{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T13:05:58Z","timestamp":1784120758250,"version":"3.55.0"},"reference-count":89,"publisher":"Springer Science and Business Media LLC","issue":"21","license":[{"start":{"date-parts":[[2024,1,3]],"date-time":"2024-01-03T00:00:00Z","timestamp":1704240000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,3]],"date-time":"2024-01-03T00:00:00Z","timestamp":1704240000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-023-17884-4","type":"journal-article","created":{"date-parts":[[2024,1,3]],"date-time":"2024-01-03T05:02:45Z","timestamp":1704258165000},"page":"60655-60687","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Small size CNN-Based COVID-19 Disease Prediction System using CT scan images on PaaS cloud"],"prefix":"10.1007","volume":"83","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9670-3020","authenticated-orcid":false,"given":"Madhusudan G.","family":"Lanjewar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kamini G.","family":"Panchbhai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Panem","family":"Charanarur","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,1,3]]},"reference":[{"key":"17884_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2021\/5554408","volume":"2021","author":"AB Salem Salamh","year":"2021","unstructured":"Salem Salamh AB, Salamah AA, Aky\u00fcz HI (2021) A study of a new technique of the CT scan view and disease classification protocol based on Level challenges in cases of Coronavirus Disease. Radiol Res Pract 2021:1\u20139. https:\/\/doi.org\/10.1155\/2021\/5554408","journal-title":"Radiol Res Pract"},{"key":"17884_CR2","doi-asserted-by":"publisher","first-page":"839","DOI":"10.1016\/j.jestch.2020.12.026","volume":"24","author":"H Arslan","year":"2021","unstructured":"Arslan H, Arslan H (2021) A new COVID-19 detection method from human genome sequences using CpG island features and KNN classifier. Eng Sci Technol Int J 24:839\u2013847. https:\/\/doi.org\/10.1016\/j.jestch.2020.12.026","journal-title":"Eng Sci Technol Int J"},{"key":"17884_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.106849","volume":"218","author":"C Li","year":"2021","unstructured":"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. https:\/\/doi.org\/10.1016\/j.knosys.2021.106849","journal-title":"Knowl Based Syst"},{"key":"17884_CR4","doi-asserted-by":"publisher","DOI":"10.1007\/s12065-020-00540-3","author":"DR Sarvamangala","year":"2021","unstructured":"Sarvamangala DR, Kulkarni RV (2021) Convolutional neural networks in medical image understanding: a survey. Evol Intel. https:\/\/doi.org\/10.1007\/s12065-020-00540-3","journal-title":"Evol Intel"},{"key":"17884_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2020.100427","volume":"20","author":"P Silva","year":"2020","unstructured":"Silva P, Luz E, Silva G et al (2020) COVID-19 detection in CT images with deep learning: a voting-based scheme and cross-datasets analysis. Inf Med Unlocked 20:100427. https:\/\/doi.org\/10.1016\/j.imu.2020.100427","journal-title":"Inf Med Unlocked"},{"key":"17884_CR6","doi-asserted-by":"publisher","first-page":"1240","DOI":"10.1109\/TMI.2016.2538465","volume":"35","author":"S Pereira","year":"2016","unstructured":"Pereira S, Pinto A, Alves V, Silva CA (2016) Brain Tumor segmentation using Convolutional neural networks in MRI images. IEEE Trans Med Imaging 35:1240\u20131251. https:\/\/doi.org\/10.1109\/TMI.2016.2538465","journal-title":"IEEE Trans Med Imaging"},{"key":"17884_CR7","doi-asserted-by":"publisher","DOI":"10.1148\/ryct.2020200034","volume":"2","author":"M-Y Ng","year":"2020","unstructured":"Ng M-Y, Lee EYP, Yang J et al (2020) Imaging profile of the COVID-19 Infection: radiologic findings and literature review. Radiol: Cardiothorac Imaging 2:e200034. https:\/\/doi.org\/10.1148\/ryct.2020200034","journal-title":"Radiol: Cardiothorac Imaging"},{"key":"17884_CR8","doi-asserted-by":"publisher","unstructured":"Li T, Han Z, Wei B et al (2020) Robust screening of COVID-19 from chest x-ray via discriminative cost-sensitive