OpenLB
Open Library of Bioscience
Advanced Search
Medical & biological engineering & computing
Sep, 2024;62 (9): 2893-2909.

FACNN: fuzzy-based adaptive convolution neural network for classifying COVID-19 in noisy CXR images.

Suganyadevi S, Seethalakshmi V
Author Information
  1. Suganyadevi S: Department of ECE, KPR Institute of Engineering and Technology, Coimbatore, 641 407, India. suganya3223@gmail.com. ORCID
  2. Seethalakshmi V: Department of ECE, KPR Institute of Engineering and Technology, Coimbatore, 641 407, India.
PMID: 38710960 DOI: 10.1007/s11517-024-03107-x

Abstract

COVID-19 detection using chest X-rays (CXR) has evolved as a significant method for early diagnosis of the pandemic disease. Clinical trials and methods utilize X-ray images with computer and intelligent algorithms to improve detection and classification precision. This article thus proposes a fuzzy-based adaptive convolution neural network (FACNN) model to improve the detection precision by confining the false rates. The feature extraction process between the successive regions is validated using a fuzzy process that classifies labeled and unknown pixels. The membership functions are derived based on high precision features for detection and false rate suppression process. The convolution neural network process is responsible for increasing detection precision through recurrent training based on feature availability. This availability analysis is verified using fuzzy derivatives under local variances. Based on variance-reduced features, the appropriate regions with labeled and unknown features are used for normal or infected classification. Thus, the proposed FACNN improves accuracy, precision, and feature extraction by 14.36%, 8.74%, and 12.35%, respectively. This model reduces the false rate and extraction time by 10.35% and 10.66%, respectively.

