آلة هجينة قوية ونموذج قائم على التعلم العميق للتصنيف والتعرف في صور الأشعة السينية للصدر

Jasim RZ. Biochemical Action of Vaccines in Iraqi Patients with COVID-19 Infection. Baghdad Sci J. 2023 Aug 30; 20(4 (SI)):1469-79. https://doi.org/10.21123/bsj.2023.8750 .

Alnedawe SM, Aljobouri HK. A New Model Design for Combating COVID-19 Pandemic Based on SVM and CNN Approaches. Baghdad Sci J. 2023 Aug 1; 20(4):1402-1413. https://doi.org/10.21123/bsj.2023.7403 .

Shanshool, Abeer M., Mariam Bouchakwa, and Ikram Amous-Ben Amor. Employing data mining techniques to classify Covid-19 pandemic. In AIP Conference Proceedings. 2024 3036(1). https://doi.org/10.1063/5.0196328.

Qin C, Yao D, Shi Y, Song Z. Computer-aided detection in chest radiography based on artificial intelligence: a survey. Biomed Eng online. 2018 Dec; 17: 1-23.https://doi.org/10.1186/s12938-018-0544-y .

Lodwick GS, Keats TE, Dorst JP. The coding of roentgen images for computer analysis as applied to lung cancer. Radiology. 1963 Aug; 81(2): 185-200. https://doi.org/10.1148/81.2.185 .

Zakirov AN, Kuleev RF, Timoshenko AS, Vladimirov AV. Advanced approaches to computer-aided detection of thoracic diseases on chest X-rays. Appl Math Sci. 2015; 9(88): 4361-9. https://doi.org/10.12988/ams.2015.54348 .

Yadessa AG, Salau AO. Low cost sensor based hand washing solution for COVID-19 prevention. In2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT) 2021 Sep 29; 93-97. IEEE. https://doi.org/10.1109/3ICT53449.2021.9581821 .

Carvalho K, Vicente JP, Jakovljevic M, Teixeira JP. Analysis and forecasting incidence, intensive care unit admissions, and projected mortality attributable to COVID-19 in Portugal, the UK, Germany, Italy, and France: predictions for 4 weeks ahead. Bioeng. 2021 Jun 11; 8(6): 84. https://doi.org/10.3390/bioengineering8060084 .

Shanshool AM, Salman AH, Rafash AG. A review study on machine learning approaches on coronavirus big data. J Al-Ma'moon Coll. 2022;1(37):431-59.

Ghrabat MJ, Ma G, Cheng C. Towards efficient for learning model image retrieval. In2018 14th International Conference on Semantics, Knowledge and Grids (SKG) 2018 Sep; 12: pp. 92-99. IEEE. https://doi.org/10.1109/SKG.2018.00020 .

Grima S, Rupeika-Apoga R, Kizilkaya M, Romānova I, Dalli Gonzi R, Jakovljevic M. A proactive approach to identify the exposure risk to COVID-19: validation of the pandemic risk exposure measurement (PREM) model using real-world data. Risk Management and Healthcare Policy. 2021 Nov 26:4775-87.https://doi.org/10.2147/RMHP.S341500 .

Chetoui M, Akhloufi MA. Explainable vision transformers and radiomics for covid-19 detection in chest x-rays. J Clin Med. 2022 May 26; 11(11): 3013. https://doi.org/10.3390/jcm11113013 .

Sahal R, Alsamhi SH, Brown KN, O’Shea D, Alouffi B. Blockchain‐Based Digital Twins Collaboration for Smart Pandemic Alerting: Decentralized COVID‐19 Pandemic Alerting Use Case. Comput Intell Neurosci. 2022; 2022(1): 7786441. https://doi.org/10.1155/2022/7786441 .

AlMohimeed A, Saleh H, El-Rashidy N, Saad RM, El-Sappagh S, Mostafa S. Diagnosis of Covid-19 using chest X-ray images and disease symptoms based on stacking ensemble deep learning. Diagnostics. 2023 Jun 5; 13(11): 1968. https://doi.org/10.3390/diagnostics13111968 .

Hong M, Rim B, Lee H, Jang H, Oh J, Choi S. Multi-class classification of lung diseases using CNN models. Appl Sci. 2021 Oct 6; 11(19):9289. https://doi.org/10.3390/app11199289 .

Lafraxo S, El Ansari M. Covinet: Automated covid-19 detection from x-rays using deep learning techniques. In2020 6th IEEE congress on information science and technology (CiSt) .2021 Jun 5; pp. 489-494. IEEE. https://doi.org/10.1109/CiSt49399.2021.9357250 .

