International Journal of Advanced Technology and Engineering Exploration ISSN (Print): 2394-5443    ISSN (Online): 2394-7454 Volume-12 Issue-128 July-2025
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F-LeNet-based pneumonia classification using Jaccard-indexed snake contour segmentation and PDCOA-based feature selection

A. Ramya1 and R. Sundar Rajan2

Research Scholar, Department of Computer Application,Kalasaligam Academy of Research & Education, Krishnankovil, Tamil Nadu,India1
Associate Professor, Department of Information Technology,Kalasaligam Academy of Research & Education, Krishnankovil, Tamil Nadu,India2
Corresponding Author : A. Ramya

Recieved : 20-May-2024; Revised : 20-July-2025; Accepted : 28-July-2025

Abstract

A novel coronavirus, known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which caused the COVID-19 pandemic, first emerged in Wuhan, China. Subsequently, a large number of individuals were infected worldwide, resulting in significant mortality. At present, various types of pneumonia continue to occur, many of which also lead to fatalities. To address this issue, several studies have proposed different pneumonia prediction techniques. However, these approaches often lack crucial diagnostic details. To overcome these limitations, a Fisher-LeNet (F-LeNet)-based pneumonia classification method is developed. Initially, input computed tomography (CT) images are preprocessed using a theta-mapped Laplacian filter (TMLF) and a code map function. Following preprocessing, the lung, trachea, and bronchi are segmented. Centrilobular nodules are then segmented using polygonal approximation (PA), Jaccard-indexed transform-based snake contour (JITSC), and the alpha hull (AH) method. The thickening value is estimated from the segmented lung regions. Using the ordered mini batch k-means (OMBKM) algorithm, the images are clustered into normal and pneumonia categories based on the extracted values. Features are then extracted from the pneumonia cluster. The Poisson distributed coati optimization algorithm (PDCOA) is employed to select the most relevant features, which are subsequently used as input to the F-LeNet model for pneumonia classification. The proposed approach is assessed experimentally and contrasted with conventional baseline techniques using standard performance metrics. According to the outcomes, the proposed approach achieves superior accuracy and computational efficiency. Therefore, this method shows strong potential for improving pneumonia diagnosis and reducing pneumonia-related fatalities.

Keywords

Pneumonia classification, Computed tomography (CT) imaging, Fisher-LeNet (F-LeNet), Feature selection, Theta-mapped Laplacian filter and Jaccard-indexed transform-based snake contour.

Cite this article

Ramya A, Rajan RS. F-LeNet-based pneumonia classification using Jaccard-indexed snake contour segmentation and PDCOA-based feature selection. International Journal of Advanced Technology and Engineering Exploration. 2025;12(128):1124-1142. DOI : 10.19101/IJATEE.2024.111100840

