{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T03:41:47Z","timestamp":1791776507189,"version":"4.3.4"},"reference-count":36,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,1,13]],"date-time":"2025-01-13T00:00:00Z","timestamp":1736726400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Pneumonia, a leading cause of mortality in children under five, is usually diagnosed through chest X-ray (CXR) images due to its efficiency and cost-effectiveness. However, the shortage of radiologists in the Least Developed Countries (LDCs) emphasizes the need for automated pneumonia diagnostic systems. This article presents a Deep Learning model, Zero-Order Optimized Convolutional Neural Network (ZooCNN), a Zero-Order Optimization (Zoo)-based CNN model for classifying CXR images into three classes, Normal Lungs (NL), Bacterial Pneumonia (BP), and Viral Pneumonia (VP); this model utilizes the Adaptive Synthetic Sampling (ADASYN) approach to ensure class balance in the Kaggle CXR Images (Pneumonia) dataset. Conventional CNN models, though promising, face challenges such as overfitting and have high computational costs. The use of ZooPlatform (ZooPT), a hyperparameter finetuning strategy, on a baseline CNN model finetunes the hyperparameters and provides a modified architecture, ZooCNN, with a 72% reduction in weights. The model was trained, tested, and validated on the Kaggle CXR Images (Pneumonia) dataset. The ZooCNN achieved an accuracy of 97.27%, a sensitivity of 97.00%, a specificity of 98.60%, and an F1 score of 97.03%. The results were compared with contemporary models to highlight the efficacy of the ZooCNN in pneumonia classification (PC), offering a potential tool to aid physicians in clinical settings.<\/jats:p>","DOI":"10.3390\/jimaging11010022","type":"journal-article","created":{"date-parts":[[2025,1,13]],"date-time":"2025-01-13T06:42:15Z","timestamp":1736750535000},"page":"22","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["ZooCNN: A Zero-Order Optimized Convolutional Neural Network for Pneumonia Classification Using Chest Radiographs"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-5807-2612","authenticated-orcid":false,"given":"Saravana Kumar","family":"Ganesan","sequence":"first","affiliation":[{"name":"Department of Electronics and Communication Engineering, Karpagam College of Engineering, Coimbatore 641032, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6514-3959","authenticated-orcid":false,"given":"Parthasarathy","family":"Velusamy","sequence":"additional","affiliation":[{"name":"Department of Computer Science Engineering, Karpagam Academy of Higher Education (Deemed to Be University), Coimbatore 641021, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9287-5829","authenticated-orcid":false,"given":"Santhosh","family":"Rajendran","sequence":"additional","affiliation":[{"name":"Department of Computer Science Engineering, Karpagam Academy of Higher Education (Deemed to Be University), Coimbatore 641021, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-5252-2306","authenticated-orcid":false,"given":"Ranjithkumar","family":"Sakthivel","sequence":"additional","affiliation":[{"name":"Department of Computer Science Engineering, Karpagam Academy of Higher Education (Deemed to Be University), Coimbatore 641021, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2996-0729","authenticated-orcid":false,"given":"Manikandan","family":"Bose","sequence":"additional","affiliation":[{"name":"Department of Computer Science Engineering, Karpagam Academy of Higher Education (Deemed to Be University), Coimbatore 641021, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4301-7614","authenticated-orcid":false,"given":"Baskaran Stephen","family":"Inbaraj","sequence":"additional","affiliation":[{"name":"Department of Food Science, Fu Jen Catholic University, New Taipei City 242062, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,13]]},"reference":[{"key":"ref_1","unstructured":"(2024, August 18). World Health Organization. Available online: https:\/\/www.who.int\/health-topics\/pneumonia#tab=tab_1."