Keywords

CNN COVID-19 Classification Fuzzy optimization X-ray images

References

  1. Ying X, Liu H, Huang R (2023) COVID-19 chest X-ray image classification in the presence of noisy labels. Displays 77:102370 [DOI: 10.1016/j.displa.2023.102370]
  2. Akbarimajd A, Hoertel N, Hussain MA, Neshat AA, Marhamati M, Bakhtoor M, Momeny M (2022) Learning-to-augment incorporated noise-robust deep CNN for detection of COVID-19 in noisy X-ray images. J Comput Sci 63:101763 [DOI: 10.1016/j.jocs.2022.101763]
  3. El-Kenawy ESM, Mirjalili S, Ibrahim A, Alrahmawy M, El-Said M, Zaki RM, Eid MM (2021) Advanced meta-heuristics, convolutional neural networks, and feature selectors for efficient COVID-19 X-ray chest image classification. Ieee Access 9:36019–36037 [DOI: 10.1109/ACCESS.2021.3061058]
  4. Jyoti K, Sushma S, Yadav S, Kumar P, Pachori RB, Mukherjee S (2023) Automatic diagnosis of COVID-19 with MCA-inspired TQWT-based classification of chest X-ray images. Comput Biol Med 152:106331 [DOI: 10.1016/j.compbiomed.2022.106331]
  5. Bhatele KR, Jha A, Tiwari D, Bhatele M, Sharma S, Mithora MR, Singhal S (2022) COVID-19 detection: a systematic review of machine and deep learning-based approaches utilizing chest X-Rays and CT scans. Cognit Comput 1–38. https://doi.org/10.1007/s12559-022-10076-6
  6. Ding W, Chakraborty S, Mali K, Chatterjee S, Nayak J, Das AK, Banerjee S (2021) An unsupervised fuzzy clustering approach for early screening of COVID-19 from radiological images. IEEE Trans Fuzzy Syst 30(8):2902–2914 [DOI: 10.1109/TFUZZ.2021.3097806]
  7. Ieracitano C, Mammone N, Versaci M, Varone G, Ali AR, Armentano A, Morabito FC (2022) A fuzzy-enhanced deep learning approach for early detection of Covid-19 pneumonia from portable chest X-ray images. Neurocomputing 481:202–215 [DOI: 10.1016/j.neucom.2022.01.055]
  8. Dey S, Bhattacharya R, Malakar S, Schwenker F, Sarkar R (2022) CovidConvLSTM: a fuzzy ensemble model for COVID-19 detection from chest X-rays. Expert Systems with Applications 206:117812 [DOI: 10.1016/j.eswa.2022.117812]
  9. Gao L, Ni H, Liu X (2024) Type-II fuzzy deep neural network model for diagnosing COVID-19 infection with chest X-ray images. J Opt 53:1508–1515. https://doi.org/10.1007/s12596-023-01317-4
  10. Rajeswari R, Gampala V, Maram B, Cristin R (2022) FWLICM-deep learning: fuzzy weighted local information C-means clustering-based lung lobe segmentation with deep learning for COVID-19 detection. J Digital Imaging 35(6):1463–1478 [DOI: 10.1007/s10278-022-00667-y]
  11. Esmi N, Golshan Y, Asadi S, Shahbahrami A, Gaydadjiev G (2023) A fuzzy fine-tuned model for COVID-19 diagnosis. Comput Biol Med 153:106483 [DOI: 10.1016/j.compbiomed.2022.106483]
  12. Gupta A, Gupta S, Katarya R (2021) InstaCovNet-19: a deep learning classification model for the detection of COVID-19 patients using Chest X-ray. Appl Soft Comput 99:106859 [DOI: 10.1016/j.asoc.2020.106859]
  13. Verma K, Kumar A, Swaraj A, Sagar A (2023) Classification of suspected objects and severity assessment of COVID-19 from chest X-ray images using deep transfer learning. Res Biomed Eng 39(3):705–718 [DOI: 10.1007/s42600-023-00302-x]
  14. Habib M, Ramzan M, Khan SA (2022) A deep learning and handcrafted based computationally intelligent technique for effective COVID-19 detection from X-ray/CT-scan imaging. J Grid Comput 20(3):23 [DOI: 10.1007/s10723-022-09615-0]
  15. Kumar N, Gupta M, Gupta D, Tiwari S (2023) Novel deep transfer learning model for COVID-19 patient detection using X-ray chest images. J Ambient Intell Humaniz Comput 14(1):469–478 [DOI: 10.1007/s12652-021-03306-6]
  16. Saha P, Neogy S (2022) Concat_CNN: a model to detect COVID-19 from chest X-ray images with deep learning. SN Comput Sci 3(4):305 [DOI: 10.1007/s42979-022-01182-1]
  17. Elhanashi A, Saponara S, Zheng Q (2023) Classification and localization of multi-type abnormalities on chest X-Rays images. IEEE Access 11:83264–83277
