Deep convolutional neural network (CNN) model optimization techniques—Review for medical imaging

Ghazanfar Latif; Jaafar Alghazo; Majid Ali Khan; Ghassen Ben Brahim; Khaled Fawagreh; Nazeeruddin Mohammad; Ghazanfar Latif; Jaafar Alghazo; Majid Ali Khan; Ghassen Ben Brahim; Khaled Fawagreh; Nazeeruddin Mohammad

doi:10.3934/math.2024998

AIMS Mathematics

2024, Volume 9, Issue 8: 20539-20571. doi: 10.3934/math.2024998

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Review Special Issues

Deep convolutional neural network (CNN) model optimization techniques—Review for medical imaging

1.
Department of Computer Science, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia
2.
Department of Software Engineering and Information Technology Management, University of Minnesota Crookston, Crookston, MN 56716, USA

Received: 22 February 2024 Revised: 02 May 2024 Accepted: 20 May 2024 Published: 25 June 2024
MSC : 62M45, 68T45

The field of artificial intelligence (AI) and machine learning (ML) has been expanding and is explored by researchers in various fields. In medical diagnosis, for instance, the field of AI/ML is being explored because if medical diagnostic devices are built and designed with a backend of AI/ML, then the benefits would be unprecedented. Automated diagnostic tools would result in reduced health care costs, diagnosis without human intervention, overcoming human errors, and providing adequate and affordable medical care to a wider portion of the population with portions of the actual cost. One domain where AI/ML can make an immediate impact is medical imaging diagnosis (MID), namely the classification of medical images, where researchers have applied optimization techniques aiming to improve image classification accuracy. In this paper, we provide the research community with a comprehensive review of the most relevant studies to date on the use of deep CNN architecture optimization techniques for MID. As a case study, the application of these techniques to COVID-19 medical images were made. The impacts of the related variables, including datasets and AI/ML techniques, were investigated in detail. Additionally, the significant shortcomings and challenges of the techniques were touched upon. We concluded our work by affirming that the application of AI/ML techniques for MID will continue for many years to come, and the performance of the AI/ML classification techniques will continue to increase.
- convolutional neural network,
- CNN architecture optimization,
- deep learning,
- medical imaging,
- AI model optimization,
- medical diagnosis
Citation: Ghazanfar Latif, Jaafar Alghazo, Majid Ali Khan, Ghassen Ben Brahim, Khaled Fawagreh, Nazeeruddin Mohammad. Deep convolutional neural network (CNN) model optimization techniques—Review for medical imaging[J]. AIMS Mathematics, 2024, 9(8): 20539-20571. doi: 10.3934/math.2024998

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Abstract

The field of artificial intelligence (AI) and machine learning (ML) has been expanding and is explored by researchers in various fields. In medical diagnosis, for instance, the field of AI/ML is being explored because if medical diagnostic devices are built and designed with a backend of AI/ML, then the benefits would be unprecedented. Automated diagnostic tools would result in reduced health care costs, diagnosis without human intervention, overcoming human errors, and providing adequate and affordable medical care to a wider portion of the population with portions of the actual cost. One domain where AI/ML can make an immediate impact is medical imaging diagnosis (MID), namely the classification of medical images, where researchers have applied optimization techniques aiming to improve image classification accuracy. In this paper, we provide the research community with a comprehensive review of the most relevant studies to date on the use of deep CNN architecture optimization techniques for MID. As a case study, the application of these techniques to COVID-19 medical images were made. The impacts of the related variables, including datasets and AI/ML techniques, were investigated in detail. Additionally, the significant shortcomings and challenges of the techniques were touched upon. We concluded our work by affirming that the application of AI/ML techniques for MID will continue for many years to come, and the performance of the AI/ML classification techniques will continue to increase.