learning. https:\/\/doi.org\/10.48550\/ARXIV.2004.12592","DOI":"10.48550\/ARXIV.2004.12592"},{"key":"17884_CR9","doi-asserted-by":"publisher","first-page":"931","DOI":"10.1016\/j.jestch.2020.01.005","volume":"23","author":"A Kececi","year":"2020","unstructured":"Kececi A, Yildirak A, Ozyazici K et al (2020) Implementation of machine learning algorithms for gait recognition. Eng Sci Technol Int J 23:931\u2013937. https:\/\/doi.org\/10.1016\/j.jestch.2020.01.005","journal-title":"Eng Sci Technol Int J"},{"key":"17884_CR10","doi-asserted-by":"publisher","first-page":"1131","DOI":"10.1080\/19440049.2023.2241557","volume":"40","author":"MG Lanjewar","year":"2023","unstructured":"Lanjewar MG, Morajkar PP, Parab JS (2023) Hybrid method for accurate starch estimation in adulterated turmeric using Vis-NIR spectroscopy. Food Addit Contaminants: Part A 40:1131\u20131146. https:\/\/doi.org\/10.1080\/19440049.2023.2241557","journal-title":"Food Addit Contaminants: Part A"},{"key":"17884_CR11","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1016\/j.jestch.2020.07.008","volume":"24","author":"\u0130 Atli","year":"2021","unstructured":"Atli \u0130, Gedik OS (2021) Sine-Net: a fully convolutional deep learning architecture for retinal blood vessel segmentation. Eng Sci Technol Int J 24:271\u2013283. https:\/\/doi.org\/10.1016\/j.jestch.2020.07.008","journal-title":"Eng Sci Technol Int J"},{"key":"17884_CR12","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-023-16886-6","author":"MG Lanjewar","year":"2023","unstructured":"Lanjewar MG, Parab JS (2023) CNN and transfer learning methods with augmentation for citrus leaf Diseases detection using PaaS cloud on mobile. Multimed Tools Appl. https:\/\/doi.org\/10.1007\/s11042-023-16886-6","journal-title":"Multimed Tools Appl"},{"key":"17884_CR13","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.ijcce.2022.01.004","volume":"3","author":"S Kalaivani","year":"2022","unstructured":"Kalaivani S, Seetharaman K (2022) A three-stage ensemble boosted convolutional neural network for classification and analysis of COVID-19 chest x-ray images. Int J Cogn Comput Eng 3:35\u201345. https:\/\/doi.org\/10.1016\/j.ijcce.2022.01.004","journal-title":"Int J Cogn Comput Eng"},{"key":"17884_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.advengsoft.2022.103317","volume":"175","author":"ND Kathamuthu","year":"2023","unstructured":"Kathamuthu ND, Subramaniam S, Le QH et al (2023) A deep transfer learning-based convolution neural network model for COVID-19 detection using computed tomography scan images for medical applications. Adv Eng Softw 175:103317. https:\/\/doi.org\/10.1016\/j.advengsoft.2022.103317","journal-title":"Adv Eng Softw"},{"key":"17884_CR15","doi-asserted-by":"publisher","first-page":"16591","DOI":"10.1007\/s11042-022-13820-0","volume":"82","author":"E Hassan","year":"2023","unstructured":"Hassan E, Shams MY, Hikal NA, Elmougy S (2023) The effect of choosing optimizer algorithms to improve computer vision tasks: a comparative study. Multimed Tools Appl 82:16591\u201316633. https:\/\/doi.org\/10.1007\/s11042-022-13820-0","journal-title":"Multimed Tools Appl"},{"key":"17884_CR16","doi-asserted-by":"publisher","unstructured":"Selvaraju RR, Cogswell M, Das A et al (2016) Grad-CAM: visual explanations from deep networks via gradient-based localization. https:\/\/doi.org\/10.48550\/ARXIV.1610.02391","DOI":"10.48550\/ARXIV.1610.02391"},{"key":"17884_CR17","doi-asserted-by":"publisher","first-page":"891","DOI":"10.1016\/j.jestch.2019.11.002","volume":"23","author":"MS Sanaj","year":"2020","unstructured":"Sanaj MS, Joe Prathap PM (2020) Nature inspired chaotic squirrel search algorithm (CSSA) for multi objective task scheduling in an IAAS cloud computing atmosphere. Eng Sci Technol Int J 23:891\u2013902. https:\/\/doi.org\/10.1016\/j.jestch.2019.11.002","journal-title":"Eng Sci Technol Int J"},{"key":"17884_CR18","doi-asserted-by":"publisher","first-page":"595","DOI":"10.1007\/s12065-019-00337-z","volume":"14","author":"K Padmaja","year":"2021","unstructured":"Padmaja K, Seshadri R (2021) Analytics on real time security Attacks in healthcare, retail and banking applications in the cloud. Evol Intel 14:595\u2013605. https:\/\/doi.org\/10.1007\/s12065-019-00337-z","journal-title":"Evol