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Giovanni, I., Maurizio, C., and Elena, C. (2023). Chest X-ray in Emergency Radiology: What Artificial Intelligence Applications Are Available?. Diagnostics, 13.","DOI":"10.3390\/diagnostics13020216"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Nithya, T.M., Kanna, P.R., Vanithamani, S., and Santhi, P.I. (2023). An Efficient PM\u2014Multisampling Image Filtering with Enhanced CNN Architecture for Pneumonia Classification. Biomed. Signal Process. Control, 86.","DOI":"10.1016\/j.bspc.2023.105296"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"349","DOI":"10.7861\/clinmedicine.13-4-349","article-title":"Assessing the accuracy and certainty in interpreting chest X-rays in the medical division","volume":"13","author":"Satia","year":"2013","journal-title":"Clin. Med."},{"key":"ref_5","first-page":"46","article-title":"ODGNet: Robotic Grasp Detection Network Based on Omni-Dimensional Dynamic Convolution","volume":"14","author":"Xinghong","year":"2024","journal-title":"Appl. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1109\/JBHI.2023.3247949","article-title":"Interpretable CNN-Multilevel Attention Transformer for Rapid Recognition of Pneumonia from Chest X-Ray Images","volume":"28","author":"Chen","year":"2023","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_7","first-page":"8259","article-title":"Stacked ensemble learning based on deep convolutional neural networks for pediatric pneumonia diagnosis using chest x-ray images","volume":"35","author":"Ravi","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Khan, A.M., Akram, M., and Nazir, S.S. (2023). Automated Grading of Chest X-Ray Images for Viral Pneumonia with Convolutional Neural Networks Ensemble and Region of Interest Localization. PLoS ONE, 18.","DOI":"10.1371\/journal.pone.0280352"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"154794","DOI":"10.1016\/j.jcrc.2024.154794","article-title":"Automatic ARDS surveillance with chest X-ray recognition using convolutional neural networks","volume":"82","author":"Ye","year":"2024","journal-title":"J. Crit. Care"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ascencio-Cabral, A., and Reyes-Aldasoro, C.C. (2022). Comparison of Convolutional Neural Networks and Transformers for the Classification of Images of COVID-19, Pneumonia and Healthy Individuals as Observed with Computed Tomography. J. Imaging, 8.","DOI":"10.3390\/jimaging8090237"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Loddo, A., Fadda, C., and Di Ruberto, C. (2022). An Empirical Evaluation of Convolutional Networks for Malaria Diagnosis. J. Imaging, 8.","DOI":"10.3390\/jimaging8030066"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Avianto, D., Harjoko, A. (2022). CNN-Based Classification for Highly Similar Vehicle Model Using Multi-Task Learning. J. Imaging, 8.","DOI":"10.3390\/jimaging8110293"},{"key":"ref_13","first-page":"11167","article-title":"A novel method for ventricular fibrillation detection using convolutional neural networks","volume":"14","author":"Mjahad","year":"2020","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Garga, B., Abboubakar, H., Sourpele, R.S., Li Gwet, D.L., and Bitjoka, L. (2024). Pollen Grain Classification Using Some Convolutional Neural Network Architectures. J. Imaging, 10.","DOI":"10.3390\/jimaging10070158"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Lacerda, P., Barros, B., Albuquerque, C., and Conci, A. (2021). Hyperparameter Optimization for COVID-19 Pneumonia Diagnosis Based on Chest CT. Sensors, 21.","DOI":"10.3390\/s21062174"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"111328","DOI":"10.1016\/j.automatica.2023.111328","article-title":"Distributed Zeroth-Order Optimization: Convergence Rates That Match Centralized Counterpart","volume":"159","author":"Yuan","year":"2024","journal-title":"Automatica"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1016\/j.neunet.2022.03.010","article-title":"A Zeroing Neural Dynamics Based Acceleration Optimization Approach for Optimizers in Deep Neural Networks","volume":"150","author":"Liao","year":"2022","journal-title":"Neural Netw."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Huang, F., Gao, S., Chen, S., and Huang, H. (2019). Zeroth-Order Stochastic Alternating Direction Method of Multipliers for Nonconvex Nonsmooth Optimization. Int. Jt. Conf. Artif. Intell., 2549\u20132555.","DOI":"10.24963\/ijcai.2019\/354"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"110491","DOI":"10.1016\/j.measurement.2021.110491","article-title":"ChxCapsNet: Deep Capsule Network with Transfer Learning for Evaluating Pneumonia in Paedi-atric Chest Radiographs","volume":"188","author":"Bodapati","year":"2021","journal-title":"Measurement"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3278","DOI":"10.1109\/TMI.2022.3182168","article-title":"CX-DaGAN: Domain Adaptation