50
Views
2
CrossRef citations to date
0
Altmetric
Research Article

LM-DNN: pre-trained DNN with LSTM and cross Fold validation for detecting viral pneumonia from chest CT

ORCID Icon &
Article: 2280619 | Received 17 Nov 2022, Accepted 02 Nov 2023, Published online: 22 Nov 2023

References

  • Agarwala S, Kale M, Kumar D, Swaroop R, Kumar A, Dhara AK, Nandi D. 2020. Deep learning for screening of interstitial lung disease patterns in high-resolution CT images. Clin Radiol. 75(6):481–e1. doi: 10.1016/j.crad.2020.01.010.
  • Ahamed KU, Islam M, Uddin A, Akhter A, Paul BK, Yousuf MA, Moni MA. 2021. A deep learning approach using effective preprocessing techniques to detect COVID-19 from chest CT-scan and X-ray images. Comput Biol Med. 139:105014. doi: 10.1016/j.compbiomed.2021.105014.
  • Ahuja S, Panigrahi BK, Dey N, Rajinikanth V, Gandhi TK. 2021. Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices. Appl Intell. 51(1):571–585. doi: 10.1007/s10489-020-01826-w.
  • Arora V, Ng EYK, Leekha RS, Darshan M, Singh A. 2021. Transfer learning-based approach for detecting COVID-19 ailment in lung CT scan. Comput Biol Med. 135:104575. doi: 10.1016/j.compbiomed.2021.104575.
  • Aurna NF, Yousuf MA, Taher KA, Azad AKM, Moni MA. 2022. A classification of MRI brain tumor based on two stage feature level ensemble of deep CNN models. Comput Biol Med. 146:105539. doi: 10.1016/j.compbiomed.2022.105539.
  • Bai HX, Hsieh B, Xiong Z, Halsey K, Choi JW, Tran TML, Liao WH. 2020. Performance of radiologists in differentiating COVID-19 from non-COVID-19 viral pneumonia at chest CT. Radiology. 296(2):E46–E54. doi: 10.1148/radiol.2020200823.
  • Bhattacharya S, Maddikunta PKR, Pham QV, Gadekallu TR, Chowdhary CL, Alazab M, Piran MJ. 2021. Deep learning and medical image processing for coronavirus (COVID-19) pandemic: a survey. Sustain Cities Soc. 65:102589. doi: 10.1016/j.scs.2020.102589.
  • Bo G, Cheng P, Dezhi K, Xiping W, Chaodong L, Mingming G, Ghadimi N. 2022. Optimum structure of a combined wind/photovoltaic/fuel cell-based on amended dragon fly optimization algorithm: a case study. Energy Sources, Part A: Recovery, Utili, Environ Eff. 44(3):7109–7131. doi: 10.1080/15567036.2022.2105453.
  • Brunetti A, Carnimeo L, Trotta GF, Bevilacqua V. 2019. Computer-assisted frameworks for classification of liver, breast and blood neoplasias via neural networks: a survey based on medical images. Neurocomputing. 335:274–298. doi: 10.1016/j.neucom.2018.06.080.
  • Cai X, Li X, Razmjooy N, Ghadimi N. 2021. Breast cancer diagnosis by convolutional neural network and advanced thermal exchange optimization algorithm. Comput Math Method M. doi: 10.1155/2021/5595180.
  • Chakraborty T, Banik SK, Bhadra AK, Nandi D. 2021. Dynamically learned PSO based neighborhood influenced fuzzy c-means for pre-treatment and post-treatment organ segmentation from CT images. Comput Methods Programs Biomed. 202:105971. doi: 10.1016/j.cmpb.2021.105971.
  • Chen L, Huang H, Tang P, Yao D, Yang H, Ghadimi N. 2022. Optimal modeling of combined cooling, heating, and power systems using developed African vulture optimization: a case study in watersport complex. Energy Sources, Part A: Recovery, Utili, Environ Eff. 44(2):4296–4317. doi: 10.1080/15567036.2022.2074174.
  • Chollet F. 2017. Xception: deep learning with depthwise separable convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition; Honolulu, USA. p. 1251–1258.
  • Das PK, Diya VA, Meher S, Panda R, Abraham A. 2022. A systematic review on recent advancements in deep and machine learning based detection and classification of acute lymphoblastic leukemia. IEEE Access. 10:81741–81763. doi: 10.1109/ACCESS.2022.3196037.