Intel"},{"key":"17884_CR19","doi-asserted-by":"publisher","first-page":"821","DOI":"10.3233\/XST-200715","volume":"28","author":"MM Rahaman","year":"2020","unstructured":"Rahaman MM, Li C, Yao Y et al (2020) Identification of COVID-19 samples from chest X-Ray images using deep learning: a comparison of transfer learning approaches. XST 28:821\u2013839. https:\/\/doi.org\/10.3233\/XST-200715","journal-title":"XST"},{"key":"17884_CR20","doi-asserted-by":"publisher","first-page":"3615","DOI":"10.1080\/07391102.2020.1767212","volume":"39","author":"K El Asnaoui","year":"2021","unstructured":"El Asnaoui K, Chawki Y (2021) Using X-ray images and deep learning for automated detection of coronavirus Disease. J Biomol Struct Dynamics 39:3615\u20133626. https:\/\/doi.org\/10.1080\/07391102.2020.1767212","journal-title":"J Biomol Struct Dynamics"},{"key":"17884_CR21","doi-asserted-by":"publisher","unstructured":"Zhang J, Xie Y, Pang G et al (2020) Viral pneumonia screening on chest x-ray images using confidence-aware anomaly detection. https:\/\/doi.org\/10.48550\/ARXIV.2003.12338","DOI":"10.48550\/ARXIV.2003.12338"},{"key":"17884_CR22","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-76550-z","author":"L Wang","year":"2020","unstructured":"Wang L, Wong A (2020) COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-Ray images. Sci Rep. https:\/\/doi.org\/10.1038\/s41598-020-76550-z. (arXiv:200309871 [cs, eess])","journal-title":"Sci Rep"},{"key":"17884_CR23","doi-asserted-by":"publisher","unstructured":"Abbas A, Abdelsamea MM, Gaber MM (2020) Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network. https:\/\/doi.org\/10.48550\/ARXIV.2003.13815","DOI":"10.48550\/ARXIV.2003.13815"},{"key":"17884_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105581","volume":"196","author":"AI Khan","year":"2020","unstructured":"Khan AI, Shah JL, Bhat M (2020) CoroNet: a deep neural network for detection and diagnosis of COVID-19 from chest x-ray images. Comput Methods Programs Biomed 196:105581. https:\/\/doi.org\/10.1016\/j.cmpb.2020.105581","journal-title":"Comput Methods Programs Biomed"},{"key":"17884_CR25","doi-asserted-by":"publisher","unstructured":"Maghdid HS, Asaad AT, Ghafoor KZ et al (2020) Diagnosing COVID-19 pneumonia from x-ray and CT images using deep learning and transfer learning algorithms. https:\/\/doi.org\/10.48550\/ARXIV.2004.00038","DOI":"10.48550\/ARXIV.2004.00038"},{"key":"17884_CR26","doi-asserted-by":"publisher","unstructured":"Rehman A, Naz S, Khan A et al (2020) Improving Coronavirus (COVID-19) diagnosis using deep transfer learning. Infectious Diseases (except HIV\/AIDS). https:\/\/doi.org\/10.1101\/2020.04.11.20054643","DOI":"10.1101\/2020.04.11.20054643"},{"key":"17884_CR27","doi-asserted-by":"publisher","unstructured":"Sarker L, Islam MM, Hannan T, Ahmed Z (2020) COVID-DenseNet: a Deep Learning Architecture to detect COVID-19 from chest radiology images. Math Comput Sci. https:\/\/doi.org\/10.20944\/preprints202005.0151.v1","DOI":"10.20944\/preprints202005.0151.v1"},{"key":"17884_CR28","doi-asserted-by":"publisher","first-page":"2798","DOI":"10.1109\/JBHI.2020.3019505","volume":"24","author":"L Sun","year":"2020","unstructured":"Sun L, Mo Z, Yan F et al (2020) Adaptive feature selection guided Deep Forest for COVID-19 classification with chest CT. IEEE J Biomed Health Inform 24:2798\u20132805. https:\/\/doi.org\/10.1109\/JBHI.2020.3019505","journal-title":"IEEE J Biomed Health Inform"},{"key":"17884_CR29","doi-asserted-by":"publisher","first-page":"2615","DOI":"10.1109\/TMI.2020.2995965","volume":"39","author":"X Wang","year":"2020","unstructured":"Wang X, Deng X, Fu Q et al (2020) A weakly-supervised framework for COVID-19 classification and lesion localization from chest CT. IEEE Trans Med Imaging 39:2615\u20132625. https:\/\/doi.org\/10.1109\/TMI.2020.2995965","journal-title":"IEEE Trans Med Imaging"},{"key":"17884_CR30","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1016\/j.eng.2020.04.010","volume":"6","author":"X Xu","year":"2020","unstructured":"Xu X, Jiang X, Ma C et al (2020) Deep learning system to screen coronavirus Disease 2019 