Pulmo Scan: A Deep Learning Framework for Pneumonia Detection using X-Ray Images
Dharmaiah Devarapalli1, Mekathoti Vamsi Kiran2, Topella Venkata Mahipathi Rao3, Adabala Murali Veera Sri Sai4, Bezawada Dhanush5
1Dr Dharmaiah Devarapalli, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, (A.P.), India.
2Dr Mekathoti Vamsi Kiran, Department of Computer Science and Engineering, K L University, Vijayawada (A.P.), India.
3Topella Venkata Mahipathi Rao, Department of Computer Science and Engineering, K L University, Vijayawada (A.P.), India.
4Adabala Murali Veera Sri Sai, Department of Computer Science and Engineering, K L University, Vijayawada (A.P.), India.
5Bezawada Dhanush, Department of Computer Science and Engineering, K L University, Vijayawada (A.P.), India.
Manuscript received on 30 September 2025 | Revised Manuscript received on 07 October 2025 | Manuscript Accepted on 15 October 2025 | Manuscript published on 30 October 2025 | PP: 15-24 | Volume-13 Issue-11, October 2025 | Retrieval Number: 100.1/ijese.L262213121125 | DOI: 10.35940/ijese.L2622.13111025
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Pneumonia is an acute respiratory infection of the lung that must be identified at its early stages to keep mortality rates to a minimum, especially in Wireless Body Area Networks (WBAN). Traditional diagnosing methods, i.e., manual interpretation of X-rays, are time-consuming and prone to human errors. The existing models are plagued by generalizability issues, dataset imbalance, and a high false-detection rate, which complicate pneumonia classification. To address these challenges, we propose a CNN-based model that leverages transfer learning to improve detection accuracy. The model consists of three convolutional layers, dropout regularisation, the Adam optimiser, and robust data augmentation methods to learn improved features and prevent overfitting. We trained the model on the Chest X-ray dataset (NORMAL vs. PNEUMONIA) containing 5,863 images. We achieved enhanced accuracy across five state-of-the-art models in our experiments, with higher precision, recall, and F1 Scores. Additionally, the model generalises well by leveraging diverse preprocessing techniques, including image resizing, normalisation, and various forms of augmentation. Compared with existing architectures such as VGG-16, ResNet50, and InceptionV3, the model demonstrated improved robustness and classification accuracy. This research facilitates the development of a solid deep learning framework for detecting pneumonia to be incorporated into real-time medical software.
Keywords: Pneumonia Detection, CNN, Transfer Learning, Chest X-ray, Data Augmentation, Wireless Body Area Networks.
Scope of the Article: Artificial Intelligence and Methods