  • Das PK, Meher S. 2021. Transfer learning-based automatic detection of acute lymphocytic leukemia. Proceedings of the 2021 National Conference on Communications (NCC); Kanpur, India: IEEE. p. 1–6.
  • Das PK, Meher S, Panda R, Abraham A. 2019. A review of automated methods for the detection of sickle cell disease. IEEE Rev Biomed Eng. 13:309–324. doi: 10.1109/RBME.2019.2917780.
  • Das PK, Meher S, Panda R, Abraham A. 2021. An efficient blood-cell segmentation for the detection of hematological disorders. IEEE Trans Cyber. 52(10):10615–10626. doi: 10.1109/TCYB.2021.3062152.
  • Das PK, Nayak B, Meher S. 2022. A lightweight deep learning system for automatic detection of blood cancer. Measurement. 191:110762. doi: 10.1016/j.measurement.2022.110762.
  • Das PK, Sahoo B, Meher S. 2022. An efficient detection and classification of acute leukemia using Transfer learning and orthogonal softmax layer-based model. IEEE/ACM Trans Comput Biol Bioinf. 20:1817–1828. doi: 10.1109/TCBB.2022.3218590.
  • Debelee TG, Schwenker F, Ibenthal A, Yohannes D. 2020. Survey of deep learning in breast cancer image analysis. Evol Syst. 11(1):143–163. doi: 10.1007/s12530-019-09297-2.
  • Erickson BJ, Korfiatis P, Akkus Z, Kline TL. 2017. Machine learning for medical imaging. Radiographics. 37(2):505–515. doi: 10.1148/rg.2017160130.
  • Faruqui N, Yousuf MA, Whaiduzzaman M, Azad AKM, Barros A, Moni MA. 2021. LungNet: a hybrid deep-CNN model for lung cancer diagnosis using CT and wearable sensor-based medical IoT data. Comput Biol Med. 139:104961. doi: 10.1016/j.compbiomed.2021.104961.
  • Gao S, Lima D. 2022. A review of the application of deep learning in the detection of alzheimer’s disease. Int J Cogn Comput Eng. 3:1–8. doi: 10.1016/j.ijcce.2021.12.002.
  • Ghaderzadeh M, Asadi F, Hosseini A, Bashash D, Abolghasemi H, Roshanpour A. 2021. Machine learning in detection and classification of leukemia using smear blood images: a systematic review. Sci Program-Neth. 1–14. doi: 10.1155/2021/9933481.
  • Ghosal P, Chowdhury T, Kumar A, Bhadra AK, Chakraborty J, Nandi D. 2021. MhURI: a supervised segmentation approach to leverage salient brain tissues in magnetic resonance images. Comput Methods Programs Biomed. 200:105841. doi: 10.1016/j.cmpb.2020.105841.
  • Ghosal P, Nandanwar L, Kanchan S, Bhadra A, Chakraborty J, Nandi D. 2019. Brain tumor classification using ResNet-101 based squeeze and excitation deep neural network. Proceedings of the 2019 Second International Conference on Advanced Computational and Communication Paradigms (ICACCP); Gangtok, India: IEEE. p. 1–6.
  • Goodarzian F, Ghasemi P, Gunasekaren A, Taleizadeh AA, Abraham A. 2021. A sustainable-resilience healthcare network for handling COVID-19 pandemic. Ann Oper Res. 312(2):1–65. doi: 10.1007/s10479-021-04238-2.
  • Greenspan H, Van Ginneken B, Summers RM. 2016. Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique. IEEE Trans Med Imaging. 35(5):1153–1159. doi: 10.1109/TMI.2016.2553401.
  • Gunraj H, Wang L, Wong A. 2020. Covidnet-ct: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest ct images. Front Med. 7:608525. doi: 10.3389/fmed.2020.608525.
  • Hammoudi K, Benhabiles H, Melkemi M, Dornaika F, Arganda-Carreras I, Collard D, Scherpereel A. 2021. Deep learning on chest X-ray images to detect and evaluate pneumonia cases at the era of COVID-19. J Med Syst. 45(7):75. doi: 10.1007/s10916-021-01745-4.
  • Han E, Ghadimi N. 2022. Model identification of proton-exchange membrane fuel cells based on a hybrid convolutional neural network and extreme learning machine optimized by improved honey badger algorithm. Sustainable Energy Technol Assess. 52:102005. doi: 10.1016/j.seta.2022.102005.