References

[1]	V. Sharma, M. G. Dastidar, S. Sutradhar, V. Raj, K. De Silva, S. Roy, A step toward better sample management of COVID-19: On-spot detection by biometric technology and artificial intelligence, COVID-19 Sustain, Develop. Goals, 2022 (2022), 349–380. https://doi.org/10.1016/B978-0-323-91307-2.00017-1 doi: 10.1016/B978-0-323-91307-2.00017-1
[2]	G. Latif, H. Morsy, A. Hassan, J. Alghazo, Novel coronavirus and common pneumonia detection from CT scans using deep learning-based extracted features, Viruses, 14 (2022), 1667. https://doi.org/10.3390/v14081667 doi: 10.3390/v14081667
[3]	A. Islam, T. Rahim, M. Masuduzzaman, S. Y. Shin, A blockchain-based artificial intelligence-empowered contagious pandemic situation supervision scheme using internet of drone things, IEEE Wirel. Commun., 28 (2021), 166–173. https://doi.org/10.1109/MWC.001.2000429 doi: 10.1109/MWC.001.2000429
[4]	T. Rahim, M. A. Usman, S. Y. Shin, A survey on contemporary computer-aided tumor, polyp, and ulcer detection methods in wireless capsule endoscopy imaging, Comput. Med. Imag. Grap., 85 (2020), 101767. https://doi.org/10.1016/j.compmedimag.2020.101767 doi: 10.1016/j.compmedimag.2020.101767
[5]	G. Latif, DeepTumor: Framework for brain MR image classification, segmentation and tumor detection, Diagnostics, 12 (2022), 2888. https://doi.org/10.3390/diagnostics12112888 doi: 10.3390/diagnostics12112888
[6]	T. Rahim, S. A. Hassan, S. Y. Shin, A deep convolutional neural network for the detection of polyps in colonoscopy images, Biomed. Signal Proces., 68 (2021), 102654. https://doi.org/10.1016/j.bspc.2021.102654 doi: 10.1016/j.bspc.2021.102654
[7]	A. Bashar, G. Latif, G. Ben Brahim, N. Mohammad, J. Alghazo, COVID-19 pneumonia detection using optimized deep learning techniques, Diagnostics, 11 (2021), 1972. https://doi.org/10.3390/diagnostics11111972 doi: 10.3390/diagnostics11111972
[8]	E. Hussain, M. Hasan, M. A. Rahman, I. Lee, T. Tamanna, M. Z. Parvez, CoroDet: A deep learning based classification for COVID-19 detection using chest X-ray images, Chaos Soliton. Fract., 142 (2021), 110495. https://doi.org/10.1016/j.chaos.2020.110495 doi: 10.1016/j.chaos.2020.110495
[9]	G. Latif, G. Ben Brahim, D. N. F. A. Iskandar, A. Bashar, J. Alghazo, Glioma tumors' classification using deep-neural-network-based features with SVM classifier, Diagnostics, 12 (2022), 1018. https://doi.org/10.3390/diagnostics12041018 doi: 10.3390/diagnostics12041018
[10]	I. Iqbal, M. Younus, K. Walayat, M. U. Kakar, J. Ma, Automated multi-class classification of skin lesions through deep convolutional neural network with dermoscopic images, Comput. Med. Imag. grap, 88 (2021), 101843. https://doi.org/10.1016/j.compmedimag.2020.101843 doi: 10.1016/j.compmedimag.2020.101843
[11]	I. Iqbal, K. Walayat, M. U. Kakar, J. Ma, Automated identification of human gastrointestinal tract abnormalities based on deep convolutional neural network with endoscopic images, Intell. Syst. Appl., 16 (2022), 200149. https://doi.org/10.1016/j.iswa.2022.200149 doi: 10.1016/j.iswa.2022.200149
[12]	V. Shah, R. Keniya, A. Shridharani, M. Punjabi, J. Shah, N. Mehendale, Diagnosis of COVID-19 using CT scan images and deep learning techniques, Emerg. Radiol., 28 (2021), 497–505. https://doi.org/10.1007/s10140-020-01886-y doi: 10.1007/s10140-020-01886-y
[13]	M. M. Rahaman, C. Li, Y. Yao, K. Frank, M. A. Rahman, Q. Wang, et al., Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches, J. X-Ray Sci. Technol., 28 (2020), 821–839. https://doi.org/10.3233/XST-200715 doi: 10.3233/XST-200715