  18. Pathak Y, Shukla PK, Arya KV (2020) Deep bidirectional classification model for COVID-19 disease infected patients. IEEE/ACM Trans Comput Biol Bioinform 18(4):1234–1241 [DOI: 10.1109/TCBB.2020.3009859]
  19. Sakib S, Tazrin T, Fouda MM, Fadlullah ZM, Guizani M (2020) DL-CRC: deep learning-based chest radiograph classification for COVID-19 detection: a novel approach. Ieee Access 8:171575–171589 [DOI: 10.1109/ACCESS.2020.3025010]
  20. Zhao H, Fang Z, Ren J, MacLellan C, Xia Y, Li S, Ren K (2022) SC2Net: A novel segmentation-based classification network for detection of COVID-19 in chest X-ray images. IEEE J Biomed Health Inform 26(8):4032–4043 [DOI: 10.1109/JBHI.2022.3177854]
  21. Rayan A, Alaerjan AS (2023) An improved crow search optimization with Bi-LSTM model for identification and classification of COVID-19 infection from chest X-Ray images. Alexandria Eng J. https://doi.org/10.1016/j.aej.2023.06.052
  22. Liu S, Cai T, Tang X, Zhang Y, Wang C (2022) COVID-19 diagnosis via chest X-ray image classification based on multiscale class residual attention. Computers in Biology and Medicine 149:106065 [DOI: 10.1016/j.compbiomed.2022.106065]
  23. Ayalew AM, Salau AO, Abeje BT, Enyew B (2022) Detection and classification of COVID-19 disease from X-ray images using convolutional neural networks and histogram of oriented gradients. Biomed Signal Process Control 74:103530 [DOI: 10.1016/j.bspc.2022.103530]
  24. Dey S, Bhattacharya R, Malakar S, Mirjalili S, Sarkar R (2021) Choquet fuzzy integral-based classifier ensemble technique for COVID-19 detection. Comput Biol Med 135:104585 [DOI: 10.1016/j.compbiomed.2021.104585]
  25. Xu Y, Lam HK, Jia G (2021) MANet: A two-stage deep learning method for classification of COVID-19 from Chest X-ray images. Neurocomputing 443:96–105 [DOI: 10.1016/j.neucom.2021.03.034]
  26. Sundaram SG, Aloyuni SA, Alharbi RA, Alqahtani T, Sikkandar MY, Subbiah C (2022) Deep transfer learning based unified framework for COVID19 classification and infection detection from chest X-ray images. Arab J Sci Eng 47(2):1675–1692 [DOI: 10.1007/s13369-021-05958-0]
  27. Jangam E, Annavarapu CSR, Barreto AAD (2023) A multi-class classification framework for disease screening and disease diagnosis of COVID-19 from chest X-ray images. Multimed Tools Appl 82(10):14367–14401 [DOI: 10.1007/s11042-022-13710-5]
  28. Kc K, Yin Z, Wu M, Wu Z (2021) Evaluation of deep learning-based approaches for COVID-19 classification based on chest X-ray images. Signal, Image Video Processing 15:959–966 [DOI: 10.1007/s11760-020-01820-2]
  29. Madaan V, Roy A, Gupta C, Agrawal P, Sharma A, Bologa C-S, Prodan R-C XCOVNet: chest X-ray image classification for COVID-19 early detection using convolutional neural networks. New Gener Comput 39(2021):583–597
  30. De Falco I, De Pietro G, Sannino G (2023) Classification of Covid-19 chest X-ray images by means of an interpretable evolutionary rule-based approach. Neural Comput Applic 35(22):16061–16071 [DOI: 10.1007/s00521-021-06806-w]
  31. Ravi V, Narasimhan H, Chakraborty C, Pham TD (2022) Deep learning-based meta-classifier approach for COVID-19 classification using CT scan and chest X-ray images. Multimedia systems 28(4):1401–1415 [DOI: 10.1007/s00530-021-00826-1]
  32. Shoaib MR, Emara HM, Elwekeil M et al (2022) Hybrid classification structures for automatic COVID-19 detection. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-021-03686-9 [DOI: 10.1007/s12652-021-03686-9]
  33. Shoaib MR et al (2021) Deep convolutional neural networks for COVID-19 automatic diagnosis. Microsc Res Tech. 84:2504–2516 [DOI: 10.1002/jemt.23713]
  34. Emara HM, Shoaib MR, El-Shafai W, Elwekeil M, EE-D Hemdan, Fouda MM et al (2023) Simultaneous super-resolution and classification of lung disease scans. Diagnostics 13(7). https://doi.org/10.3390/diagnostics13071319
  35. Shoaib MR, Elshamy MR, Taha TE, El-Fishawy AS, Abd El-Samie FE (2022) Efficient deep learning models for brain tumor detection with segmentation and data augmentation techniques. Concurr Comput Pract Exp 34:7031 [DOI: 10.1002/cpe.7031]