  • He X, Yang X, Zhang S, Zhao J, Zhang Y, Xing E, Xie P (2020). Sample-efficient deep learning for COVID-19 diagnosis based on CT scans. medrxiv.
  • He K, Zhang X, Ren S, Sun J. 2016. Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition; Las Vegas, USA. p. 770–778.
  • Houssein EH, Emam MM, Ali AA, Suganthan PN. 2021. Deep and machine learning techniques for medical imaging-based breast cancer: a comprehensive review. Expert Syst Appl. 167:114161. doi: 10.1016/j.eswa.2020.114161.
  • Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. 2017. Densely connected convolutional networks. Proceedings of the IEEE conference on computer vision and pattern recognition; Honolulu, USA. p. 4700–4708.
  • Islam MZ, Islam MM, Asraf A. 2020. A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images. Inform Med Unlocked. 20:100412. doi: 10.1016/j.imu.2020.100412.
  • Jia G, Lam HK, Xu Y. 2021. Classification of COVID-19 chest X-Ray and CT images using a type of dynamic CNN modification method. Comput Biol Med. 134:104425. doi: 10.1016/j.compbiomed.2021.104425.
  • Jiang W, Wang X, Huang H, Zhang D, Ghadimi N. 2022. Optimal economic scheduling of microgrids considering renewable energy sources based on energy hub model using demand response and improved water wave optimization algorithm. J Of Energy Storage. 55:105311. doi: 10.1016/j.est.2022.105311.
  • Kalane P, Patil S, Patil BP, Sharma DP. 2021. Automatic detection of COVID-19 disease using U-Net architecture based fully convolutional network. Biomed Signal Process Control. 67:102518. doi: 10.1016/j.bspc.2021.102518.
  • Karamnejadi Azar K, Kakouee A, Mollajafari M, Majdi A, Ghadimi N, Ghadamyari M. 2022. Developed design of battle royale optimizer for the optimum identification of solid oxide fuel cell. Sustainability. 14(16):9882. doi: 10.3390/su14169882.
  • Khojaste-Sarakhsi M, Haghighi SS, Ghomi SF, Marchiori E. 2022. Deep learning for Alzheimer’s disease diagnosis: A survey. Artificial Intelligence In Medicine. 102332:102332. doi: 10.1016/j.artmed.2022.102332.
  • Kieu STH, Bade A, Hijazi MHA, Kolivand H. 2020. A survey of deep learning for lung disease detection on medical images: state-of-the-art, taxonomy, issues and future directions. J Imaging. 6(12):131. doi: 10.3390/jimaging6120131.
  • Kumar A, Ghosal P, Kundu SS, Mukherjee A, Nandi D. 2022. A lightweight asymmetric U-Net framework for acute ischemic stroke lesion segmentation in CT and CTP images. Comput Methods Programs Biomed. 226:107157. doi: 10.1016/j.cmpb.2022.107157.
  • Kumar A, Upadhyay N, Ghosal P, Chowdhury T, Das D, Mukherjee A, Nandi D. 2020. Csnet: a new DeepNet framework for ischemic stroke lesion segmentation. Comput Methods Programs Biomed. 193:105524. doi: 10.1016/j.cmpb.2020.105524.
  • Kundu R, Singh PK, Ferrara M, Ahmadian A, Sarkar R. 2022. ET-NET: an ensemble of transfer learning models for prediction of COVID-19 infection through chest CT-scan images. Multimedia Tools Appl. 81(1):31–50. doi: 10.1007/s11042-021-11319-8.
  • Kuo CC, Chang CM, Liu KT, Lin WK, Chiang HY, Chung CW, Chen KT. 2019. Automation of the kidney function prediction and classification through ultrasound-based kidney imaging using deep learning. NPJ Digital Med. 2(1):29. doi: 10.1038/s41746-019-0104-2.
  • Lee JG, Jun S, Cho YW, Lee H, Kim GB, Seo JB, Kim N. 2017. Deep learning in medical imaging: general overview. Korean J Radiol. 18(4):570–584. doi: 10.3348/kjr.2017.18.4.570.
  • Lin L, Lu L, Cao W, Li T. 2020. Hypothesis for potential pathogenesis of SARS-CoV-2 infection–a review of immune changes in patients with viral pneumonia. Emerg Microbes Infect. 9(1):727–732. doi: 10.1080/22221751.2020.1746199.
  • Li L, Qin L, Xu Z, Yin Y, Wang X, Kong B, Xia J. 2020. Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT. USA: Radiology.
  • Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, Sánchez CI. 2017. A survey on deep learning in medical image analysis. Med Image Anal. 42:60–88. doi: 10.1016/j.media.2017.07.005.
  • Liu X, Faes L, Kale AU, Wagner SK, Fu DJ, Bruynseels A, Denniston AK. 2019. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digit Health. 1(6):e271–e297. doi: 10.1016/S2589-7500(19)30123-2.
  • Liu Y, Jain A, Eng C, Way DH, Lee K, Bui P, Coz D. 2020. A deep learning system for differential diagnosis of skin diseases. Nat Med. 26(6):900–908. doi: 10.1038/s41591-020-0842-3.
  • Li C, Yang Y, Liang H, Wu B. 2021. Transfer learning for establishment of recognition of COVID-19 on CT imaging using small-sized training datasets. Knowl Based Syst. 218:106849. doi: 10.1016/j.knosys.2021.106849.
  • Loey M, Manogaran G, Khalifa NEM. 2020. A deep transfer learning model with classical data augmentation and CGAN to detect COVID-19 from chest CT radiography digital images. Neural Comput Appl. 1–13. doi: 10.1007/s00521-020-05437-x.
  • Malik B, Abdelazeem B, Ghatol A. 2021. Pulmonary fibrosis after COVID-19 pneumonia. Cureus. 13(3). doi: 10.7759/cureus.13923.
  • Ma J, Song Y, Tian X, Hua Y, Zhang R, Wu J. 2020. Survey on deep learning for pulmonary medical imaging. Front Med. 14(4):450–469. doi: 10.1007/s11684-019-0726-4.
  • Mason RJ, Slutsky A, Murray JF, Nadel JA, Gotway MB. 2015. Murray & Nadel’s Textbook of Respiratory Medicine. UK: Elsevier Health Sciences.
  • Mishra AK, Das SK, Roy P, Bandyopadhyay S. 2020. Identifying COVID-19 from chest CT images: a deep convolutional neural networks based approach. J Healthc Eng. 2020:1–7. doi: 10.1155/2020/8843664.
  • Mobiny A, Cicalese PA, Zare S, Yuan P, Abavisani M, Wu CC, Van Nguyen H. 2020. Radiologist-level COVID-19 detection using CT scans with detail-oriented capsule networks.
  • Naeem H, Bin-Salem AA. 2021. A CNN-LSTM network with multi-level feature extraction-based approach for automated detection of coronavirus from CT scan and X-ray images. Appl Soft Comput. 113:107918. doi: 10.1016/j.asoc.2021.107918.
  • Parsian A, Ramezani M, Ghadimi N. 2017. A hybrid neural network-gray wolf optimization algorithm for melanoma detection. Biomed Res. 28(8):3408–3411.
  • Prodhan MMA, Yousuf MA. 2023. Combination of the features of pre-trained Xception and VGG16 models to identify childhood pneumonia from chest X-Ray images. Proceedings of the 2023 International Conference on Electrical, Computer and Communication Engineering (ECCE); Chittagong, Bangladesh: IEEE. p. 1–6.
  • Qin J, Chen L, Liu Y, Liu C, Feng C, Chen B. 2019. A machine learning methodology for diagnosing chronic kidney disease. IEEE Acces. 8:20991–21002. doi: 10.1109/ACCESS.2019.2963053.
  • Rahimzadeh M, Attar A, Sakhaei SM. 2021. A fully automated deep learning-based network for detecting COVID-19 from a new and large lung CT scan dataset. Biomed Signal Process Control. 68:102588. doi: 10.1016/j.bspc.2021.102588.
  • Razmjooy N, Sheykhahmad FR, Ghadimi N. 2018. A hybrid neural network–world cup optimization algorithm for melanoma detection. Open Med. 13(1):9–16. doi: 10.1515/med-2018-0002.
  • Razzak MI, Naz S, Zaib A. 2018. Deep learning for medical image processing: overview, challenges and the future. Classif BioApps: Autom Decis Making. 26:323–350.
  • Saha S, Dutta S, Goswami B, Nandi D. 2023. ADU-Net: an attention dense U-Net based deep supervised DNN for automated lesion segmentation of COVID-19 from chest CT images. Biomed Signal Process Control. 85:104974. doi: 10.1016/j.bspc.2023.104974.
  • Sahu A, Das PK, Meher S. 2023. High accuracy hybrid CNN classifiers for breast cancer detection using mammogram and ultrasound datasets. Biomed Signal Process Control. 80:104292. doi: 10.1016/j.bspc.2022.104292.
