Ekrem Eşref KILINÇ, Fahrettin AKA, Sedat METLEK

3BResNet: COVID19 Tespiti için Yeni Bir Artık Blok Tabanlı ResNet Modeli Yaklaşımı

Son yıllarda tüm dünyayı etkisi altına alan üst solunum yolu enfeksiyonları milyonlarca insanın ölümüne neden olmuştur. Önümüzdeki yıllarda da benzer enfeksiyonların yaşanabileceği öngörülmektedir. Bu nedenle özellikle salgın dönemlerinde yaygın olarak kullanılabilecek yöntemlerin geliştirilmesi gerekmektedir. Çalışmada üst solunum yolu enfeksiyonlarında kullanılmak üzere bir karar destek sistemi geliştirilmiştir. Bu aşamada öncelikle literatürde yer alan ResNet modelleri incelenmiş ve SARS-CoV-2 Ct veri seti üzerinde bir uygulama geliştirilmiştir. Sonraki aşamada literatürdeki ResNet modellerindeki blok yapısı değiştirilmiş, katman sayısı azaltılmış ve daha az parametre ile daha yüksek başarı sağlayan yeni bir model önerilmiştir. Önerilen model ile SARS-CoV-2 Ct veri kümesi üzerinde doğruluk, F1 skoru, hassasiyet ve duyarlılık için sırasıyla 0.97, 0.97, 0.94 ve 0.98 değerleri elde edilmiştir. Elde edilen değerler literatürdeki son teknoloji yöntemlerle kıyaslandığında çok daha az parametre ile rekabet edebilir düzeyde olduğu tespit edilmiştir. ResNet modellerinin düşük donanım seviyelerinde eğitilmesinde karşılaşılan donanım kaynaklı sorunlar önerilen model ile çözülerek daha yüksek başarı oranı elde edilmiştir. Ayrıca önerilen model, hafif yapısı ve yüksek performanslı sonuçları nedeniyle pandemi gibi olumsuz koşullarda acil ihtiyaç duyulan farklı karar destek sistemlerinde yaygın olarak kullanılabilecektir.

Anahtar Kelimeler:

Derin sinir ağı, Artık blok, ResNet, 3BResNet

3BResNet: A Novel Residual Block-Based ResNet Model Approach for COVID19 Detection

In recent years, upper respiratory tract infections that have affected the whole world have caused the death of millions of people. It is predicted that similar infections may occur in the coming years. Therefore, it is necessary to develop methods that can be used widely, especially during epidemic periods. The study developed a decision support system for use in upper respiratory tract infections. At this stage, first, the ResNet models in the literature were examined and an application was developed on the SARS-CoV-2 Ct dataset. Next stage, the block structure in the ResNet models in the literature was changed, the number of layers was reduced, and a new model was proposed that provides higher success with fewer parameters. With the proposed model, the values 0.97, 0.97, 0.94, and 0.98 were achieved for accuracy, F1 score, precision and sensitivity on the SARS-CoV-2 Ct dataset, respectively. When the obtained values are compared to state of the art methods in the literature, it has been determined that they are at a competitive level with much fewer parameters. Hardware-related problems encountered in the training of ResNet models at low hardware levels were solved with the proposed model, resulting in a higher success rate. Furthermore, the proposed model can be widely used in different decision support systems that are urgently needed in adverse conditions such as pandemics due to its lightweight structure and high-performance results.

Keywords:

