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Kiziloluk S, Sert E, Hammad M, Tadeusiewicz R, Pławiak P. EO-CNN: Equilibrium Optimization-Based hyperparameter tuning for enhanced pneumonia and COVID-19 detection using AlexNet and DarkNet19. Biocybern Biomed Eng 2024. [DOI: 10.1016/j.bbe.2024.06.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/23/2024]
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Talukder MA, Layek MA, Kazi M, Uddin MA, Aryal S. Empowering COVID-19 detection: Optimizing performance through fine-tuned EfficientNet deep learning architecture. Comput Biol Med 2024; 168:107789. [PMID: 38042105 DOI: 10.1016/j.compbiomed.2023.107789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 11/21/2023] [Accepted: 11/28/2023] [Indexed: 12/04/2023]
Abstract
The worldwide COVID-19 pandemic has profoundly influenced the health and everyday experiences of individuals across the planet. It is a highly contagious respiratory disease requiring early and accurate detection to curb its rapid transmission. Initial testing methods primarily revolved around identifying the genetic composition of the coronavirus, exhibiting a relatively low detection rate and requiring a time-intensive procedure. To address this challenge, experts have suggested using radiological imagery, particularly chest X-rays, as a valuable approach within the diagnostic protocol. This study investigates the potential of leveraging radiographic imaging (X-rays) with deep learning algorithms to swiftly and precisely identify COVID-19 patients. The proposed approach elevates the detection accuracy by fine-tuning with appropriate layers on various established transfer learning models. The experimentation was conducted on a COVID-19 X-ray dataset containing 2000 images. The accuracy rates achieved were impressive of 99.55%, 97.32%, 99.11%, 99.55%, 99.11% and 100% for Xception, InceptionResNetV2, ResNet50 , ResNet50V2, EfficientNetB0 and EfficientNetB4 respectively. The fine-tuned EfficientNetB4 achieved an excellent accuracy score, showcasing its potential as a robust COVID-19 detection model. Furthermore, EfficientNetB4 excelled in identifying Lung disease using Chest X-ray dataset containing 4,350 Images, achieving remarkable performance with an accuracy of 99.17%, precision of 99.13%, recall of 99.16%, and f1-score of 99.14%. These results highlight the promise of fine-tuned transfer learning for efficient lung detection through medical imaging, especially with X-ray images. This research offers radiologists an effective means of aiding rapid and precise COVID-19 diagnosis and contributes valuable assistance for healthcare professionals in accurately identifying affected patients.
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Affiliation(s)
- Md Alamin Talukder
- Department of Computer Science and Engineering, Jagannath University, Dhaka, Bangladesh.
| | - Md Abu Layek
- Department of Computer Science and Engineering, Jagannath University, Dhaka, Bangladesh.
| | - Mohsin Kazi
- Department of Pharmaceutics, College of Pharmacy, King Saud University, P.O. Box-2457, Riyadh 11451, Saudi Arabia.
| | - Md Ashraf Uddin
- School of Information Technology, Deakin University, Waurn Ponds Campus, Geelong, Australia.
| | - Sunil Aryal
- School of Information Technology, Deakin University, Waurn Ponds Campus, Geelong, Australia.