References
[1]
Kör H, Erbay H, Yurttakal AH. Diagnosing and differentiating viral pneumonia and COVID-19 using X-ray images. Multimedia Tools and Applications. 2022; 81(27):39041-57.
[Crossref] [Google Scholar]
[2]
Chaudhary PK, Pachori RB. Automatic diagnosis of COVID-19 and pneumonia using FBD method. In international conference on bioinformatics and biomedicine (BIBM) 2020 (pp. 2257-63). IEEE.
[Crossref] [Google Scholar]
[3]
Narin A, Kaya C, Pamuk Z. Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks. Pattern Analysis and Applications. 2021; 24(3):1207-20.
[Crossref] [Google Scholar]
[4]
Chowdhury ME, Rahman T, Khandakar A, Mazhar R, Kadir MA, Mahbub ZB, et al. Can AI help in screening viral and COVID-19 pneumonia? IEEE Access. 2020; 8:132665-76.
[Crossref] [Google Scholar]
[5]
Serte S, Serener A. Discerning COVID-19 from mycoplasma and viral pneumonia on CT images via deep learning. In 4th international symposium on multidisciplinary studies and innovative technologies (ISMSIT) 2020 (pp. 1-5). IEEE.
[Crossref] [Google Scholar]
[6]
Abiyev RH, Ismail A. COVID‐19 and pneumonia diagnosis in X‐ray images using convolutional neural networks. Mathematical Problems in Engineering. 2021; 2021(1):1-14.
[Crossref] [Google Scholar]
[7]
Sheykhivand S, Mousavi Z, Mojtahedi S, Rezaii TY, Farzamnia A, Meshgini S, et al. Developing an efficient deep neural network for automatic detection of COVID-19 using chest X-ray images. Alexandria Engineering Journal. 2021; 60(3):2885-903.
[Crossref] [Google Scholar]
[8]
Tilve A, Nayak S, Vernekar S, Turi D, Shetgaonkar PR, Aswale S. Pneumonia detection using deep learning approaches. In international conference on emerging trends in information technology and engineering (ic-ETITE) 2020 (pp. 1-8). IEEE.
[Crossref] [Google Scholar]
[9]
Harmon SA, Sanford TH, Xu S, Turkbey EB, Roth H, Xu Z, et al. Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets. Nature Communications. 2020; 11(1):1-7.
[Crossref] [Google Scholar]
[10]
Zhang HT, Zhang JS, Zhang HH, Nan YD, Zhao Y, Fu EQ, et al. Automated detection and quantification of COVID-19 pneumonia: CT imaging analysis by a deep learning-based software. European Journal of Nuclear Medicine and Molecular Imaging. 2020; 47(11):2525-32.
[Crossref] [Google Scholar]
[11]
Elgendi M, Nasir MU, Tang Q, Fletcher RR, Howard N, Menon C, et al. The performance of deep neural networks in differentiating chest X-rays of COVID-19 patients from other bacterial and viral pneumonias. Frontiers in Medicine. 2020; 7:1-8.
[Crossref] [Google Scholar]
[12]
Hasija S, Akash P, Hemanth MB, Kumar A, Sharma S. A novel approach for detection of COVID-19 and pneumonia using only binary classification from chest CT-scans. Neuroscience Informatics. 2022; 2(4):1-9.
[Crossref] [Google Scholar]
[13]
Qiao X, Lu C, Xu M, Yang G, Chen W, Liu Z. DeepSAP: a novel brain image-based deep learning model for predicting stroke-associated pneumonia from spontaneous intracerebral hemorrhage. Academic Radiology. 2024; 31(12):5193-203.
[Crossref] [Google Scholar]
[14]
Atceken Z, Celik Y, Atasoy C, Peker Y. Association of high-risk obstructive sleep apnea with artificial intelligence-guided, CT-Based severity scores in patients with COVID-19 pneumonia. Journal of Clinical Medicine. 2024; 13(21):1-11.
[Crossref] [Google Scholar]
[15]
Chen J, Li Y, Guo L, Zhou X, Zhu Y, He Q, et al. Machine learning techniques for CT imaging diagnosis of novel coronavirus pneumonia: a review. Neural Computing and Applications. 2024; 36(1):181-99.
[Crossref] [Google Scholar]
[16]
Jiang J, Chen S, Zhang S, Zeng Y, Liu J, Lei W, et al. A radiomics model utilizing CT for the early detection and diagnosis of severe community-acquired pneumonia. BMC Medical Imaging. 2024; 24(1):1-11.
[Crossref] [Google Scholar]
[17]
Song J, Wang H, Liu Y, Wu W, Dai G, Wu Z, et al. End-to-end automatic differentiation of the coronavirus disease 2019 (COVID-19) from viral pneumonia based on chest CT. European Journal of Nuclear Medicine and Molecular Imaging. 2020; 47(11):2516-24.
[Crossref] [Google Scholar]
[18]
Gour M, Jain S. Automated COVID-19 detection from X-ray and CT images with stacked ensemble convolutional neural network. Biocybernetics and Biomedical Engineering. 2022; 42(1):27-41.
[Crossref] [Google Scholar]
[19]
Lou X, Gao C, Wu L, Wu T, He L, Shen J, et al. Prediction of short-term progression of COVID-19 pneumonia based on chest CT artificial intelligence: during the omicron epidemic. BMC Infectious Diseases. 2024; 24(1):1-11.