Pneumonia. Engineering 6:1122\u20131129. https:\/\/doi.org\/10.1016\/j.eng.2020.04.010","journal-title":"Engineering"},{"key":"17884_CR31","doi-asserted-by":"publisher","DOI":"10.1101\/2020.04.13.20063941","author":"X He","year":"2020","unstructured":"He X, Yang X, Zhang S et al (2020) Sample-efficient deep learning for COVID-19 diagnosis based on CT scans. Health Inf. https:\/\/doi.org\/10.1101\/2020.04.13.20063941","journal-title":"Health Inf"},{"key":"17884_CR32","doi-asserted-by":"publisher","first-page":"E65","DOI":"10.1148\/radiol.2020200905","volume":"296","author":"L Li","year":"2020","unstructured":"Li L, Qin L, Xu Z et al (2020) Using artificial intelligence to detect COVID-19 and community-acquired Pneumonia based on pulmonary CT: evaluation of the diagnostic accuracy. Radiology 296:E65\u2013E71. https:\/\/doi.org\/10.1148\/radiol.2020200905","journal-title":"Radiology"},{"key":"17884_CR33","doi-asserted-by":"publisher","unstructured":"Song Y, Zheng S, Li L et al (2021) Deep learning enables accurate diagnosis of novel coronavirus (COVID-19) with CT images. IEEE\/ACM Trans Comput Biol and Bioinf 1\u20131.https:\/\/doi.org\/10.1109\/TCBB.2021.3065361","DOI":"10.1109\/TCBB.2021.3065361"},{"key":"17884_CR34","doi-asserted-by":"publisher","first-page":"E156","DOI":"10.1148\/radiol.2020201491","volume":"296","author":"HX Bai","year":"2020","unstructured":"Bai HX, Wang R, Xiong Z et al (2020) Artificial intelligence augmentation of radiologist performance in distinguishing COVID-19 from Pneumonia of other origin at chest CT. Radiology 296:E156\u2013E165. https:\/\/doi.org\/10.1148\/radiol.2020201491","journal-title":"Radiology"},{"key":"17884_CR35","doi-asserted-by":"publisher","unstructured":"Gozes O, Frid-Adar M, Greenspan H et al (2020) Rapid AI development cycle for the coronavirus (COVID-19) pandemic: initial results for automated detection & patient monitoring using deep learning CT image analysis. https:\/\/doi.org\/10.48550\/ARXIV.2003.05037","DOI":"10.48550\/ARXIV.2003.05037"},{"key":"17884_CR36","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1007\/s10140-020-01886-y","volume":"28","author":"V Shah","year":"2021","unstructured":"Shah V, Keniya R, Shridharani A et al (2021) Diagnosis of COVID-19 using CT scan images and deep learning techniques. Emerg Radiol 28:497\u2013505. https:\/\/doi.org\/10.1007\/s10140-020-01886-y","journal-title":"Emerg Radiol"},{"key":"17884_CR37","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-05437-x","author":"M Loey","year":"2020","unstructured":"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. https:\/\/doi.org\/10.1007\/s00521-020-05437-x","journal-title":"Neural Comput Appl"},{"key":"17884_CR38","doi-asserted-by":"publisher","unstructured":"Mobiny A, Cicalese PA, Zare S et al (2020) Radiologist-level COVID-19 detection using CT scans with detail-oriented capsule networks. https:\/\/doi.org\/10.48550\/ARXIV.2004.07407","DOI":"10.48550\/ARXIV.2004.07407"},{"key":"17884_CR39","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/j.patrec.2020.10.001","volume":"140","author":"M Polsinelli","year":"2020","unstructured":"Polsinelli M, Cinque L, Placidi G (2020) A light CNN for detecting COVID-19 from CT scans of the chest. Pattern Recognit Lett 140:95\u2013100. https:\/\/doi.org\/10.1016\/j.patrec.2020.10.001","journal-title":"Pattern Recognit Lett"},{"key":"17884_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2020\/8843664","volume":"2020","author":"AK Mishra","year":"2020","unstructured":"Mishra AK, Das SK, Roy P, Bandyopadhyay S (2020) Identifying COVID19 from chest CT images: a deep convolutional neural networks based Approach. J Healthc Eng 2020:1\u20137. https:\/\/doi.org\/10.1155\/2020\/8843664","journal-title":"J Healthc Eng"},{"key":"17884_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106270","volume":"205","author":"WM Shaban","year":"2020","unstructured":"Shaban WM, Rabie AH, Saleh AI, Abo-Elsoud MA (2020) A new COVID-19 patients Detection Strategy (CPDS) based on hybrid feature selection and enhanced KNN classifier. Knowl Based Syst 205:106270. https:\/\/doi.org\/10.1016\/j.knosys.2020.106270","journal-title":"Knowl Based