[14]	A. S. Al-Waisy, S. Al-Fahdawi, M. A. Mohammed, K. H. Abdulkareem, S. A. Mostafa, M. S. Maashi, et al., COVID-CheXNet: Hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images, Soft Comput., 27 (2020), 2657–2672. https://doi.org/10.1007/s00500-020-05424-3 doi: 10.1007/s00500-020-05424-3
[15]	Y. Chang, X. Jing, Z. Ren, B. Schuller, CovNet: A transfer learning framework for automatic COVID-19 detection from crowd-sourced cough sounds, Front. Digit. Health, 3 (2022), 799067. https://doi.org/10.3389/fdgth.2021.799067 doi: 10.3389/fdgth.2021.799067
[16]	M. Elpeltagy, H. Sallam, Automatic prediction of COVID-19 from chest images using modified ResNet50, Multimed. Tools Appl., 80 (2021), 26451–26463. https://doi.org/10.1007/s11042-021-10783-6 doi: 10.1007/s11042-021-10783-6
[17]	R. K. Patel, M. Kashyap, Automated diagnosis of COVID stages from lung CT images using statistical features in 2-dimensional flexible analytic wavelet transform, Biocybern. Biomed. Eng., 42 (2022), 829–841. https://doi.org/10.1016/j.bbe.2022.06.005 doi: 10.1016/j.bbe.2022.06.005
[18]	D. K. Redie, A. E. Sirko, T. M. Demissie, S. S. Teferi, V. K. Shrivastava, O. P. Verma, et al., Diagnosis of COVID-19 using chest X-ray images based on modified DarkCovidNet model, Evol Intell., 16 (2022), 729–738. https://doi.org/10.1007/s12065-021-00679-7 doi: 10.1007/s12065-021-00679-7
[19]	F. Özyurt, Efficient deep feature selection for remote sensing image recognition with fused deep learning architectures, J. Supercomput, 76 (2020), 8413–8431. https://doi.org/10.1007/s11227-019-03106-y doi: 10.1007/s11227-019-03106-y
[20]	D. H. Hubel, T. N. Wiesel, Receptive fields, binocular interaction and functional architecture in the cat's visual cortex, J. Physiol., 160 (1962), 106–154. https://doi.org/10.1113/jphysiol.1962.sp006837 doi: 10.1113/jphysiol.1962.sp006837
[21]	Y. LeCun, Y. Bengio, Convolutional networks for images, speech, and time series, In: The handbook of brain theory and neural networks, 1995.
[22]	G. Latif, J. Alghazo, L. Alzubaidi, M. N. Nasser, Y. Alghazo, Deep convolutional neural network for recognition of unified multi-language handwritten numerals, In: 2018 IEEE 2nd International Workshop on Arabic and Derived Script Analysis and Recognition (ASAR), 2018. https://doi.org/10.1109/ASAR.2018.8480289
[23]	A. Krizhevsky, I. Sutskever, G. E. Hinton, ImageNet classification with deep convolutional neural networks. Commun. ACM, 60 (2017), 84–90. https://doi.org/10.1145/3065386 doi: 10.1145/3065386
[24]	C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, et al., Going deeper with convolutions, 2014, arXiv: 1409.4842.
[25]	S. Alghamdi, M. Alabkari, F. Aljishi, G. Latif, A. Bashar, Lung cancer detection from LDCT images using deep convolutional neural networks, In: International Conference on Communication, Computing and Electronics Systems, Singapore: Springer, 733 (2021), 363–374. https://doi.org/10.1007/978-981-33-4909-4_27
[26]	D. A. Alghmgham, G. Latif, J. Alghazo, L. Alzubaidi, Autonomous traffic sign (ATSR) detection and recognition using deep CNN, Procedia Comput. Sci., 163 (2019), 266–274. https://doi.org/10.1016/j.procs.2019.12.108 doi: 10.1016/j.procs.2019.12.108
[27]	G. Latif, N. Mohammad, R. AlKhalaf, R. AlKhalaf, J. Alghazo, M. Khan, An automatic arabic sign language recognition system based on deep CNN: An assistive system for the deaf and hard of hearing, Int. J. Comput. Digital Syst., 9 (2020), 715–724. http://doi.org/10.12785/ijcds/090418 doi: 10.12785/ijcds/090418
[28]	B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, A. Oliva, Learning deep features for scene recognition using places database, In: NIPS'14: Proceedings of the 27th International Conference on Neural Information Processing Systems, 1 (2014), 487–495.