[Crossref] [Google Scholar]
[20]
Yao JC, Wang T, Hou GH, Ou D, Li W, Zhu QD, et al. AI detection of mild COVID-19 pneumonia from chest CT scans. European radiology. 2021; 31(9):7192-201.
[Crossref] [Google Scholar]
[21]
Perumal V, Narayanan V, Rajasekar SJ. Detection of COVID-19 using CXR and CT images using transfer learning and haralick features. Applied Intelligence. 2021; 51(1):341-58.
[Crossref] [Google Scholar]
[22]
Latif G, Morsy H, Hassan A, Alghazo J. Novel coronavirus and common pneumonia detection from CT scans using deep learning-based extracted features. Viruses. 2022; 14(8):1-17.
[Crossref] [Google Scholar]
[23]
Yan T, Wong PK, Ren H, Wang H, Wang J, Li Y. Automatic distinction between COVID-19 and common pneumonia using multi-scale convolutional neural network on chest CT scans. Chaos, Solitons & Fractals. 2020; 140:1-8.
[Crossref] [Google Scholar]
[24]
Mahmoudi R, Benameur N, Mabrouk R, Mohammed MA, Garcia-zapirain B, Bedoui MH. A deep learning-based diagnosis system for COVID-19 detection and pneumonia screening using CT imaging. Applied Sciences. 2022; 12(10):1-23.
[Crossref] [Google Scholar]
[25]
Panwar H, Gupta PK, Siddiqui MK, Morales-menendez R, Bhardwaj P, Singh V. A deep learning and grad-CAM based color visualization approach for fast detection of COVID-19 cases using chest X-ray and CT-Scan images. Chaos, Solitons & Fractals. 2020; 140:1-12.
[Crossref] [Google Scholar]
[26]
Mishra NK, Singh P, Joshi SD. Automated detection of COVID-19 from CT scan using convolutional neural network. Biocybernetics and Biomedical Engineering. 202; 41(2):572-88.
[Crossref] [Google Scholar]
[27]
Ibrahim AU, Ozsoz M, Serte S, Al-turjman F, Yakoi PS. Pneumonia classification using deep learning from chest X-ray images during COVID-19. Cognitive Computation. 2024; 16(4):1589-601.
[Crossref] [Google Scholar]
[28]
Di D, Shi F, Yan F, Xia L, Mo Z, Ding Z, et al. Hypergraph learning for identification of COVID-19 with CT imaging. Medical Image Analysis. 2021; 68:1-9.
[Crossref] [Google Scholar]
[29]
Zheng F, Li L, Zhang X, Song Y, Huang Z, Chong Y, et al. Accurately discriminating COVID-19 from viral and bacterial pneumonia according to CT images via deep learning. Interdisciplinary Sciences: Computational Life Sciences. 2021; 13(2):273-85.
[Crossref] [Google Scholar]
[30]
Umar IA, Ozsoz M, Serte S, Al‐turjman F, Habeeb KS. Convolutional neural network for diagnosis of viral pneumonia and COVID‐19 alike diseases. Expert Systems. 2022; 39(10):e12705.
[Crossref] [Google Scholar]
[31]
Santos MA, Berton L. An enhanced framework for overcoming pitfalls and enabling model interpretation in pneumonia and COVID-19 classification. IEEE Access. 2023; 11:115330-47.
[Crossref] [Google Scholar]
[32]
Narula A, Vaegae NK. Development of CNN-LSTM combinational architecture for COVID-19 detection. Journal of Ambient Intelligence and Humanized Computing. 2023; 14(3):2645-56.
[Crossref] [Google Scholar]
[33]
Lin CY, Guo SM, Lien JJ, Lin WT, Liu YS, Lai CH, et al. Combined model integrating deep learning, radiomics, and clinical data to classify lung nodules at chest CT. La Radiologia Medica. 2024; 129(1):56-69.
[Crossref] [Google Scholar]
[34]
Rana S, Hosen MJ, Tonni TJ, Rony MA, Fatema K, Hasan MZ, et al. DeepChestGNN: a comprehensive framework for enhanced lung disease identification through advanced graphical deep features. Sensors. 2024; 24(9):1-29.
[Crossref] [Google Scholar]
[35]
Alaiad AI, Mugdadi EA, Hmeidi II, Obeidat N, Abualigah L. Predicting the severity of COVID-19 from lung CT images using novel deep learning. Journal of Medical and Biological Engineering. 2023; 43(2):135-46.
[Crossref] [Google Scholar]
[36]
Wong PK, Yan T, Wang H, Chan IN, Wang J, Li Y, et al. Automatic detection of multiple types of pneumonia: open dataset and a multi-scale attention network. Biomedical Signal Processing and Control. 2022; 73:1-11.
[Crossref] [Google Scholar]
[37]
Serte S, Demirel H. Deep learning for diagnosis of COVID-19 using 3D CT scans. Computers in Biology and Medicine. 2021; 132:1-8.
[Crossref] [Google Scholar]
[38]
Huang H, Kamata SI. Classification of COVID-19 on chest CT Scans with higher order residual network. International Journal of Pharma Medicine and Biological Sciences. 2022; 11(4):70-5.
[Crossref] [Google Scholar]