Syst"},{"key":"17884_CR42","doi-asserted-by":"publisher","unstructured":"Javaheri T, Homayounfar M, Amoozgar Z et al (2020) CovidCTNet: an open-source deep learning approach to identify COVID-19 using CT image. https:\/\/doi.org\/10.48550\/ARXIV.2005.03059","DOI":"10.48550\/ARXIV.2005.03059"},{"key":"17884_CR43","doi-asserted-by":"publisher","first-page":"2000775","DOI":"10.1183\/13993003.00775-2020","volume":"56","author":"S Wang","year":"2020","unstructured":"Wang S, Zha Y, Li W et al (2020) A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis. Eur Respir J 56:2000775. https:\/\/doi.org\/10.1183\/13993003.00775-2020","journal-title":"Eur Respir J"},{"key":"17884_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.104037","volume":"126","author":"A Amyar","year":"2020","unstructured":"Amyar A, Modzelewski R, Li H, Ruan S (2020) Multi-task deep learning based CT imaging analysis for COVID-19 Pneumonia: classification and segmentation. Comput Biol Med 126:104037. https:\/\/doi.org\/10.1016\/j.compbiomed.2020.104037","journal-title":"Comput Biol Med"},{"key":"17884_CR45","doi-asserted-by":"publisher","first-page":"2626","DOI":"10.1109\/TMI.2020.2996645","volume":"39","author":"D-P Fan","year":"2020","unstructured":"Fan D-P, Zhou T, Ji G-P et al (2020) Inf-Net: automatic COVID-19 lung Infection segmentation from CT images. IEEE Trans Med Imaging 39:2626\u20132637. https:\/\/doi.org\/10.1109\/TMI.2020.2996645","journal-title":"IEEE Trans Med Imaging"},{"key":"17884_CR46","doi-asserted-by":"publisher","first-page":"14353","DOI":"10.1038\/s41598-021-93832-2","volume":"11","author":"W Zhao","year":"2021","unstructured":"Zhao W, Jiang W, Qiu X (2021) Deep learning for COVID-19 detection based on CT images. Sci Rep 11:14353. https:\/\/doi.org\/10.1038\/s41598-021-93832-2","journal-title":"Sci Rep"},{"key":"17884_CR47","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph20021268","volume":"20","author":"A Hayat","year":"2023","unstructured":"Hayat A, Baglat P, Mendon\u00e7a F et al (2023) Novel comparative study for the detection of COVID-19 using CT scan and chest X-ray images. IJERPH 20:1268. https:\/\/doi.org\/10.3390\/ijerph20021268","journal-title":"IJERPH"},{"key":"17884_CR48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2023\/4301745","volume":"2023","author":"Md Foysal","year":"2023","unstructured":"Foysal Md, Hossain ABMA, Yassine A, Hossain MS (2023) Detection of COVID-19 case from chest CT images using deformable deep convolutional neural network. J Healthc Eng 2023:1\u201312. https:\/\/doi.org\/10.1155\/2023\/4301745","journal-title":"J Healthc Eng"},{"key":"17884_CR49","doi-asserted-by":"publisher","DOI":"10.3390\/healthcare11091204","volume":"11","author":"T Althaqafi","year":"2023","unstructured":"Althaqafi T, AL-Ghamdi ASA-M, Ragab M (2023) Artificial intelligence based COVID-19 detection and classification model on chest X-ray images. Healthcare 11:1204. https:\/\/doi.org\/10.3390\/healthcare11091204","journal-title":"Healthcare"},{"key":"17884_CR50","doi-asserted-by":"publisher","DOI":"10.1007\/s12530-023-09511-2","author":"E Soares","year":"2023","unstructured":"Soares E, Angelov P, Biaso S et al (2023) A large multiclass dataset of CT scans for COVID-19 identification. Evol Syst. https:\/\/doi.org\/10.1007\/s12530-023-09511-2","journal-title":"Evol Syst"},{"key":"17884_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120477","volume":"229","author":"SH Khan","year":"2023","unstructured":"Khan SH, Iqbal J, Hassnain SA et al (2023) COVID-19 detection and analysis from lung CT images using novel channel boosted CNNs. Expert Syst Appl 229:120477. https:\/\/doi.org\/10.1016\/j.eswa.2023.120477","journal-title":"Expert Syst Appl"},{"key":"17884_CR52","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2023.1025746","volume":"11","author":"A Marefat","year":"2023","unstructured":"Marefat A, Marefat M, Hassannataj Joloudari J et al (2023) CCTCOVID: COVID-19 detection from chest X-ray images using Compact Convolutional transformers. Front Public Health 11:1025746. https:\/\/doi.org\/10.3389\/fpubh.2023.1025746","journal-title":"Front Public