Deep neural network, Residual block, ResNet, 3BResNet.,

PDF

___

[1] A. A. Ardakani, A. R. Kanafi, U. R. Acharya, N. Khadem, ve A. Mohammadi, “Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks”, Comput Biol Med, vol. 121, Haz. 2020, doi: 10.1016/j.compbiomed.2020.103795.
[2] M. E. H. Chowdhury vd., “Can AI Help in Screening Viral and COVID-19 Pneumonia?”, IEEE Access, vol. 8, pp. 132665-132676, 2020, doi: 10.1109/ACCESS.2020.3010287.
[3] Z. hui Chen, S. ping Wan, ve J. ying Dong, “An integrated interval-valued intuitionistic fuzzy technique for resumption risk assessment amid COVID-19 prevention”, Inf Sci (N Y), vol. 619, pp. 695-721, Oca. 2023, doi: 10.1016/j.ins.2022.11.028.
[4] X. Li, C. Li, ve D. Zhu, “Covid-mobilexpert: On-device covid-19 screening using snapshots of chest x-ray”, arXiv preprint arXiv:2004.03042, 2020.
[5] Çalışkan, A. (2023). “Diagnosis of malaria disease by integrating chi-square feature selection algorithm with convolutional neural networks and autoencoder network”, Transactions of the Institute of Measurement and Control, vol. 45, no. 5, pp. 975-985. https://doi.org/10.1177/01423312221147335.
[6] M. P. Cheng vd., “Diagnostic Testing for Severe Acute Respiratory Syndrome–Related Coronavirus 2”, Ann Intern Med, vol. 172, no 11, pp. 726-734, Nis. 2020, doi: 10.7326/M20-1301.
[7] X. He vd., “Sample-efficient deep learning for COVID-19 diagnosis based on CT scans”, in IEEE Transactions on Medical Imaging, p. 10, 2020. doi: 10.1101/2020.04.13.20063941.
[8] T. B. Chandra, K. Verma, B. K. Singh, D. Jain, ve S. S. Netam, “Coronavirus disease (COVID-19) detection in Chest X-Ray images using majority voting based classifier ensemble”, Expert Syst Appl, vol. 165, p. 113909, 2021, doi: https://doi.org/10.1016/j.eswa.2020.113909.
[9] A. Bernheim et al., “Chest CT findings in coronavirus disease 2019 (COVID-19): Relationship to duration of infection”, Radiology, vol. 295, no 3, pp. 200463, 2020. doi: 10.1148/radiol.2020200463.
[10] O. Gozes et al., “Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection & Patient Monitoring using Deep Learning CT Image Analysis”, 2020, [Çevrimiçi]. Erişim adresi: http://arxiv.org/abs/2003.05037
[11] R. Chelghoum, A. Ikhlef, A. Hameurlaine, and S. Jacquir, “Transfer learning using convolutional neural network architectures for brain tumor classification from MRI images”, in IFIP Advances in Information and Communication Technology, Springer International Publishing, 2020, pp. 189-200. doi: 10.1007/978-3-030-49161-1_17.
[12] S. Metlek, “A new proposal for the prediction of an aircraft engine fuel consumption: a novel CNN-BiLSTM deep neural network model”, Aircraft Engineering and Aerospace Technology, vol. 95, no 5, pp. 838-848, 2023, doi: 10.1108/AEAT-05-2022-0132.
[13] A. Halder and B. Datta, “COVID-19 detection from lung CT-scan images using transfer learning approach”, Mach Learn Sci Technol, vol. 2, no 4, p. 0450013, 2021. doi: 10.1088/2632-2153/abf22c.
[14] M. Usman, T. Zia, and A. Tariq, “Analyzing transfer learning of vision transformers for interpreting chest radiography”, J Digit Imaging, vol. 35, no. 6, pp. 1445-1462, 2022.
[15] C. Srinivas et al., “Deep transfer learning approaches in performance analysis of brain tumor classification using MRI images”, J Healthc Eng, vol. 2022, 2022.
[16] H. Aljuaid, N. Alturki, N. Alsubaie, L. Cavallaro, and A. Liotta, “Computer-aided diagnosis for breast cancer classification using deep neural networks and transfer learning”, Comput Methods Programs Biomed, vol. 223, p. 106951, 2022.
[17] M. Aly and N. S. Alotaibi, “A novel deep learning model to detect COVID-19 based on wavelet features extracted from Mel-scale spectrogram of patients’ cough and breathing sounds”, Inform Med Unlocked, vol. 32, p. 101049, 2022, doi: https://doi.org/10.1016/j.imu.2022.101049.