Rezaee K, Badiei A, Meshgini S. A hybrid deep transfer learning based approach for COVID-19 classification in chest X-ray images. In2020 27th national and 5th international Iranian conference on biomedical engineering (ICBME) 2020 Nov 26; 234-241. IEEE. https://doi.org/10.1109/ICBME51989.2020.9319426 .

Anwar T, Zakir S. Deep learning based diagnosis of COVID-19 using chest CT-scan images. In2020 IEEE 23rd international multitopic conference (INMIC). 2020 Nov 5; (pp. 1-5). IEEE. https://doi.org/10.1109/INMIC50486.2020.9318212 .

Triveni V, Priyanka RG, Teja KD, Sangeetha Y. Programmable Detection of COVID-19 Infection Using Chest X-Ray Images Through Transfer Learning. In2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA). 2021 Sep 2; pp. 1486-1492. IEEE. https://doi.org/10.1109/ICIRCA51532.2021.9545050.

Karajah EN, Awad M. Covid-19 Detection From Chest X-Rays Using Modified VGG 16 Model. In2021 International Conference on Promising Electronic Technologies (ICPET) .2021 Nov 17; pp. 46-51. IEEE. https://doi.org/10.1109/ICPET53277.2021.00015 .

Al Majid AH, Wibowo A, Ramadhanti S, Zaneta D, Faoziya R, Maris V. Comparison of Models Architecture on Chest X-Ray Image Classification With Transfer Learning Algorithms. In2021 5th International Conference on Informatics and Computational Sciences (ICICoS) 2021 Nov 24;171-175. IEEE. https://doi.org/10.1109/ICICoS53627.2021.9651771 .

Kim S, Rim B, Choi S, Lee A, Min S, Hong M. Deep learning in multi-class lung diseases’ classification on chest X-ray images. Diagnostics. 2022 Apr 6; 12(4): 915.https://doi.org/10.1109/AIPR57179.2022.10092231 .

Nawaz M, Nazir T, Baili J, Khan MA, Kim YJ, Cha JH. Cxray-effdet: chest disease detection and classification from x-ray images using the efficientdet model. Diagnostics. 2023 Jan 9; 13(2): 248. https://doi.org/10.3390/diagnostics13020248 .

Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: synthetic minority over-sampling technique. J Artif Intell Res. 2002 Jun 1;16:321-57. https://doi.org/10.1613/jair.953 .

Huang, Gao, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q. Weinberger. Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2017 4700-4708.

Shiri FM, Perumal T, Mustapha N, Mohamed R. A comprehensive overview and comparative analysis on deep learning models: CNN, RNN, LSTM, GRU. arXiv preprint. arXiv: 2023 May; 2305: 27. https://doi.org/10.48550/arXiv.2305.17473

Gaikwad NB, Tiwari V, Keskar A, Shivaprakash NC. Efficient FPGA implementation of multilayer perceptron for real-time human activity classification. IEEE Access. 2019 Feb 24; 7: 26696-706. https://doi.org/10.1109/ACCESS.2019.2900084.

Wang SH, Zhang YD. DenseNet-201-based deep neural network with composite learning factor and precomputation for multiple sclerosis classification. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM). 2020 Jun 20; 16(2s): 1-9. https://doi.org/10.1145/3341095.

Shanshool AM, Bouchakwa M, Amor IA. Improved accuracy of pulmonary nodule classification on LIDC-IDRI dataset using deep learning. Procedia Comput Sci. 2023 Jan 1; 225: 394-403. https://doi.org/10.1016/j.procs.2023.10.024

Jain D, Singh V. A two-phase hybrid approach using feature selection and adaptive SVM for chronic disease classification. Int J Comput Appl. 2021 Jul 3; 43(6): 524-36. https://doi.org/10.1080/1206212X.2019.1577534 .

Wu JX, Chen PY, Li CM, Kuo YC, Pai NS, Lin CH. Multilayer fractional-order machine vision classifier for rapid typical lung diseases screening on digital chest X-ray images. IEEE Access. 2020 Jun 4;8: 105886-902. https://doi.org/10.1109/ACCESS.2020.3000186 .

Uparkar O, Bharti J, Pateriya RK, Gupta RK, Sharma A. Vision transformer outperforms deep convolutional neural network-based model in classifying X-ray images. Procedia Comput Sci. 2023 Jan 1; 218: 2338-49. https://doi.org/10.1016/j.procs.2023.01.209 .

Reshan MS, Gill KS, Anand V, Gupta S, Alshahrani H, Sulaiman A, Shaikh A. Detection of pneumonia from chest X-ray images utilizing mobilenet model. Healthcare. 2023 May 26; 11(11): 1561. MDPI. https://doi.org/10.3390/healthcare11111561.