Health"},{"key":"17884_CR53","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1111\/coin.12568","volume":"39","author":"S Roy","year":"2023","unstructured":"Roy S, Das AK (2023) Deep-CoV: an integrated deep learning model to detect COVID \u201019 using chest X\u2010ray and CT images. Comput Intell 39:369\u2013400. https:\/\/doi.org\/10.1111\/coin.12568","journal-title":"Comput Intell"},{"key":"17884_CR54","doi-asserted-by":"publisher","unstructured":"Soares E, Angelov P, Biaso S et al (2020) SARS-CoV-2 CT-scan dataset: A large dataset of real patients CT scans for SARS-CoV-2 identification. medRxiv 2020.04.24.20078584. https:\/\/doi.org\/10.1101\/2020.04.24.20078584","DOI":"10.1101\/2020.04.24.20078584"},{"key":"17884_CR55","doi-asserted-by":"publisher","first-page":"2869","DOI":"10.1038\/s41598-019-38966-0","volume":"9","author":"W Liang","year":"2019","unstructured":"Liang W, Zhang H, Zhang G, Cao H (2019) Rice blast disease recognition using a deep convolutional neural network. Sci Rep 9:2869. https:\/\/doi.org\/10.1038\/s41598-019-38966-0","journal-title":"Sci Rep"},{"key":"17884_CR56","unstructured":"Skalski P (2019) Gentle dive into math behind convolutional neural networks. In: Medium. https:\/\/towardsdatascience.com\/gentle-dive-into-math-behind-convolutional-neural-networks-79a07dd44cf9. Accessed 12 Aug 2023"},{"key":"17884_CR57","unstructured":"(2021) Batch Normalization | What is Batch Normalization in Deep Learning. In: Analytics Vidhya. https:\/\/www.analyticsvidhya.com\/blog\/2021\/03\/introduction-to-batch-normalization\/. Accessed 11 Aug 2021"},{"key":"17884_CR58","unstructured":"CS231n Convolutional Neural Networks for Visual Recognition. https:\/\/cs231n.github.io\/convolutional-networks\/. Accessed 11 Aug 2021"},{"key":"17884_CR59","unstructured":"Dertat A (2017) Applied deep learning - Part 4: convolutional neural networks. In: Medium. https:\/\/towardsdatascience.com\/applied-deep-learning-part-4-convolutional-neural-networks-584bc134c1e2. Accessed 11 Aug 2021"},{"key":"17884_CR60","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton G, Krizhevsky A et al (2014) Dropout: a simple way to prevent neural networks from Overfitting. J Mach Learn Res 15:1929\u20131958","journal-title":"J Mach Learn Res"},{"key":"17884_CR61","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecoinf.2020.101197","volume":"61","author":"RC Joshi","year":"2021","unstructured":"Joshi RC, Kaushik M, Dutta MK et al (2021) VirLeafNet: automatic analysis and viral Disease diagnosis using deep-learning in Vigna mungo plant. Ecol Inf 61:101197. https:\/\/doi.org\/10.1016\/j.ecoinf.2020.101197","journal-title":"Ecol Inf"},{"key":"17884_CR62","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J (2015) Deep residual learning for image recognition. https:\/\/doi.org\/10.48550\/ARXIV.1512.03385","DOI":"10.48550\/ARXIV.1512.03385"},{"key":"17884_CR63","doi-asserted-by":"publisher","first-page":"10313","DOI":"10.1007\/s11042-022-12200-y","volume":"81","author":"MG Lanjewar","year":"2022","unstructured":"Lanjewar MG, Gurav OL (2022) Convolutional neural networks based classifications of soil images. Multimed Tools Appl 81:10313\u201310336. https:\/\/doi.org\/10.1007\/s11042-022-12200-y","journal-title":"Multimed Tools Appl"},{"key":"17884_CR64","doi-asserted-by":"publisher","first-page":"16537","DOI":"10.1007\/s11042-022-12392-3","volume":"81","author":"MG Lanjewar","year":"2022","unstructured":"Lanjewar MG, Morajkar PP, Parab J (2022) Detection of tartrazine colored rice flour adulteration in turmeric from multi-spectral images on smartphone using convolutional neural network deployed on PaaS cloud. Multimed Tools Appl 81:16537\u201316562. https:\/\/doi.org\/10.1007\/s11042-022-12392-3","journal-title":"Multimed Tools Appl"},{"key":"17884_CR65","unstructured":"Thakur R (2020) Step by step VGG16 implementation in Keras for beginners. In: Medium. https:\/\/towardsdatascience.com\/step-by-step-vgg16-implementation-in-keras-for-beginners-a833c686ae6c. Accessed 11 Aug 2021"},{"key":"17884_CR66","doi-asserted-by":"publisher","unstructured":"Szegedy C, Liu W, Jia Y et al (2014) Going deeper with