Pneumonia Net: Pneumonia Detection and Categorization in Chest X-ray Images

Author:

Srivastava Somya1ORCID,Verma Seema2,Das Nripendra Narayan3ORCID,Sharma Shraddha4ORCID,Dubey Gaurav5ORCID

Affiliation:

1. ABES Engineering College, Computer Science, Ghaziabad, India

2. Guru Gobind Singh Indraprastha University, Computer Science and Engineering, Delhi, India

3. Manipal University Jaipur, Information Technology, Pandit Deendayal Energy University, Computer Science

4. KIET Group of Institutions, Computer Science , Gandhinagar, India

5. KIET Group of Institutions, Computer Science

Abstract

Background: Pneumonia is one of the leading causes of death and disability due to respiratory infections. The key to successful treatment of pneumonia is in its early diagnosis and correct classification. PneumoniaNet is a unique deep-learning model based on CNN for identifying pneumonia on chest X-rays. Objective: A deep learning model that combines convolutional, pooling, and fully connected layers is presented in this study. Methods: In order to learn how to identify cases of pneumonia and healthy controls on chest X-ray pictures, PneumoniaNet was trained on a large labeled library of such images. A robust data augmentation technique was adopted to enhance the model generalization and training set diversity. Standard measures like as accuracy, precision, recall, and F1-score were applied to PneumoniaNet's performance evaluation. Results: The suggested model performed effectively in detecting pneumonia cases with an accuracy of 93.88%. Conclusion: The model was evaluated against the current state-of-art methods and showed that PneumoniaNet outperformed the other models.

Publisher

Bentham Science Publishers Ltd.

Subject

General Computer Science

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Recent Advances in Artificial Intelligence & Machine Learning: A Practical Approach;Recent Advances in Computer Science and Communications;2024-05

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