Analysing Pneumonia Disease Depending on X-Ray Images of Chest Using Deep Learning

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dc.contributor.advisorProfessor Khaled Shalaan
dc.contributor.authorKhamees, Ahmed
dc.date.accessioned2024-01-04T06:11:15Z
dc.date.available2024-01-04T06:11:15Z
dc.date.issued2023-10
dc.description.abstractUsing Machine Learning (ML) in industry has vast applications, however using it in medical domain alerts a priority to help doctors determine unseen or hidden indicators of any probable illness or medical condition, which if not treated urgently may affect patient health. In this paper, the author aims to review and enhance Image recognition and classification using ML methodologies. The data input of X-ray images taken for medical proposes, used to gain better outcomes through advanced analysis of the training data, this includes specifying the average amount of data needed for training to make a good enough predictions using deep learning (DL) in order to save costs. In addition, exploring training data by applying data cleaning techniques to gain a well-balanced model for classification purposes. Author shown that setting 1600 x-ray images or more, as a training data input, tend to enforce a steady percentage of accuracy greater than 90%. Moreover, author described the results of using dirty (unclean) or unbalanced data to the ML model, which showed a clearly drop in precision, recall and F1 score percentages. Overall, our proposed experiments showed the importance of having a quality training data in achieving higher performance results.
dc.identifier.other20181437
dc.identifier.urihttps://bspace.buid.ac.ae/handle/1234/2475
dc.language.isoen
dc.publisherThe British University in Dubai (BUiD)
dc.subjectMachine Learning (ML), deep learning, convolutional neural network, image classification, x-ray, pneumonia
dc.titleAnalysing Pneumonia Disease Depending on X-Ray Images of Chest Using Deep Learning
dc.typeDissertation
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