[29]	M. M. Butt, G. Latif, D. N. F. A. Iskandar, J. Alghazo, A. H. Khan, Multi-channel convolutions neural network based diabetic retinopathy detection from fundus images, Procedia Comput. Sci., 163 (2019), 283–291. https://doi.org/10.1016/j.procs.2019.12.110 doi: 10.1016/j.procs.2019.12.110
[30]	D. C. Cireşan, U. Meier, J. Masci, L. Gambardella, J. Schmidhuber, Flexible, high performance convolutional neural networks for image classification, In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, 2011, 1237–1242. https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-210
[31]	G. Lokku, G. H. Reddy, M. N. G. Prasad, OPFaceNet: Optimized face recognition network for noise and occlusion affected face images using hyperparameters tuned convolutional neural network, Appl. Soft Comput., 117 (2022), 108365. https://doi.org/10.1016/j.asoc.2021.108365 doi: 10.1016/j.asoc.2021.108365
[32]	S. Y. Kim, Z. W. Geem, G. Han, Hyperparameter optimization method based on harmony search algorithm to improve performance of 1D CNN human respiration pattern recognition system, Sensors, 20 (2020), 3697. https://doi.org/10.3390/s20133697 doi: 10.3390/s20133697
[33]	G. Latif, K. Bouchard, J. Maitre, A. Back, L. P. Bedard, Deep-learning-based automatic mineral grain segmentation and recognition, Minerals, 12 (2022), 455. https://doi.org/10.3390/min12040455 doi: 10.3390/min12040455
[34]	J. Bruna, S. Mallat, Invariant scattering convolution networks, IEEE T. Pattern Anal., 35 (2013), 1872–1886. https://doi.org/10.1109/TPAMI.2012.230 doi: 10.1109/TPAMI.2012.230
[35]	S. Lawrence, C. L. Giles, A. C. Tsoi, What size neural network gives optimal generalization? Convergence properties of backpropagation, In: Digital Repository at the University of Maryland, 1998.
[36]	L. Wan, M. Zeiler, S. Zhang, Y. Cun, R. Fergus, Regularization of neural networks using dropconnect, In: ICML'13: Proceedings of the 30th International Conference on International Conference on Machine Learning, 28 (2013), 1058–1066.
[37]	Q. Xu, M. Zhang, Z. Gu, G. Pan, Overfitting remedy by sparsifying regularization on fully-connected layers of CNNs, Neurocomputing, 328 (2019), 69–74. https://doi.org/10.1016/j.neucom.2018.03.080 doi: 10.1016/j.neucom.2018.03.080
[38]	S. R. Dubey, S. K. Singh, B. B. Chaudhuri, Activation functions in deep learning: A comprehensive survey and benchmark, Neurocomputing, 503 (2022), 92–108. https://doi.org/10.1016/j.neucom.2022.06.111 doi: 10.1016/j.neucom.2022.06.111
[39]	S. Akbar, M. Peikari, S. Salama, S. Nofech-Mozes, A. Martel, The transition module: A method for preventing overfitting in convolutional neural networks, Comput. Methods Biomech. Biomed. Eng.: Imaging Vis., 7 (2019), 260–265. https://doi.org/10.1080/21681163.2018.1427148 doi: 10.1080/21681163.2018.1427148
[40]	H. Wu, X. Gu, Towards dropout training for convolutional neural networks, Neural Networks, 71 (2015), 1–10. https://doi.org/10.1016/j.neunet.2015.07.007 doi: 10.1016/j.neunet.2015.07.007
[41]	M. Anthimopoulos, S. Christodoulidis, L. Ebner, A. Christe, S. Mougiakakou, Lung pattern classification for interstitial lung diseases using a deep convolutional neural network, IEEE T. Med. Imaging, 35 (2016), 1207–1216. https://doi.org/10.1109/TMI.2016.2535865 doi: 10.1109/TMI.2016.2535865
[42]	J. Chen, Y. Shen, The effect of kernel size of CNNs for lung nodule classification, In: 2017 9th International Conference on Advanced Infocomm Technology (ICAIT), 2017,340–344. https://doi.org/10.1109/ICAIT.2017.8388942