convolutions. https:\/\/doi.org\/10.48550\/ARXIV.1409.4842","DOI":"10.48550\/ARXIV.1409.4842"},{"key":"17884_CR67","doi-asserted-by":"publisher","first-page":"1285","DOI":"10.1109\/TMI.2016.2528162","volume":"35","author":"H-C Shin","year":"2016","unstructured":"Shin H-C, Roth HR, Gao M et al (2016) Deep convolutional neural networks for computer-aided detection: CNN Architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 35:1285\u20131298. https:\/\/doi.org\/10.1109\/TMI.2016.2528162","journal-title":"IEEE Trans Med Imaging"},{"key":"17884_CR68","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1109\/JBHI.2016.2635663","volume":"21","author":"A Kumar","year":"2017","unstructured":"Kumar A, Kim J, Lyndon D et al (2017) An ensemble of fine-tuned convolutional neural networks for medical image classification. IEEE J Biomed Health Inform 21:31\u201340. https:\/\/doi.org\/10.1109\/JBHI.2016.2635663","journal-title":"IEEE J Biomed Health Inform"},{"key":"17884_CR69","unstructured":"Advanced Guide to Inception v3 on Cloud TPU. In: Google Cloud. https:\/\/cloud.google.com\/tpu\/docs\/inception-v3-advanced. Accessed 11 Aug 2021"},{"key":"17884_CR70","unstructured":"What is Cloud Computing? Pros and Cons of Different Types of Services. In: Investopedia. https:\/\/www.investopedia.com\/terms\/c\/cloud-computing.asp. Accessed 13 Aug 2023"},{"key":"17884_CR71","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-022-07743-y","author":"MG Lanjewar","year":"2022","unstructured":"Lanjewar MG, Panchbhai KG (2022) Convolutional neural network based tea leaf Disease prediction system on smart phone using paas cloud. Neural Comput & Applic. https:\/\/doi.org\/10.1007\/s00521-022-07743-y","journal-title":"Neural Comput & Applic"},{"key":"17884_CR72","unstructured":"(2020) What is Heroku? Price, features, benefits, and competitors | Low-code backend to build modern apps. In: Back4App Blog. https:\/\/blog.back4app.com\/what-is-heroku\/. Accessed 11 Aug 2021"},{"key":"17884_CR73","doi-asserted-by":"publisher","DOI":"10.3390\/s21124176","volume":"21","author":"D-H Wang","year":"2021","unstructured":"Wang D-H, Zhou W, Li J et al (2021) Exploring misclassification information for fine-grained image classification. Sensors 21:4176. https:\/\/doi.org\/10.3390\/s21124176","journal-title":"Sensors"},{"key":"17884_CR74","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1007\/978-3-030-85529-1_26","volume-title":"Modeling decisions for Artificial Intelligence","author":"R Haffar","year":"2021","unstructured":"Haffar R, Jebreel NM, Domingo-Ferrer J, S\u00e1nchez D (2021) Explaining Image Misclassification in Deep Learning via adversarial examples. In: Torra V, Narukawa Y (eds) Modeling decisions for Artificial Intelligence. Springer International Publishing, Cham, pp 323\u2013334"},{"key":"17884_CR75","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.119961","volume":"224","author":"MG Lanjewar","year":"2023","unstructured":"Lanjewar MG, Panchbhai KG, Charanarur P (2023) Lung cancer detection from CT scans using modified DenseNet with feature selection methods and ML classifiers. Expert Syst Appl 224:119961. https:\/\/doi.org\/10.1016\/j.eswa.2023.119961","journal-title":"Expert Syst Appl"},{"key":"17884_CR76","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-022-03752-7","author":"MG Lanjewar","year":"2022","unstructured":"Lanjewar MG, Parab JS, Shaikh AY, Sequeira M (2022) CNN with machine learning approaches using ExtraTreesClassifier and MRMR feature selection techniques to detect Liver Diseases on cloud. Cluster Comput. https:\/\/doi.org\/10.1007\/s10586-022-03752-7","journal-title":"Cluster Comput"},{"key":"17884_CR77","doi-asserted-by":"publisher","first-page":"12699","DOI":"10.1007\/s11042-022-13935-4","volume":"82","author":"MG Lanjewar","year":"2023","unstructured":"Lanjewar MG, Parab JS, Shaikh AY (2023) Development of framework by combining CNN with KNN to detect Alzheimer\u2019s Disease using MRI images. Multimed Tools Appl 82:12699\u201312717. https:\/\/doi.org\/10.1007\/s11042-022-13935-4","journal-title":"Multimed Tools Appl"},{"key":"17884_CR78","doi-asserted-by":"crossref","unstructured":"Lanjewar MG, Parate RK, Parab JS (2022) Machine