Saniya P.M,
Mohammad Shabeer,
Muhammed Sinan M.S,
Muzammil Rahman K.M,
Sanad Fazal Kota,
- Assistant Professor, P A College of Engineering, Mangalore, Karnataka, India
- Student, P A College of Engineering, Mangalore, Karnataka, India
- Student, P A College of Engineering, Mangalore, Karnataka, India
- Student, P A College of Engineering, Mangalore, Karnataka, India
- Student, P A College of Engineering, Mangalore, Karnataka, India
Abstract document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_abs_106761’);});Edit Abstract & Keyword
Pneumonia, an infectious lung disease primarily caused by bacteria, often exacerbated by environmental factors, leads to the accumulation of pus in the lung’s alveoli. Accurate diagnosis through chest X-rays, ultrasounds, or lung biopsies is crucial to avoid misdiagnosis and ensure proper treatment, crucial for patients’ quality of life. Diagnostic capacities have been greatly improved by deep learning advances, especially with convolutional neural networks (CNNs). This research presents a robust CNN-based approach for predicting and detecting pneumonia from chest X-ray images. Using a dataset comprising 20,000 images at a resolution of 224×224 and trained with a batch size of 32, the CNN model achieved an impressive 95% accuracy during training. The study demonstrates the CNN model’s effectiveness in identifying COVID-19, bacterial, and viral pneumonia solely from chest X-ray images, highlighting its potential for accurate diagnosis in clinical settings. It was widely believed at the time that computers would eventually become as adaptive as humans. The fundamental idea behind adaptive learning is that the system or tool may adapt to the user’s or student’s preferred learning style, giving them a better and more efficient learning experience.
Keywords: Pneumonia Detection, Adaptive Deep Learning, Deep Convolutional Neural Network Architecture
[This article belongs to Journal of Microwave Engineering and Technologies (jomet)]
Saniya P.M, Mohammad Shabeer, Muhammed Sinan M.S, Muzammil Rahman K.M, Sanad Fazal Kota. Pneumonia Detection and Classification using Deep Learning. Journal of Microwave Engineering and Technologies. 2024; 11(03):-.
Saniya P.M, Mohammad Shabeer, Muhammed Sinan M.S, Muzammil Rahman K.M, Sanad Fazal Kota. Pneumonia Detection and Classification using Deep Learning. Journal of Microwave Engineering and Technologies. 2024; 11(03):-. Available from: https://journals.stmjournals.com/jomet/article=2024/view=0
References
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[1] Vandecia Fernandes et al., “Bayesian convolutional neural network estimation for pediatric pneumonia detection and diagnosis”, Computer Methods and Programs in Biomedicine, Elsevier, 2021 [2] Hongen Lu et al., “Transfer Learning from Pneumonia to COVID-19”, Asia-Pacific on Computer Science and Data Engineering (CSDE), 2020 IEEE [3] Sammy V. Militante et al., “Pneumonia and COVID-19 Detection using Convolutional Neural Networks”, 2020 the third International on Vocational Education and Electrical Engineering (ICVEE), IEEE, 2021 [4] Nanette V. Dionisio et al., “Pneumonia Detection through Adaptive Deep Learning Models of Convolutional Neural Networks”, 2020 11th IEEE Control and System Graduate Research Colloquium (ICSGRC 2020), 8 August 2020 [5] Md. Jahid Hasan et al., “Deep Learning-based Detection and Segmentation of COVID-19 & Pneumonia on Chest X-ray Image”, 2021 International Information and Communication Technology for Sustainable Development (ICICT4SD), 27-28 February 2021 [6]https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia [7] LeCun Y, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, Jackel LD. Backpropagation applied to handwritten zip code recognition. Neural computation. 1989 Dec;1(4):541-51. [8] Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Communications of the ACM. 2017 May 24;60(6):84-90. [9] Simonyan, K.; Zisserman, A. Very deep convolutional networks for large-scale image recognition. arXiv 2014, arXiv:1409.1556 [10] R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh and D. Batra, “Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization,” 2017 IEEE International Conference on Computer Vision (ICCV), Venice, 2017, pp. 618-626. [11] L. Wang and A. Wong, “COVID-Net: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest radiography images,” arXiv:2003.09871, 2020. [12] https://towardsdatascience.com/medical-x-ray-%EF%B8%8F-image-classification-using-convolutional-neural-network-9a6d33b1c2a
Journal of Microwave Engineering and Technologies
| Volume | 11 |
| Issue | 03 |
| Received | 10/08/2024 |
| Accepted | 26/08/2024 |
| Published | 09/10/2024 |
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