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Khan SH, Alahmadi TJ, Alsahfi T, Alsadhan AA, Mazroa AA, Alkahtani HK, Albanyan A, Sakr HA. COVID-19 infection analysis framework using novel boosted CNNs and radiological images. Sci Rep 2023; 13:21837. [PMID: 38071373 PMCID: PMC10710448 DOI: 10.1038/s41598-023-49218-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 12/05/2023] [Indexed: 12/18/2023] Open
Abstract
COVID-19, a novel pathogen that emerged in late 2019, has the potential to cause pneumonia with unique variants upon infection. Hence, the development of efficient diagnostic systems is crucial in accurately identifying infected patients and effectively mitigating the spread of the disease. However, the system poses several challenges because of the limited availability of labeled data, distortion, and complexity in image representation, as well as variations in contrast and texture. Therefore, a novel two-phase analysis framework has been developed to scrutinize the subtle irregularities associated with COVID-19 contamination. A new Convolutional Neural Network-based STM-BRNet is developed, which integrates the Split-Transform-Merge (STM) block and Feature map enrichment (FME) techniques in the first phase. The STM block captures boundary and regional-specific features essential for detecting COVID-19 infectious CT slices. Additionally, by incorporating the FME and Transfer Learning (TL) concept into the STM blocks, multiple enhanced channels are generated to effectively capture minute variations in illumination and texture specific to COVID-19-infected images. Additionally, residual multipath learning is used to improve the learning capacity of STM-BRNet and progressively increase the feature representation by boosting at a high level through TL. In the second phase of the analysis, the COVID-19 CT scans are processed using the newly developed SA-CB-BRSeg segmentation CNN to accurately delineate infection in the images. The SA-CB-BRSeg method utilizes a unique approach that combines smooth and heterogeneous processes in both the encoder and decoder. These operations are structured to effectively capture COVID-19 patterns, including region-homogenous, texture variation, and border. By incorporating these techniques, the SA-CB-BRSeg method demonstrates its ability to accurately analyze and segment COVID-19 related data. Furthermore, the SA-CB-BRSeg model incorporates the novel concept of CB in the decoder, where additional channels are combined using TL to enhance the learning of low contrast regions. The developed STM-BRNet and SA-CB-BRSeg models achieve impressive results, with an accuracy of 98.01%, recall of 98.12%, F-score of 98.11%, Dice Similarity of 96.396%, and IOU of 98.85%. The proposed framework will alleviate the workload and enhance the radiologist's decision-making capacity in identifying the infected region of COVID-19 and evaluating the severity stages of the disease.
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Affiliation(s)
- Saddam Hussain Khan
- Department of Computer Systems Engineering, University of Engineering and Applied Science, Swat, 19060, Pakistan
| | - Tahani Jaser Alahmadi
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia.
| | - Tariq Alsahfi
- Department of Information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
| | - Abeer Abdullah Alsadhan
- Computer Science Department, Applied College, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia.
| | - Alanoud Al Mazroa
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia
| | - Hend Khalid Alkahtani
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia
| | - Abdullah Albanyan
- College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - Hesham A Sakr
- Nile Higher Institute for Engineering and Technology, Mansoura, Egypt
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Zhunissova U, Dzierżak R, Omiotek Z, Lytvynenko V. A Novel COVID-19 Diagnosis Approach Utilizing a Comprehensive Set of Diagnostic Information (CSDI). J Clin Med 2023; 12:6912. [PMID: 37959377 PMCID: PMC10649663 DOI: 10.3390/jcm12216912] [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: 09/04/2023] [Revised: 10/19/2023] [Accepted: 10/31/2023] [Indexed: 11/15/2023] Open
Abstract
The aim of the study was to develop a computerized method for distinguishing COVID-19-affected cases from cases of pneumonia. This task continues to be a real challenge in the practice of diagnosing COVID-19 disease. In the study, a new approach was proposed, using a comprehensive set of diagnostic information (CSDI) including, among other things, medical history, demographic data, signs and symptoms of the disease, and laboratory results. These data have the advantage of being much more reliable compared with data based on a single source of information, such as radiological imaging. On this basis, a comprehensive process of building predictive models was carried out, including such steps as data preprocessing, feature selection, training, and evaluation of classification models. During the study, 9 different methods for feature selection were used, while the grid search method and 12 popular classification algorithms were employed to build classification models. The most effective model achieved a classification accuracy (ACC) of 85%, a sensitivity (TPR) equal to 83%, and a specificity (TNR) of 88%. The model was built using the random forest method with 15 features selected using the recursive feature elimination selection method. The results provide an opportunity to build a computer system to assist the physician in the diagnosis of the COVID-19 disease.
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Affiliation(s)
- Ulzhalgas Zhunissova
- Department of Biostatistics, Bioinformatics and Information Technologies, Astana Medical University, Beibitshilik Street 49A, Astana 010000, Kazakhstan
| | - Róża Dzierżak
- Faculty of Electrical Engineering and Computer Science, Lublin University of Technology, Nadbystrzycka 38 A, 20-618 Lublin, Poland
| | - Zbigniew Omiotek
- Faculty of Electrical Engineering and Computer Science, Lublin University of Technology, Nadbystrzycka 38 A, 20-618 Lublin, Poland
| | - Volodymyr Lytvynenko
- Department of Informatics and Computer Science, Kherson National Technical University, Beryslavs’ke Hwy, 24, 730082 Kherson, Kherson Oblast, Ukraine
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