[43]	B. Chen, W. Deng, J. Du, Noisy softmax: Improving the generalization ability of DCNN via postponing the early softmax saturation, In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, 4021–4030. https://doi.org/10.1109/CVPR.2017.428
[44]	Y. Bengio, P. Lamblin, D. Popovici, H. Larochelle, Greedy layer-wise training of deep networks, In: Advances in Neural Information Processing Systems 19 (NIPS 2006), 2006.
[45]	K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016,770–778. https://doi.org/10.1109/CVPR.2016.90
[46]	S. Han, J. Pool, J. Tran, W. J. Dally, Learning both weights and connections for efficient neural network, In: NIPS'15: Proceedings of the 28th International Conference on Neural Information Processing Systems, 1 (2015), 1135–1143.
[47]	P. Ochs, A. Dosovitskiy, T. Brox, T. Pock, On iteratively reweighted algorithms for nonsmooth nonconvex optimization in computer vision, SIAM J. Imaging Sci., 8 (2015), 331–372. https://doi.org/10.1137/140971518 doi: 10.1137/140971518
[48]	P. Murugan, S. Durairaj, Regularization and optimization strategies in deep convolutional neural network, 2017, arXiv: 1712.04711.
[49]	J. Snoek, O. Rippel, K. Swersky, R. Kiros, N. Satish, N. Sundaram, et al., Scalable Bayesian optimization using deep neural networks, In: ICML'15: Proceedings of the 32nd International Conference on International Conference on Machine Learning, 37 (2015), 2171–2180.
[50]	D. Cheng, Y. Gong, S. Zhou, J. Wang, N. Zheng, Person re-identification by multi-channel parts-based CNN with improved triplet loss function, In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, 1335–1344. https://doi.org/10.1109/CVPR.2016.149
[51]	Y. S. Aurelio, G. M. de Almeida, C. L. de Castro, A. P. Braga, Learning from imbalanced data sets with weighted cross-entropy function, Neural Process Lett., 50 (2019), 1937–1949. https://doi.org/10.1007/s11063-018-09977-1 doi: 10.1007/s11063-018-09977-1
[52]	M. Bouten, J. Schietse, C. Van. den Broeck, Gradient descent learning in perceptrons: A review of its possibilities, Phys. Rev. E, 52 (1995), 1958–1967. https://doi.org/10.1103/PhysRevE.52.1958 doi: 10.1103/PhysRevE.52.1958
[53]	A. El-Sawy, M. Loey, H. El-Bakry, Arabic handwritten characters recognition using convolutional neural network, WSEAS Trans. Comput. Res., 5 (2017), 11–19.
[54]	Y. Sun, W. Zhang, H. Gu, C. Liu, S. Hong, W. Xu, et al., Convolutional neural network based models for improving super-resolution imaging, IEEE Access, 7 (2019), 43042–43051. https://doi.org/10.1109/ACCESS.2019.2908501 doi: 10.1109/ACCESS.2019.2908501
[55]	G. D. Rubin, C. J. Ryerson, L. B. Haramati, N. Sverzellati, J. P. Kanne, S. Raoof, et al., The role of chest imaging in patient management during the COVID-19 pandemic: A multinational consensus statement from the Fleischner society, Radiology, 296 (2020), 172–180. https://doi.org/10.1148/radiol.2020201365 doi: 10.1148/radiol.2020201365
[56]	B. Xu, Y. Xing, J. Peng, Z. Zheng, W. Tang, Y. Sun, et al., Chest CT for detecting COVID-19: A systematic review and meta-analysis of diagnostic accuracy, Eur Radiol, 30 (2020), 5720–5727. https://doi.org/10.1007/s00330-020-06934-2 doi: 10.1007/s00330-020-06934-2
[57]	A. M. Rahmani, E. Azhir, M. Naserbakht, M. Mohammadi, A. H. M. Aldalwie, M. K. Majeed, et al., Automatic COVID-19 detection mechanisms and approaches from medical images: A systematic review, Multimed. Tools Appl., 81 (2022), 28779–28798. https://doi.org/10.1007/s11042-022-12952-7 doi: 10.1007/s11042-022-12952-7
[58]	E. E. Hemdan, M. A. Shouman, M. E. Karar, COVIDX-Net: A framework of deep learning classifiers to diagnose COVID-19 in X-ray images, 2020, arXiv: 2003.11055.
