A hybrid deep convolutional neural network model for improved diagnosis of pneumonia
Authors: 
Palvinder Singh Mann, Shailesh D. Panchal, Satvir Singh, Guramritpal Singh Saggu, Keshav GuptaAuthors Info & Claims
Palvinder Singh Mann, Shailesh D. Panchal, Satvir Singh, Guramritpal Singh Saggu, Keshav GuptaAuthors Info & ClaimsPages 1791 - 1804
Abstract
Pneumonia is an infection that inflames the air sacs in lungs and is one of the prime causes of deaths under the age of five, all over the world. Moreover, sometimes it became quite difficult to detect and diagnose pneumonia by just looking at the chest plain X-ray images. Therefore, we propose a hybrid deep convolutional neural network model (HDCNN) for improved diagnosis of pneumonia, to simplify the detection for medical practitioners and specialists. In this proposed model, image preprocessing is performed using Student's t distribution, a compact probability density function (cPDF), for better sampling and segregation between the healthy and infected part of lungs, to improve the predictions. Further, a hybrid deep convolutional neural network model is built to extract image features by fine-tuning the pretrained models, viz. Resnet-50, EfficientNet, VGG-16, MobileNetV2 and DenseNet to achieve better results of diagnosis. The proposed hybrid model is analyzed using Grad-CAM visualization, which produces a course localization map, highlighting the infected region in the image used for prediction. The proposed hybrid model is evaluated based on governing parameters, viz. precision, recall, F1-score and accuracy. The results show our proposed model achieves precision of 97.47%, recall 98.09%, F1-score of 97.77% and overall accuracy of 97.69% as compared to other existing models.
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© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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Publication History
Published: 16 November 2023
Accepted: 17 October 2023
Received: 02 March 2023
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