Login Register Cart 0
Generic placeholder image

Current Bioinformatics

Editor-in-Chief

ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
General Research Article

Application of Improved Support Vector Machine for Pulmonary Syndrome Exposure with Computer Vision Measures

Author(s): Adil O. Khadidos, Abdulrhman M. Alshareef, Hariprasath Manoharan, Alaa O. Khadidos and Shitharth Selvarajan*

Volume 19, Issue 3, 2024

Published on: 09 March, 2023

Page: [281 - 293] Pages: 13

DOI: 10.2174/1574893618666230206121127

Price: $65

Abstract

Background: In many medically developed applications, the process of early diagnosis in cases of pulmonary disease does not exist. Many people experience immediate suffering due to the lack of early diagnosis, even after becoming aware of breathing difficulties in daily life. Because of this, identifying such hazardous diseases is crucial, and the suggested solution combines computer vision and communication processing techniques. As computing technology advances, a more sophisticated mechanism is required for decision-making.

Objective: The major objective of the proposed method is to use image processing to demonstrate computer vision-based experimentation for identifying lung illness. In order to characterize all the uncertainties that are present in nodule segments, an improved support vector machine is also integrated into the decision-making process.

Methods: As a result, the suggested method incorporates an Improved Support Vector Machine (ISVM) with a clear correlation between various margins. Additionally, an image processing technique is introduced where all impacted sites are marked at high intensity to detect the presence of pulmonary syndrome. Contrary to other methods, the suggested method divides the image processing methodology into groups, making the loop generation process much simpler.

Results: Five situations are taken into account to demonstrate the effectiveness of the suggested technique, and test results are compared with those from existing models.

Conclusion: The proposed technique with ISVM produces 83 percent of successful results.

« Previous
Graphical Abstract

[1]
Xie Y, Xia Y, Zhang J, et al. Knowledge-based collaborative deep learning for benign-malignant lung nodule classification on chest CT. IEEE Trans Med Imaging 2019; 38(4): 991-1004.
[http://dx.doi.org/10.1109/TMI.2018.2876510] [PMID: 30334786]
[2]
Kshirsagar PR, Manoharan H, Selvarajan S, Alterazi HA, Singh D, Lee HN. Perception exploration on robustness syndromes with pre-processing entities using machine learning algorithm. Front Public Health 2022; 10: 893989.
[http://dx.doi.org/10.3389/fpubh.2022.893989] [PMID: 35784247]
[3]
Manoharan H, Selvarajan S, Yafoz A, et al. Deep conviction systems for biomedical applications using intuiting procedures with cross point approach. Front Public Health 2022; 10: 909628.
[http://dx.doi.org/10.3389/fpubh.2022.909628] [PMID: 35677767]
[4]
Ayub S, RJ Kannan , Shitharth S, Raed A, Hasanin T, Sasidha C. LSTM-Based RNN Framework to remove motion artifacts in dynamic multi-contrast MR images with registration model”.Wireless Communications and Mobile Computing. Hindawi 2022; 2022: 5906877.
[http://dx.doi.org/10.1155/2022/5906877]
[5]
Selvarajan S, Manoharan H, Hasanin T, et al. Biomedical signals for healthcare using hadoop infrastructure with artificial intelligence and fuzzy logic interpretation. Applied Sci MDPI 2022; 12(10): 5097.
[http://dx.doi.org/10.3390/app12105097]
[6]
Sahlol AT, Abd Elaziz M, Tariq Jamal A. Damaševičius R, Farouk Hassan O. A novel method for detection of tuberculosis in chest radiographs using artificial ecosystem-based optimisation of deep neural network features. Symmetry (Basel) 2020; 12(7): 1146.
[http://dx.doi.org/10.3390/sym12071146]
[7]
Maqsood M, Yasmin S, Mehmood I, Bukhari M, Kim M. An efficient da-net architecture for lung nodule segmentation. Mathematics 2021; 9(13): 1457.
[http://dx.doi.org/10.3390/math9131457]
[8]
Oluyide OM, Tapamo JR, Viriri S. Automatic lung segmentation based on Graph Cut using a distance-constrained energy. IET Comput Vis 2018; 12(5): 609-15.
[http://dx.doi.org/10.1049/iet-cvi.2017.0226]
[9]
Fokas AS, Dikaios N, Kastis GA. Mathematical models and deep learning for predicting the number of individuals reported to be infected with SARS-CoV-2. J R Soc Interface 2020; 17(169): 20200494.
[http://dx.doi.org/10.1098/rsif.2020.0494] [PMID: 32752997]
[10]
Guo R, Passi K, Jain CK. Tuberculosis diagnostics and localization in chest x-rays via deep learning models. Front Artif Intell 2020; 3: 583427.
[http://dx.doi.org/10.3389/frai.2020.583427] [PMID: 33733221]
[11]
Xu Z, Bagci U, Mansoor A, et al. Computer-aided pulmonary image analysis in small animal models. Med Phys 2015; 42(7): 3896-910.
[http://dx.doi.org/10.1118/1.4921618] [PMID: 26133591]
[12]
Elaziz MA, Hosny KM, Salah A, Darwish MM, Lu S, Sahlol AT. New machine learning method for image-based diagnosis of COVID-19. PLoS One 2020; 15(6): e0235187.
[http://dx.doi.org/10.1371/journal.pone.0235187] [PMID: 32589673]
[13]
Ho TT, Kim T, Kim WJ, et al. A 3D-CNN model with CT-based parametric response mapping for classifying COPD subjects. Sci Rep 2021; 11(1): 34.
[http://dx.doi.org/10.1038/s41598-020-79336-5] [PMID: 33420092]
[14]
Kieu STH, Bade A, Hijazi MHA, Kolivand H. A survey of deep learning for lung disease detection on medical images: State-of-the-art, taxonomy, issues and future directions. J Imaging 2020; 6(12): 131.
[http://dx.doi.org/10.3390/jimaging6120131] [PMID: 34460528]
[15]
Du R, Qi S, Feng J, et al. Identification of COPD from multi-view snapshots of 3D lung airway tree via deep CNN. IEEE Access 2020; 8: 38907-19.
[http://dx.doi.org/10.1109/ACCESS.2020.2974617]
[16]
Monkam P, Qi S, Ma H, Gao W, Yao Y, Qian W. Detection and classification of pulmonary nodules using convolutional neural networks: a survey. IEEE Access 2019; 7: 78075-91.
[http://dx.doi.org/10.1109/ACCESS.2019.2920980]
[17]
Zhou T, Lu H, Zhang J, Shi H. Pulmonary nodule detection model based on SVM and CT image feature-level fusion with rough sets. Biomed Res Int 2016; 2016
[http://dx.doi.org/10.1155/2016/8052436]
[18]
Xia XL, Jiao W, Li K, Irwin G. A novel sparse least squares support vector machines. Math Probl Eng 2013; 2013
[http://dx.doi.org/10.1155/2013/602341]
[19]
Anguita D, Boni A, Ridella S. SVM learning with fixed-point math. Proc Int Jt Conf Neural Netw 2003; 3: 2072-6.
[20]
Liu F, Yin C, Wu X, Ge S, Zhang P, Sun X. Contrastive attention for automatic chest x-ray report generation. Find Assoc Comput Linguist ACL-IJCNLP 2021; 2021: 269-80.

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy