A Comparison Study of Deep CNN Architecture in Detecting of Pneumonia

• URL: http://arxiv.org/abs/2212.14744v1
• Date: Fri, 30 Dec 2022 14:37:32 GMT
• Title: A Comparison Study of Deep CNN Architecture in Detecting of Pneumonia
• Authors: Al Mohidur Rahman Porag, Md. Mahedi Hasan, Dr. Md Taimur Ahad
• Abstract summary: Pneumonia, a respiratory infection brought on by bacteria or viruses, affects a large number of people. Deep convolutional neural network to classify plant diseases based on images and tested its performance. DenseNet201 achieves state-of-the-art performance with a significantly a smaller number of parameters and within a reasonable computing time.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pneumonia, a respiratory infection brought on by bacteria or viruses, affects a large number of people, especially in developing and impoverished countries where high levels of pollution, unclean living conditions, and overcrowding are frequently observed, along with insufficient medical infrastructure. Pleural effusion, a condition in which fluids fill the lung and complicate breathing, is brought on by pneumonia. Early detection of pneumonia is essential for ensuring curative care and boosting survival rates. The approach most usually used to diagnose pneumonia is chest X-ray imaging. The purpose of this work is to develop a method for the automatic diagnosis of bacterial and viral pneumonia in digital x-ray pictures. This article first presents the authors' technique, and then gives a comprehensive report on recent developments in the field of reliable diagnosis of pneumonia. In this study, here tuned a state-of-the-art deep convolutional neural network to classify plant diseases based on images and tested its performance. Deep learning architecture is compared empirically. VGG19, ResNet with 152v2, Resnext101, Seresnet152, Mobilenettv2, and DenseNet with 201 layers are among the architectures tested. Experiment data consists of two groups, sick and healthy X-ray pictures. To take appropriate action against plant diseases as soon as possible, rapid disease identification models are preferred. DenseNet201 has shown no overfitting or performance degradation in our experiments, and its accuracy tends to increase as the number of epochs increases. Further, DenseNet201 achieves state-of-the-art performance with a significantly a smaller number of parameters and within a reasonable computing time. This architecture outperforms the competition in terms of testing accuracy, scoring 95%. Each architecture was trained using Keras, using Theano as the backend.

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