the-art methods. CONCLUSIONS These results demonstrate that deep learning-based models are an effective tool for building a robust CAD solution based on CT data in response to present disaster of COVID-19.
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Affiliation(s)
| | | | - Kang Ryoung Park
- Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Korea; (M.O.); (N.R.B.)
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