A hybrid convolutional neural network model to detect COVID‐19 and pneumonia using chest X‐ray images. Issue 1 (17th November 2022)

• Record Type: Journal Article
• Authors: Gupta, Harsh; Bansal, Naman; Garg, Swati; Mallik, Hritesh; Prabha, Anju; Yadav, Jyoti
• Is Part Of: International journal of imaging systems and technology. Volume 33:Issue 1(2023)
• Page Start: 39
• Page End: 52
• Publication Date: 2022-11-17
• Subjects: chest X‐rays -- CNN -- COVID‐19 -- hybrid model -- pneumonia -- transfer learning techniques; Imaging systems -- Periodicals; Image processing -- Periodicals; 621.367
• DOI: 10.1002/ima.22829
• Languages: English
• ISSNs: 0899-9457

Abstract

Abstract: A hybrid convolutional neural network (CNN)‐based model is proposed in the article for accurate detection of COVID‐19, pneumonia, and normal patients using chest X‐ray images. The input images are first pre‐processed to tackle problems associated with the formation of the dataset from different sources, image quality issues, and imbalances in the dataset. The literature suggests that several abnormalities can be found with limited medical image datasets by using transfer learning. Hence, various pre‐trained CNN models: VGG‐19, InceptionV3, MobileNetV2, and DenseNet are adopted in the present work. Finally, with the help of these models, four hybrid models: VID (VGG‐19, Inception, and DenseNet), VMI(VGG‐19, MobileNet, and Inception), VMD (VGG‐19, MobileNet, and DenseNet), and IMD(Inception, MobileNet, and DenseNet) are proposed. The model outcome is also tested using five‐fold cross‐validation. The best‐performing hybrid model is the VMD model with an overall testing accuracy of 97.3%. Thus, a new hybrid model architecture is presented in the work that combines three individual base CNN models in a parallel configuration to counterbalance the shortcomings of individual models. The experimentation result reveals that the proposed hybrid model outperforms most of the previously suggested models. This model can also be used in the identification of diseases, especially in rural areas where limited laboratory facilities are available.