[18] L. K. Butola, R. Ambad, P. K. Kute, R. K. Jha, A. D. Shinde, and W. DMIMS, “The pandemic of 21st century-COVID-19”, Journal of evolution of medical and dental Sciences-JEMDS, vol. 9, no 39, pp. 2913-2918, 2020.
[19] Y. Zhao, B. R. Dong, and Q. Hao, “Probiotics for preventing acute upper respiratory tract infections”, Cochrane Libr, no. 8, 2022, doi: 10.1002/14651858.CD006895.pub4.
[20] A. Bianco, F. Licata, C. G. A. Nobile, F. Napolitano, ve M. Pavia, “Pattern and appropriateness of antibiotic prescriptions for upper respiratory tract infections in primary care paediatric patients”, Int J Antimicrob Agents, vol. 59, no 1, p. 106469, 2022. doi: https://doi.org/10.1016/j.ijantimicag.2021.106469.
[21] A. W. Bartlow vd., “Comparing variability in diagnosis of upper respiratory tract infections in patients using syndromic, next generation sequencing, and PCR-based methods”, PLOS Global Public Health, vol. 2, no 7, pp. e0000811, 2022. https://doi.org/10.1371/journal.pgph.0000811
[22] M. Farooq and A. Hafeez, “Covid-resnet: A deep learning framework for screening of covid19 from radiographs”, arXiv preprint arXiv:2003.14395, 2020.
[23] E. B. G. Kana, M. G. Z. Kana, A. F. D. Kana, and R. H. A. Kenfack, “A web-based Diagnostic Tool for COVID-19 Using Machine Learning on Chest Radiographs (CXR)”, medRxiv, s. 2020.04.21.20063263, 2020. doi: 10.1101/2020.04.21.20063263.
[24] A. Keles, M. B. Keles, and A. Keles, “COV19-CNNet and COV19-ResNet: diagnostic inference Engines for early detection of COVID-19”, Cognit Comput, pp. 1-11, 2021.
[25] R. Zhang et al., “COVID19XrayNet: A Two-Step Transfer Learning Model for the COVID-19 Detecting Problem Based on a Limited Number of Chest X-Ray Images”, Interdiscip Sci, vol. 12, no 4, pp. 555-565, 2020. doi: 10.1007/s12539-020-00393-5.
[26] M. M. Rahaman et al., “Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches”, J Xray Sci Technol, vol. 28, no. 5, pp. 821-839, 2020, doi: 10.3233/XST-200715.
[27] K. El Asnaoui and Y. Chawki, “Using X-ray images and deep learning for automated detection of coronavirus disease”, J Biomol Struct Dyn, vol. 39, no 10, pp. 3615-3626, 2021.
[28] X. Xu et al., “A deep learning system to screen novel coronavirus disease 2019 pneumonia”, Engineering, vol. 6, no 10, pp. 1122-1129, 2020.
[29] C. Zheng et al., “Deep Learning-based Detection for COVID-19 from Chest CT using Weak Label”, IEEE Trans Med Imaging, pp. 1-13, 2020, doi: 10.1101/2020.03.12.20027185.
[30] S. Hu et al., “Weakly supervised deep learning for covid-19 infection detection and classification from ct images”, IEEE Access, vol. 8, pp. 118869-118883, 2020.
[31] L. Li et al., “Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy”, Radiology, vol. 296, no 2, pp. E65-E71, Mar. 2020, doi: 10.1148/radiol.2020200905.
[32] Y. Song et al., “Deep learning enables accurate diagnosis of novel coronavirus (COVID-19) with CT images”, IEEE/ACM Trans Comput Biol Bioinform, vol. 18, no 6, pp. 2775-2780, 2021.
[33] V. Shah, R. Keniya, A. Shridharani, M. Punjabi, J. Shah, and N. Mehendale, “Diagnosis of COVID-19 using CT scan images and deep learning techniques”, Emerg Radiol, vol. 28, no 3, pp. 497-505, 2021.
[34] S. Metlek, “Forecasting of Dow Jones sukuk index prices using artificial intelligence systems.”, Econ Comput Econ Cybern Stud Res, vol. 56, no 1, 2022.
[35] K. O’Shea ve R. Nash, “An Introduction to Convolutional Neural Networks”, CoRR, c. abs/1511.0, 2015, [Çevrimiçi]. Erişim adresi: http://arxiv.org/abs/1511.08458