  36. Taher F, Shoaib MR, Emara HM, Abdelwahab KM, El-Samie FEA, Haweel MT (2022) Efficient framework for brain tumor detection using different deep learning techniques. Front Public Health 10:959667 [DOI: 10.3389/fpubh.2022.959667]
  37. Shoaib MR, Elshamy MR, Taha TE, ElFishawy AS, Abd El-Samie FE (2021) Efficient brain tumor detection based on deep learning models. J Phys Conf Ser 2128(1):012012. https://doi.org/10.1088/1742-6596/2128/1/012012
  38. Shaukat Kamran, Luo Suhuai, Varadharajan Vijay (2023) A novel deep learning-based approach for malware detection. Eng Appl Artif Intelligence 122:106030 [DOI: 10.1016/j.engappai.2023.106030]
  39. Shaukat K, Luo S, Varadharajan V, Hameed IA, Chen S, Liu D, Li J (2020) Performance comparison and current challenges of using machine learning techniques in cybersecurity. Energies 13(10):2509. https://doi.org/10.3390/en13102509
  40. Shaukat K, Luo S, Varadharajan V, Hameed IA, Xu M (2020) A survey on machine learning techniques for cyber security in the last decade. IEEE Access 8:222310–222354. https://doi.org/10.1109/ACCESS.2020.3041951 [DOI: 10.1109/ACCESS.2020.3041951]
  41. Shaukat K, Alam TM, Hameed IA, Khan WA, Abbas  N, Luo S (2021) A review on security challenges in Internet of Things (IoT). In: 26th International conference on automation and computing (ICAC), Portsmouth, United Kingdom, pp 1–6.  https://doi.org/10.23919/ICAC50006.2021.9594183
  42. Ibrar Muhammad et al (2022) A machine learning-based model for stability prediction of decentralized power grid linked with renewable energy resources. Wirel Commun Mob Comput. https://doi.org/10.1155/2022/2697303 [DOI: 10.1155/2022/2697303]
  43. Kumar MR, Vekkot S, Lalitha S, Gupta D, Govindraj VJ, Shaukat K, Alotaibi YA, Zakariah M (2022) “Dementia detection from speech using machine learning and deep learning architectures” Sensors 22, 23 9311. https://doi.org/10.3390/s22239311
  44. Shaukat K, Alam TM, Ahmed M et al (2020) A model to enhance governance issues through opinion extraction. In: 2020 11th IEEE annual information technology, electronics and mobile communication conference (IEMCON), Vancouver, pp 511–0516.  https://doi.org/10.1109/IEMCON51383.2020.92848764876
  45. Alharbi, Fahad, Suhuai Luo, Hongyu Zhang, Kamran Shaukat, Guang Yang, Craig A. Wheeler, and Zhiyong Chen. (2023) “A brief review of acoustic and vibration signal-based fault detection for belt conveyor idlers using machine learning models” Sensors 23, 4 1902 https://doi.org/10.3390/s23041902
  46. Yang X, Khushi M, Shaukat K (2020) Biomarker CA125 feature engineering and class imbalance learning improves ovarian cancer prediction. In: 2020 IEEE asia-pacific conference on computer science and data engineering (CSDE), Gold Coast, Australia, pp 1–6. https://doi.org/10.1109/CSDE50874.2020.9411607
  47. Shaukat K, Luo S, Chen S,  Liu D (2020) Cyber threat detection using machine learning techniques: a performance evaluation perspective. In: 2020 international conference on cyber warfare and security (ICCWS), pp 1–6. https://doi.org/10.1109/ICCWS48432.2020.9292388

MeSH Term

COVID-19
Humans
Fuzzy Logic
Neural Networks, Computer
SARS-CoV-2
Algorithms
Radiography, Thoracic
Journal Article

Word Cloud

Created with Highcharts 10.0.0detectionprecisionprocessCOVID-19usingimagesconvolutionneuralnetworkfalsefeatureextractionfeaturesCXRX-rayimproveclassificationfuzzy-basedadaptiveFACNNmodelregionsfuzzylabeledunknownbasedrateavailability35%respectively10chestX-raysevolvedsignificantmethodearlydiagnosispandemicdiseaseClinicaltrialsmethodsutilizecomputerintelligentalgorithmsarticlethusproposesconfiningratessuccessivevalidatedclassifiespixelsmembershipfunctionsderivedhighsuppressionresponsibleincreasingrecurrenttraininganalysisverifiedderivativeslocalvariancesBasedvariance-reducedappropriateusednormalinfectedThusproposedimprovesaccuracy1436%874%12reducestime66%FACNN:classifyingnoisyCNNClassificationFuzzyoptimization

Similar Articles

Cited By