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Issue: May 2023

Volume 8 | Issue 5

Impact factor: 8.15

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Published Paper Details
Paper Title: Pneumonia Detection Using X-ray Images by Shallow CNNs
Authors Name: Khushboo Lalwani , Rashmi Upasani
Unique Id: IJSDR2305186
Published In: Volume 8 Issue 5, May-2023
Abstract: Pneumonia is a hazardous interstitial lung disease that is considered life-threatening and is responsible for taking away the lives of infants, children under the age of five and adults over the age of 65. Pneumonia is a respiratory infection that causes inflammation in the air sacs of one or both lungs and is mainly caused by viruses, fungi and bacteria. According to google, over 10 million people in India are diagnosed and affected by CAP every year. WHO (World Health Organization) reported that every one in three deaths is caused by pneumonia in India, which is extremely alarming. Detection of pneumonia on early stages can be crucial as it helps in speeding-up the recovery rate in children as well as adults. In order to increase and improve the efficiency of the predicted results, chest x-ray images are proven to be helpful as it helps the doctors to examine your lungs and blood vessels. These images help radiologists to look for white spots in your lungs which indicates infection but to obtain the results that are that are as true as he predicted results, deep learning has changed the game in the field of medical image analysis as neural networks are impacting the accuracy of the results drastically since the last few years. A Convolutional Neural Network (CNN or ConvNet) is a type of deep learning network that can learn patterns directly from data. CNNs were created expressly to recognise patterns in images, allowing them to distinguish between objects, classes, and categories. In addition to categorising image data, they can also be used successfully to categorise audio, time-series, and signal data. To make things less complex and more efficient, we trained the model with less parameters so that it is shallower in depth and easy to train with more accurate results. We used a dataset [5] of 5125 x-ray images where 4172 images belong to the training set, 1044 images belong to the validation set and 624 images belong to the test set. This chest x-ray dataset [5] was distributed and labelled between 2 classes, 0 and 1 respectively indicating normal (no pneumonia) and pneumonia. On training this dataset [5], the results received for validation set was that validation loss was brought down to 0.1560 and the validation accuracy received was 0.94 which is equivalent to 94%. In the testing set, the test loss was brought down to 0.4381 and the test accuracy obtained was 0.8324 which is equivalent to 83.24%.
Keywords: CNN, deep learning, image classification, medical image analysis, Pneumonia, x-ray images
Cite Article: "Pneumonia Detection Using X-ray Images by Shallow CNNs", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.8, Issue 5, page no.1170 - 1177, May-2023, Available :http://www.ijsdr.org/papers/IJSDR2305186.pdf
Downloads: 000222061
Publication Details: Published Paper ID: IJSDR2305186
Registration ID:206387
Published In: Volume 8 Issue 5, May-2023
DOI (Digital Object Identifier):
Page No: 1170 - 1177
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631

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