ZooCNN: A Zero-Order Optimized Convolutional Neural Network for Pneumonia Classification Using Chest Radiographs
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 sys...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Journal of Imaging |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-433X/11/1/22 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832588268646957056 |
|---|---|
| author | Saravana Kumar Ganesan Parthasarathy Velusamy Santhosh Rajendran Ranjithkumar Sakthivel Manikandan Bose Baskaran Stephen Inbaraj |
| author_facet | Saravana Kumar Ganesan Parthasarathy Velusamy Santhosh Rajendran Ranjithkumar Sakthivel Manikandan Bose Baskaran Stephen Inbaraj |
| author_sort | Saravana Kumar Ganesan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-453515b035934bcfbf706d7e4b246be6 |
| institution | Kabale University |
| issn | 2313-433X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj-art-453515b035934bcfbf706d7e4b246be62025-01-24T13:36:18ZengMDPI AGJournal of Imaging2313-433X2025-01-011112210.3390/jimaging11010022ZooCNN: A Zero-Order Optimized Convolutional Neural Network for Pneumonia Classification Using Chest RadiographsSaravana Kumar Ganesan0Parthasarathy Velusamy1Santhosh Rajendran2Ranjithkumar Sakthivel3Manikandan Bose4Baskaran Stephen Inbaraj5Department of Electronics and Communication Engineering, Karpagam College of Engineering, Coimbatore 641032, IndiaDepartment of Computer Science Engineering, Karpagam Academy of Higher Education (Deemed to Be University), Coimbatore 641021, IndiaDepartment of Computer Science Engineering, Karpagam Academy of Higher Education (Deemed to Be University), Coimbatore 641021, IndiaDepartment of Computer Science Engineering, Karpagam Academy of Higher Education (Deemed to Be University), Coimbatore 641021, IndiaDepartment of Computer Science Engineering, Karpagam Academy of Higher Education (Deemed to Be University), Coimbatore 641021, IndiaDepartment of Food Science, Fu Jen Catholic University, New Taipei City 242062, TaiwanPneumonia, 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.https://www.mdpi.com/2313-433X/11/1/22convolutional neural networkzero-order optimizationhyperparameter optimizationpneumonia classificationchest X-ray images |
| spellingShingle | Saravana Kumar Ganesan Parthasarathy Velusamy Santhosh Rajendran Ranjithkumar Sakthivel Manikandan Bose Baskaran Stephen Inbaraj ZooCNN: A Zero-Order Optimized Convolutional Neural Network for Pneumonia Classification Using Chest Radiographs Journal of Imaging convolutional neural network zero-order optimization hyperparameter optimization pneumonia classification chest X-ray images |
| title | ZooCNN: A Zero-Order Optimized Convolutional Neural Network for Pneumonia Classification Using Chest Radiographs |
| title_full | ZooCNN: A Zero-Order Optimized Convolutional Neural Network for Pneumonia Classification Using Chest Radiographs |
| title_fullStr | ZooCNN: A Zero-Order Optimized Convolutional Neural Network for Pneumonia Classification Using Chest Radiographs |
| title_full_unstemmed | ZooCNN: A Zero-Order Optimized Convolutional Neural Network for Pneumonia Classification Using Chest Radiographs |
| title_short | ZooCNN: A Zero-Order Optimized Convolutional Neural Network for Pneumonia Classification Using Chest Radiographs |
| title_sort | zoocnn a zero order optimized convolutional neural network for pneumonia classification using chest radiographs |
| topic | convolutional neural network zero-order optimization hyperparameter optimization pneumonia classification chest X-ray images |
| url | https://www.mdpi.com/2313-433X/11/1/22 |
| work_keys_str_mv | AT saravanakumarganesan zoocnnazeroorderoptimizedconvolutionalneuralnetworkforpneumoniaclassificationusingchestradiographs AT parthasarathyvelusamy zoocnnazeroorderoptimizedconvolutionalneuralnetworkforpneumoniaclassificationusingchestradiographs AT santhoshrajendran zoocnnazeroorderoptimizedconvolutionalneuralnetworkforpneumoniaclassificationusingchestradiographs AT ranjithkumarsakthivel zoocnnazeroorderoptimizedconvolutionalneuralnetworkforpneumoniaclassificationusingchestradiographs AT manikandanbose zoocnnazeroorderoptimizedconvolutionalneuralnetworkforpneumoniaclassificationusingchestradiographs AT baskaranstepheninbaraj zoocnnazeroorderoptimizedconvolutionalneuralnetworkforpneumoniaclassificationusingchestradiographs |