The Classification and Segmentation of Pneumonia using Deep Learning Algorithms:  A Comparative Study

Vishnu Kumar Mishra; Megha Mishra; Jitendra Sheetlani; Anoop Kumar; Priya Pachouri; Thirunagari Nagapraveena; Amulya Puttamallaiah; Mandala Naga Sravya; Kimidi Parijatha

doi:10.52756/ijerr.2023.v36.007

Vishnu Kumar Mishra Department of Computer Science and Engineering, Shri Shankaracharya Technical Campus, CSVTU Bhilai, Chhattisgarh, India https://orcid.org/0000-0001-8652-9076
Megha Mishra Department of Computer Science and Engineering, Shri Shankaracharya Technical Campus, CSVTU Bhilai, Chhattisgarh, India https://orcid.org/0000-0001-7948-4586
Jitendra Sheetlani Department of Computer Science and Engineering, Chameli Devi Group of Institutions, Indore, M.P., India https://orcid.org/0000-0002-5666-512X
Anoop Kumar Department of Computer Science and Engineering, Malla Reddy College of Engineering, JNTUH University, Secunderabad, Telangana, India https://orcid.org/0000-0002-3306-8808
Priya Pachouri Department of Computer Science and Engineering, Malla Reddy College of Engineering, JNTUH University, Secunderabad, Telangana, India https://orcid.org/0009-0006-0043-5110
Thirunagari Nagapraveena Department of Computer Science and Engineering, Malla Reddy College of Engineering, JNTUH University, Secunderabad, Telangana, India https://orcid.org/0009-0003-6215-5630
Amulya Puttamallaiah Department of Computer Science and Engineering, Malla Reddy College of Engineering, JNTUH University, Secunderabad, Telangana, India https://orcid.org/0009-0001-4024-5104
Mandala Naga Sravya Department of Computer Science and Engineering, Malla Reddy College of Engineering, JNTUH University, Secunderabad, Telangana, India https://orcid.org/0000-0002-5210-6064
Kimidi Parijatha Department of Computer Science and Engineering, Malla Reddy College of Engineering, JNTUH University, Secunderabad, Telangana, India https://orcid.org/0009-0000-2139-9661

DOI: https://doi.org/10.52756/ijerr.2023.v36.007

Keywords: Computational, lung cancer, PGRMC1, Progesterone receptor membrane component 1, mutation, single nucleotide polymorphisms

Abstract

The paper uses convolutional neural networks (CNNs) to analyze radiography pictures and discriminate between areas afflicted by pneumonia and normal lung tissue. A sizable dataset of annotated chest X-rays is used to train the deep learning model, which enables it to pick up on complex patterns and characteristics linked to pneumonia. Pneumonia is a significant respiratory disease that affects a large number of individuals worldwide. Timely and accurate diagnosis of pneumonia plays a crucial role in effective treatment and management of the disease. We evaluate the performance of several up-to-date convolution neural network (CNN) architectures, namely ResNet-50, VGG-16, and DenseNet-121, then compare their results with traditional machine learning classifiers. Recent advancements in deep learning methods have shown accurate results in the investigation and diagnosis of medical image data, including the detection of pneumonia. This paper examines different deep-learning methods for categorizing pneumonia from lung X-ray imagery. Our results show that deep learning techniques performed better than conventional machine learning techniques in classifying pneumonia, with an estimated accuracy of 95% across all of the examined CNN models. These results demonstrate the potential of deep learning algorithms to significantly improve the accuracy and effectiveness of pneumonia diagnosis, supporting physicians in making knowledgeable decisions about patient care.

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