Optimization of deep neural network for multiclassification of Pneumonia
It is imperative to understand the significance of the early diagnosis of pneumonia using a convolutional neural network (CNN) to reduce the processing time and increase the quality of treatment that is delivered to the patient. We have implemented transfer learning for processing of available datas...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-12-01
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| Series: | Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/21681163.2023.2292072 |
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| Summary: | It is imperative to understand the significance of the early diagnosis of pneumonia using a convolutional neural network (CNN) to reduce the processing time and increase the quality of treatment that is delivered to the patient. We have implemented transfer learning for processing of available datasets and constructed an ensemble of 3-CNNs: SqueezeNet, ResNet-50 and EfficientNet-b0. In this work, a multiclassification model has been built that can help in early pneumonia diagnosis. The chest X-rays of patients have been classified in 2-stages. In the first stage, the EfficientNet-b0 convolutional neural network with 99% accuracy is employed to diagnose whether the patient’s chest X-ray is Normal/Abnormal. If the output of the first stage is found abnormal then the chest X-ray is processed to the second stage wherein ResNet-50 with 97% accuracy is employed to diagnose the pneumonia type, pneumonia bacteria/pneumonia virus. Further, performance metrics have been computed from the confusion matrix for both stages of X-ray. Also, it is imperative to mention that a pneumonia diagnosis app has been developed in Matlab-2023 for the ease of patients who can self-evaluate the scan report and understand the course of clinical treatment. |
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| ISSN: | 2168-1163 2168-1171 |