Analysing Pneumonia Disease Depending on X-Ray Images of Chest Using Deep Learning
Using 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 a...
Saved in:
| Main Author: | |
|---|---|
| Published: |
2023
|
| Subjects: | |
| Online Access: | https://bspace.buid.ac.ae/handle/1234/2475 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1862980610920808448 |
|---|---|
| author | Khamees, Ahmed |
| author_facet | Khamees, Ahmed |
| author_role | author |
| dc.contributor.none.fl_str_mv | Professor Khaled Shalaan |
| dc.creator.none.fl_str_mv | Khamees, Ahmed |
| dc.date.none.fl_str_mv | 2023-10 2024-01-04T06:11:15Z 2024-01-04T06:11:15Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | 20181437 https://bspace.buid.ac.ae/handle/1234/2475 |
| dc.language.none.fl_str_mv | en |
| dc.publisher.none.fl_str_mv | The British University in Dubai (BUiD) |
| dc.subject.none.fl_str_mv | Machine Learning (ML), deep learning, convolutional neural network, image classification, x-ray, pneumonia |
| dc.title.none.fl_str_mv | Analysing Pneumonia Disease Depending on X-Ray Images of Chest Using Deep Learning |
| dc.type.none.fl_str_mv | Dissertation |
| description | Using 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. |
| id | budr_f831b0a5de5d71d27ec8bbb23322b2bd |
| identifier_str_mv | 20181437 |
| language_invalid_str_mv | en |
| network_acronym_str | budr |
| network_name_str | The British University in Dubai repository |
| oai_identifier_str | oai:bspace.buid.ac.ae:1234/2475 |
| publishDate | 2023 |
| publisher.none.fl_str_mv | The British University in Dubai (BUiD) |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Analysing Pneumonia Disease Depending on X-Ray Images of Chest Using Deep LearningKhamees, AhmedMachine Learning (ML), deep learning, convolutional neural network, image classification, x-ray, pneumoniaUsing 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.The British University in Dubai (BUiD)Professor Khaled Shalaan2024-01-04T06:11:15Z2024-01-04T06:11:15Z2023-10Dissertationapplication/pdf20181437https://bspace.buid.ac.ae/handle/1234/2475enoai:bspace.buid.ac.ae:1234/24752024-01-04T23:00:16Z |
| spellingShingle | Analysing Pneumonia Disease Depending on X-Ray Images of Chest Using Deep Learning Khamees, Ahmed Machine Learning (ML), deep learning, convolutional neural network, image classification, x-ray, pneumonia |
| title | Analysing Pneumonia Disease Depending on X-Ray Images of Chest Using Deep Learning |
| title_full | Analysing Pneumonia Disease Depending on X-Ray Images of Chest Using Deep Learning |
| title_fullStr | Analysing Pneumonia Disease Depending on X-Ray Images of Chest Using Deep Learning |
| title_full_unstemmed | Analysing Pneumonia Disease Depending on X-Ray Images of Chest Using Deep Learning |
| title_short | Analysing Pneumonia Disease Depending on X-Ray Images of Chest Using Deep Learning |
| title_sort | Analysing Pneumonia Disease Depending on X-Ray Images of Chest Using Deep Learning |
| topic | Machine Learning (ML), deep learning, convolutional neural network, image classification, x-ray, pneumonia |
| url | https://bspace.buid.ac.ae/handle/1234/2475 |