for Pneumonia Diagnosis on a Small Chest X-Ray Dataset","volume":"41","author":"Sanchez","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"118650","DOI":"10.1016\/j.eswa.2022.118650","article-title":"Automated Multi-Class Classification of Lung Diseases from CXR-Images Using Pre-Trained Convolutional Neural Networks","volume":"211","author":"Karaddi","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Shukla, S.K., Hussain, C.M., Mangla, B., Choudhary, M., and Patra, S. (2024). Convolutional Neural Network Implementation Based on SMOTE and Data Augmentation for Lung Cancer Diagnosis. Nanotechnology in Miniaturization. Nanotechnology in the Life Sciences, Springer.","DOI":"10.1007\/978-3-031-72004-8"},{"key":"ref_23","first-page":"11","article-title":"ADASYN: Adaptive Synthetic Sampling Approach for Imbalanced Learning","volume":"4","author":"He","year":"2009","journal-title":"IEEE Comput. Intell. Mag."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic Minority over-Sampling Technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, D. (2024). Attention-enhanced architecture for improved pneumonia detection in chest X-ray images. BMC Med. Imaging, 24.","DOI":"10.1186\/s12880-023-01177-1"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Nahid, A.-A., Sikder, N., Bairagi, A.K., Razzaque, M.A., Masud, M., Kouzani, A.Z., and Mahmud, M.A.P. (2020). A Novel Method to Identify Pneumonia Through Analyzing Chest Radiographs Employing a Multichannel Convolutional Neural Network. Sensors, 20.","DOI":"10.3390\/s20123482"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"119503","DOI":"10.1016\/j.eswa.2023.119503","article-title":"Detection and Classification of Pneumonia Using Novel Superior Exponential (SupEx) Activation Function in Convolutional Neural Networks","volume":"217","author":"Bas","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Guail, A.A.A., Gui, J.S., Moctard, O.B., and Al-Sabri, R. (2022). A Principal Neighborhood Aggregation-Based Graph Convolutional Network for Pneumonia Detection. Sensors, 22.","DOI":"10.3390\/s22083049"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.jare.2022.08.021","article-title":"A Hybrid Explainable Ensemble Transformer Encoder for Pneumonia Identification from Chest X-Ray Images","volume":"48","author":"Ukwuoma","year":"2022","journal-title":"J. Adv. Res."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Shi, Z., and Eryilmaz, A. (2020, January 6\u20139). A Zeroth-Order ADMM Algorithm for Stochastic Optimization over Distributed Processing Networks. Proceedings of the IEEE INFOCOM 2020 IEEE Conference on Computer Communications, Tornonto, OT, Canada.","DOI":"10.1109\/INFOCOM41043.2020.9155520"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1486","DOI":"10.1016\/j.acra.2021.12.025","article-title":"Diagnosis of Active Pulmonary Tuberculosis and Community-Acquired Pneumonia Using Convolution Neural Network Based on Transfer Learning","volume":"29","author":"Han","year":"2022","journal-title":"Acad. Radiol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"109953","DOI":"10.1016\/j.measurement.2021.109953","article-title":"Automated Pneumonia Detection on Chest X-Ray Images: A Deep Learning Approach with Different Optimizers and Transfer Learning Architectures","volume":"184","author":"Manickam","year":"2021","journal-title":"Measurement"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"115834","DOI":"10.1016\/j.eswa.2021.115834","article-title":"Efficient Ensemble for Image-Based Identification of Pneumonia Utilizing Deep CNN and SGD with Warm Restarts","volume":"187","author":"Podgorelec","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"106416","DOI":"10.1016\/j.engappai.2023.106416","article-title":"Transfer Learning Approach for Pediatric Pneumonia Diagnosis Using Channel Attention Deep CNN Architectures","volume":"123","author":"Asswin","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"923","DOI":"10.1016\/j.aej.2022.10.053","article-title":"A Deep Learning Architecture for Multi-Class Lung Diseases Classification Using Chest X-Ray (CXR) Images","volume":"64","author":"Alshmrani","year":"2022","journal-title":"Alex. Eng. J."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Li, Y., Xin, Y., Li, X., Zhang, Y., Liu, C., Cao, Z., Du, S., and Wang, L. (2024). Omni-Dimensional Dynamic Convolution Feature Coordinate Attention Network for Pneumonia Classification. Vis. Comput. Ind. Biomed. 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