learning approach with data normalization technique for early stage detection of hypothyroidism. In: Artificial Intelligence Applications for Health Care, CRC Press","DOI":"10.1201\/9781003241409-5"},{"key":"17884_CR79","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","volume":"27","author":"T Fawcett","year":"2006","unstructured":"Fawcett T (2006) An introduction to ROC analysis. Pattern Recognit Lett 27:861\u2013874. https:\/\/doi.org\/10.1016\/j.patrec.2005.10.010","journal-title":"Pattern Recognit Lett"},{"key":"17884_CR80","unstructured":"Ahuja S (2021) XAI with LIME for CNN Models. In: Medium. https:\/\/medium.datadriveninvestor.com\/xai-with-lime-for-cnn-models-5560a486578. Accessed 12 Aug 2023"},{"key":"17884_CR81","unstructured":"Raschka S (2022) Creating confidence intervals for machine learning classifiers. In: Sebastian Raschka, PhD. https:\/\/sebastianraschka.com\/blog\/2022\/confidence-intervals-for-ml.html. Accessed 12 Aug 2023"},{"key":"17884_CR82","doi-asserted-by":"publisher","unstructured":"Raschka S (2018) Model evaluation, model selection, and algorithm selection in machine learning. https:\/\/doi.org\/10.48550\/ARXIV.1811.12808","DOI":"10.48550\/ARXIV.1811.12808"},{"key":"17884_CR83","unstructured":"Bootstrap Confidence Intervals (n.d.). https:\/\/acclab.github.io\/bootstrap-confidence-intervals.html#:~:text=The%2095%25%20indicates%20that%20any,of%20these%20confidence%20intervals%20would. Accessed 12 Aug 2023"},{"key":"17884_CR84","unstructured":"Gorton I (2020) Scalability and cost analysis for cloud-based software systems (Part 1). In: Medium. https:\/\/blog.devgenius.io\/scalability-and-cost-analysis-for-cloud-based-software-systems-part-1-472012435b26. Accessed 12 Aug 2023"},{"key":"17884_CR85","unstructured":"Andrei A (2022) Scalability analysis for cloud computing | Cloud Computing & SaaS Awards. https:\/\/www.cloud-awards.com\/scalability-analysis-for-cloud-computing\/. Accessed 12 Aug 2023"},{"key":"17884_CR86","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1186\/s13677-019-0134-y","volume":"8","author":"A Al-Said Ahmad","year":"2019","unstructured":"Al-Said Ahmad A, Andras P (2019) Scalability analysis comparisons of cloud-based software services. J Cloud Comp 8:10. https:\/\/doi.org\/10.1186\/s13677-019-0134-y","journal-title":"J Cloud Comp"},{"key":"17884_CR87","unstructured":"How fast is my model? (2021) https:\/\/machinethink.net\/blog\/how-fast-is-my-model\/. Accessed 11 Aug 2021"},{"key":"17884_CR88","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-021-02292-8","author":"S Sen","year":"2021","unstructured":"Sen S, Saha S, Chatterjee S et al (2021) A bi-stage feature selection approach for COVID-19 prediction using chest CT images. Appl Intell. https:\/\/doi.org\/10.1007\/s10489-021-02292-8","journal-title":"Appl Intell"},{"key":"17884_CR89","doi-asserted-by":"publisher","first-page":"29883","DOI":"10.1007\/s11042-022-14232-w","volume":"82","author":"MG Lanjewar","year":"2023","unstructured":"Lanjewar MG, Shaikh AY, Parab J (2023) Cloud-based COVID-19 Disease prediction system from X-Ray images using convolutional neural network on smartphone. Multimed Tools Appl 82:29883\u201329912. https:\/\/doi.org\/10.1007\/s11042-022-14232-w","journal-title":"Multimed Tools Appl"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-17884-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-17884-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-17884-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,4]],"date-time":"2024-06-04T04:11:39Z","timestamp":1717474299000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-17884-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,3]]},"references-count":89,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["17884"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-17884-4","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,3]]},"assertion":[{"value":"26 November 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 October 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 December 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 January 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}