[36] R. Yamashita, M. Nishio, R. K. G. Do, and K. Togashi, “Convolutional neural networks: an overview and application in radiology”, Insights into Imaging, vol. 9, no 4. pp. 611-629, 2018. doi: 10.1007/s13244-018-0639-9.
[37] J. Gu et al., “Recent advances in convolutional neural networks”, Pattern Recognit, vol. 77, pp. 354-377, 2018. doi: 10.1016/j.patcog.2017.10.013.
[38] S. Albawi, T. A. Mohammed, ve S. Al-Zawi, “Understanding of a convolutional neural network”, in 2017 International Conference on Engineering and Technology, (ICET) 2017. doi: 10.1109/ICEngTechnol.2017.8308186.
[39] A. Arı and D. Hanbay, “Bölgesel Evrişimsel Sinir Ağları Tabanlı MR Görüntülerinde Tümör Tespiti”, Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, vol. 2018, no 18-2, 2018. doi: 10.17341/gazimmfd.460535.
[40] M. Tokmak and A. Kıraç, “Classification of Some Species of Shrikes Family by Convolutional Neural Networks”, Bilge International Journal of Science and Technology Research, vol. 5, no. 1, pp. 72-79 2021, doi: 10.30516/bilgesci.886291.
[41] K. He, X. Zhang, S. Ren, ve J. Sun, “Deep residual learning for image recognition”, in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. doi: 10.1109/CVPR.2016.90.
[42] Z. Lu et al., “The classification of gliomas based on a Pyramid dilated convolution resnet model”, Pattern Recognit Lett, vol. 133, pp. 173-179, 2020, doi: https://doi.org/10.1016/j.patrec.2020.03.007.
[43] Q. A. Al-Haija ve A. Adebanjo, “Breast cancer diagnosis in histopathological images using ResNet-50 convolutional neural network”, in 2020 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), 2020. doi: 10.1109/IEMTRONICS51293.2020.9216455.
[44] E. Soares, P. Angelov, S. Biaso, M. H. Froes, ve D. K. Abe, “SARS-CoV-2 CT-scan dataset: A large dataset of real patients CT scans for SARS-CoV-2 identification”, medRxiv, s. 2020.04.24.20078584, Oca. 2020, doi: 10.1101/2020.04.24.20078584.
[45] K. Maharana, S. Mondal, ve B. Nemade, “A review: Data pre-processing and data augmentation techniques”, Global Transitions Proceedings, vol. 3, no 1, pp. 91-99, 2022, doi: https://doi.org/10.1016/j.gltp.2022.04.020.
[46] O. Turk, D. Ozhan, E. Acar, T. C. Akinci, & M. Yilmaz, (2022). “Automatic detection of brain tumors with the aid of ensemble deep learning architectures and class activation map indicators by employing magnetic resonance images,” Z. Med. Phys., 2022. https://doi.org/10.1016/j.zemedi.2022.11.010.
[47] S. Siddiqui vd., “Deep Learning Models for the Diagnosis and Screening of COVID-19: A Systematic Review”, SN Computer Science, vol. 3, no 5, 2022. doi: 10.1007/s42979-022-01326-3.
[48] E. Soares, P. Angelov, S. Biaso, M. H. Froes, ve D. K. Abe, “SARS-CoV-2 CT-scan dataset: A large dataset of real patients CT scans for SARS-CoV-2 identification”, medRxiv, p. 2020.04.24.20078584, Oca. 2020, doi: 10.1101/2020.04.24.20078584.
[49] A. Jaiswal, N. Gianchandani, D. Singh, V. Kumar, ve M. Kaur, “Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning”, J Biomol Struct Dyn, vol. 39, no 15, pp. 5682-5689, 2021, doi: 10.1080/07391102.2020.1788642.
[50] H. Panwar, P. Gupta, M. K. Siddiqui, R. Morales-Menendez, P. Bhardwaj, ve V. Singh, “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, vol. 140, no. 110190, p. 110190, Ağu. 2020, doi: 10.1016/j.chaos.2020.110190.
[51] S. Gupta, P. Aggarwal, N. Chaubey, and A. Panwar, “Accurate prognosis of Covid-19 using CT scan images with deep learning model and machine learning classifiers”, Indian Journal of Radio &Space Physics,vol.50, no. 1, pp.19-24, 2021.
[52] K. L. Kohsasih ve B. H. Hayadi, “Classification SARS-CoV-2 Disease based on CT-Scan Image Using Convolutional Neural Network”, Scientific Journal of Informatics, vol. 9, no 2, pp. 197-204, Kas. 2022, doi: 10.15294/sji.v9i2.36583.