[59]	M. Polsinelli, L. Cinque, G. Placidi, A light CNN for detecting COVID-19 from CT scans of the chest, Pattern Recogn. Lett., 140 (2020), 95–100. https://doi.org/10.1016/j.patrec.2020.10.001 doi: 10.1016/j.patrec.2020.10.001
[60]	A. Narin, C. Kaya, Z. Pamuk, Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks, Pattern Anal, Appl., 24 (2021), 1207–1220. https://doi.org/10.1007/s10044-021-00984-y doi: 10.1007/s10044-021-00984-y
[61]	P. Mooney, Chest X-ray images (Pneumonia), 2018. Available from: https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia.
[62]	T. Rahman, COVID-19 radiography database, 2020. Available from: https://www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database.
[63]	I. D. Apostolopoulos, T. A. Mpesiana, Covid-19: Automatic detection from X-ray images utilizing transfer learning with convolutional neural networks, Phys. Eng. Sci. Med., 43 (2020), 635–640. https://doi.org/10.1007/s13246-020-00865-4 doi: 10.1007/s13246-020-00865-4
[64]	P. K. Sethy, S. K. Behera, P. K. Ratha, P. Biswas, Detection of coronavirus disease (COVID-19) based on deep features and support vector machine, Int. J. Math. Eng. Manage. Sci., 5 (2020), 643–651. https://doi.org/10.33889/IJMEMS.2020.5.4.052 doi: 10.33889/IJMEMS.2020.5.4.052
[65]	Y. Wang, M. Hu, Q. Li, X. Zhang, G. Zhai, N. Yao, Abnormal respiratory patterns classifier may contribute to large-scale screening of people infected with COVID-19 in an accurate and unobtrusive manner, 2020, arXiv: 2002.05534.
[66]	J. Zhang, Y. Xie, G. Pang, Z. Liao, J. Verjans, W. Li, et al., Viral pneumonia screening on chest X-rays using confidence-aware anomaly detection, IEEE T. Med. Imaging, 40 (2021), 879–890. https://doi.org/10.1109/TMI.2020.3040950 doi: 10.1109/TMI.2020.3040950
[67]	P. Afshar, S. Heidarian, F. Naderkhani, A. Oikonomou, K. N. Plataniotis, A. Mohammadi, COVID-CAPS: A capsule network-based framework for identification of COVID-19 cases from X-ray images, Pattern Recogn. Lett., 138 (2020), 638–643. https://doi.org/10.1016/j.patrec.2020.09.010 doi: 10.1016/j.patrec.2020.09.010
[68]	M. E. H. Chowdhury, T. Rahman, A. Khandakar, R. Mazhar, M. A. Kadir, Z. B. Mahbub, et al., Can AI help in screening viral and COVID-19 pneumonia? IEEE Access, 8 (2020), 132665–132676. https://doi.org/10.1109/ACCESS.2020.3010287 doi: 10.1109/ACCESS.2020.3010287
[69]	L. O. Hall, R. Paul, D. B. Goldgof, G. M. Goldgof, Finding Covid-19 from chest X-rays using deep learning on a small dataset, 2020, arXiv: 2004.02060.
[70]	T. Ozturk, M. Talo, E. A. Yildirim, U. B. Baloglu, O. Yildirim, U. R. Acharya, Automated detection of COVID-19 cases using deep neural networks with X-ray images, Comput. Biol. Med., 121 (2020), 103792. https://doi.org/10.1016/j.compbiomed.2020.103792 doi: 10.1016/j.compbiomed.2020.103792
[71]	R. M. Pereira, D. Bertolini, L. O. Teixeira, C. N. Silla, Y. M. G. Costa, COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios, Comput. Meth. Prog. Bio., 194 (2020), 105532. https://doi.org/10.1016/j.cmpb.2020.105532 doi: 10.1016/j.cmpb.2020.105532
[72]	L. Mahdy, K. Ezzat, H. Elmousalami, H. Ella, A. Hassanien, Automatic X-ray COVID-19 lung image classification system based on multi-level thresholding and support vector machine, 2020, medRxiv preprint. https://doi.org/10.1101/2020.03.30.20047787
[73]	K. El Asnaoui, Y. Chawki, A. Idri, Automated methods for detection and classification pneumonia based on X-ray images using deep learning, In: Artificial intelligence and blockchain for future cybersecurity applications, Springer, Cham, 2021,257–284. https://doi.org/10.1007/978-3-030-74575-2_14
