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Classification of Pnuemonia disease from Chest X-rays using Deep Learning

Khanna, Aman (2021) Classification of Pnuemonia disease from Chest X-rays using Deep Learning. Masters thesis, Dublin, National College of Ireland.

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Pneumonia is an infectious lung illness that is one of the leading causes of mortality in children under the age of five. One of the most common methods for detecting pneumonia is using chest X-ray images. Several Machine Learning algorithms have proven effective in providing computer-aided diagnosis by automatically classifying medical images. In the realm of medical research, deep learning applications have extended their scope. In this paper, a model that automatically selects the best parameters and two models using transfer learning has been proposed which classifies chest X-ray images as depicting pneumonia and normal. The proposed models uses the pre-trained weights for transfer learning models and kerastuner for the other CNN model. We utilized four well-known performance metrics to conduct comparisons with other state-of-the-art models, yielding the results where f ine tuned Efficient Net model achieved remarkable performance with 98% accuracy and F1 score of 0.98 followed by accuracy of 92% by VGG and 76% accuracy by CNN-keras tuner model respectively. These positive results allows to consider this research and proposal as alternative to be used in detecting the disease where there is lack of equipment and resources.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software
R Medicine > R Medicine (General)
H Social Sciences > HM Sociology > Information Science > Communication > Medical Informatics
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Clara Chan
Date Deposited: 06 Dec 2021 11:49
Last Modified: 06 Dec 2021 11:49

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