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

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

Volume: 17 Issue: 3

Author(s): Somya Srivastava, Seema Verma, Nripendra Narayan Das*, Shraddha Sharma and Gaurav Dubey

Affiliation:

• Department of Information Technology, Manipal University Jaipur, Jaipur, Rajasthan, India

Keywords: Machine learning, CNNs, prediction model, healthcare, pneumonia, X-ray.

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.

Cite this article as:

Srivastava Somya, Verma Seema, Das Narayan Nripendra*, Sharma Shraddha and Dubey Gaurav, Pneumonia Net: Pneumonia Detection and Categorization in Chest X-ray Images, Recent Advances in Computer Science and Communications 2024; 17 (3) : e111223224354 . https://dx.doi.org/10.2174/0126662558269484231121112300