[74]	D. Singh, V. Kumar, V. Kaur, M. Kaur, Classification of COVID-19 patients from chest CT images using multi-objective differential evolution–based convolutional neural networks, Eur. J. Clin. Microbiol. Infect. Dis., 39 (2020), 1379–1389. https://doi.org/10.1007/s10096-020-03901-z doi: 10.1007/s10096-020-03901-z
[75]	M. Yamac, M. Ahishali, A. Degerli, S. Kiranyaz, M. E. H. Chowdhury, M. Gabbouj, Convolutional sparse support estimator-based COVID-19 recognition from X-ray images, IEEE T. Neur. Net. Lear. Syst., 32 (2021), 1810–1820. https://doi.org/10.1109/TNNLS.2021.3070467 doi: 10.1109/TNNLS.2021.3070467
[76]	U. Özkaya, Ş. Öztürk, M. Barstugan, Coronavirus (COVID-19) classification using deep features fusion and ranking technique, In: Big Data Analytics and Artificial Intelligence Against COVID-19: Innovation Vision and Approach, Springer, Cham, 2020,281–295. https://doi.org/10.1007/978-3-030-55258-9_17
[77]	C. Salvatore, M. Interlenghi, C. Monti, D. Ippolito, D. Capra, A. Cozzi, et al., Artificial intelligence applied to chest X-ray for differential diagnosis of COVID-19 pneumonia, Diagnostics, 11 (2021), 530. https://doi.org/10.3390/diagnostics11030530 doi: 10.3390/diagnostics11030530
[78]	T. T. Nguyen, Q. V. H. Nguyen, D. T. Nguyen, S. Yang, P. W. Eklund, T. Huynh-The, et al., Artificial intelligence in the battle against coronavirus (COVID-19): A survey and future research directions, 2020, arXiv: 2008.07343.
[79]	E. Neri, V. Miele, F. Coppola, R. Grassi, Use of CT and artificial intelligence in suspected or COVID-19 positive patients: Statement of the Italian society of medical and interventional radiology, La radiologia medica, 125 (2020), 505–508. https://doi.org/10.1007/s11547-020-01197-9 doi: 10.1007/s11547-020-01197-9
[80]	L. Li, L. Qin, Z. Xu, Y. Yin, X. Wang, B. Kong, et al., Using artificial intelligence to detect COVID-19 and community-acquired pneumonia based on pulmonary CT: Evaluation of the diagnostic accuracy, Radiology, 296 (2020), E65–E71. https://doi.org/10.1148/radiol.2020200905 doi: 10.1148/radiol.2020200905
[81]	M. A. Amou, K. Xia, S. Kamhi, M. Mouhafid, A novel MRI diagnosis method for brain tumor classification based on CNN and Bayesian optimization, Healthcare, 10 (2022), 494. https://doi.org/10.3390/healthcare10030494 doi: 10.3390/healthcare10030494
[82]	S. Z. Kurdi, M. H. Ali, M. M. Jaber, T. Saba, A. Rehman, R. Damaševičius, Brain tumor classification using meta-heuristic optimized convolutional neural networks, J. Pers. Med, 13 (2023), 181. https://doi.org/10.3390/jpm13020181 doi: 10.3390/jpm13020181
[83]	E. Irmak, Multi-classification of brain tumor MRI images using deep convolutional neural network with fully optimized framework, Iran. J. Sci. Technol. Trans. Electr. Eng., 45 (2021), 1015–1036. https://doi.org/10.1007/s40998-021-00426-9 doi: 10.1007/s40998-021-00426-9
[84]	C. Venkatesh, K. Ramana, S. Y. Lakkisetty, S. S. Band, S. Agarwal, A. Mosavi, A neural network and optimization based lung cancer detection system in CT images, Front. Public Health, 10 (2022), 769692. https://doi.org/10.3389/fpubh.2022.769692 doi: 10.3389/fpubh.2022.769692
[85]	D. Paikaray, A. K. Mehta, D. A. Khan, Optimized convolutional neural network for the classification of lung cancer, J. Supercomput., 80 (2024), 1973–1989. https://doi.org/10.1007/s11227-023-05550-3 doi: 10.1007/s11227-023-05550-3
[86]	C. Lin, S. Jeng, M. Chen, Using 2D CNN with Taguchi parametric optimization for lung cancer recognition from CT images, Appl. Sci., 10 (2020), 2591. https://doi.org/10.3390/app10072591 doi: 10.3390/app10072591

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