  • Sak H, Senior A, Beaufays F. 2014. Long short-term memory based recurrent neural network architectures for large vocabulary speech recognition. arXiv preprint arXiv: 1402.1128.
  • SARS-Cov WT (2). variants. May 25, 2022. URL: https://www.who.int/en/activities/tracking-SARS-CoV-2-variants.
  • Shen D, Wu G, Suk HI. 2017. Deep learning in medical image analysis. Annu Rev Biomed Eng. 19(1):221–248. doi: 10.1146/annurev-bioeng-071516-044442.
  • Shi F, Wang J, Shi J, Wu Z, Wang Q, Tang Z, Shen D. 2020. Review of artificial intelligence techniques in imaging data acquisition, segmentation, and diagnosis for COVID-19. IEEE Rev Biomed Eng. 14:4–15. doi: 10.1109/RBME.2020.2987975.
  • Shoeibi A, Khodatars M, Alizadehsani R, Ghassemi N, Jafari M, Moridian P, Shi P. 2020. Automated detection and forecasting of COVID-19 using deep learning techniques: a review. arXiv preprint arXiv:2007.10785.
  • Simonyan K, Zisserman A. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.
  • Singh VK, Kolekar MH. 2022. Deep learning empowered COVID-19 diagnosis using chest CT scan images for collaborative edge-cloud computing platform. Multimedia Tools Appl. 81(1):3–30. doi: 10.1007/s11042-021-11158-7.
  • Soares E, Angelov P, Biaso S, Froes MH, Abe DK. 2020. SARS-CoV-2 CT-scan dataset: A large dataset of real patients CT scans for SARS-CoV-2 identification. MedRxiv.
  • Srinivasu PN, SivaSai JG, Ijaz MF, Bhoi AK, Kim W, Kang JJ. 2021. Classification of skin disease using deep learning neural networks with MobileNet V2 and LSTM. Sensors. 21(8):2852. doi: 10.3390/s21082852.
  • Suarez-Ibarrola R, Hein S, Reis G, Gratzke C, Miernik A. 2020. Current and future applications of machine and deep learning in urology: a review of the literature on urolithiasis, renal cell carcinoma, and bladder and prostate cancer. World J Urol. 38(10):2329–2347. doi: 10.1007/s00345-019-03000-5.
  • Sun R. 2019. Optimization for deep learning: theory and algorithms. arXiv preprint arXiv:1912.08957.
  • Sun RY. 2020. Optimization for deep learning: an overview. J The Oper Res Soc Of China. 8(2):249–294. doi: 10.1007/s40305-020-00309-6.
  • Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. 2016. Rethinking the inception architecture for computer vision. Proceedings of the IEEE conference on computer vision and pattern recognition; Las Vegas, USA. p. 2818–2826.
  • Wong KK, Fortino G, Abbott D. 2020. Deep learning-based cardiovascular image diagnosis: a promising challenge. Future Gen Compt Syst. 110:802–811. doi: 10.1016/j.future.2019.09.047.
  • World Health Organization. (2021). WHO coronavirus (COVID-19) dashboard. URL: https://covid19.who.int.
  • Xu Z, Sheykhahmad FR, Ghadimi N, Razmjooy N. 2020. Computer-aided diagnosis of skin cancer based on soft computing techniques. Open Med. 15(1):860–871. doi: 10.1515/med-2020-0131.
  • Zhang L, Wang M, Liu M, Zhang D. 2020. A survey on deep learning for neuroimaging-based brain disorder analysis. Front Neurosci. 14:779. doi: 10.3389/fnins.2020.00779.
  • Zhang J, Xie Y, Pang G, Liao Z, Verjans J, Li W, Xia Y. 2020. Viral pneumonia screening on chest X-rays using confidence-aware anomaly detection. IEEE Trans Med Imaging. 40(3):879–890. doi: 10.1109/TMI.2020.3040950.
  • Zhao J, Zhang Y, He X, Xie P. 2020. Covid-ct-dataset: a ct scan dataset about covid-19. arXiv preprint arXiv:2003.13865. 490.
  • Zhou SK, Greenspan H, Davatzikos C, Duncan JS, Van Ginneken B, Madabhushi A, Summers RM. 2021. A review of deep learning in medical imaging: imaging traits, technology trends, case studies with progress highlights, and future promises. P IEEE. 109(5):820–838. doi: 10.1109/JPROC.2021.3054390.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.