A deep learning approach for pneumonia classification from chest X-Ray images with ensemble modelling and explainable AI
This thesis is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2021.
| Huvudupphovsman: | |
|---|---|
| Övriga upphovsmän: | |
| Materialtyp: | Lärdomsprov |
| Språk: | English |
| Publicerad: |
Brac University
2023
|
| Ämnen: | |
| Länkar: | http://hdl.handle.net/10361/21826 |
| id |
10361-21826 |
|---|---|
| record_format |
dspace |
| spelling |
10361-218262023-10-15T21:05:08Z A deep learning approach for pneumonia classification from chest X-Ray images with ensemble modelling and explainable AI Akhter, Nasrin Alam, Md. Ashraful Department of Computer Science and Engineering, Brac University Pneumonia Chest x-ray Transfer learning Convolutional neural network VGG16 VGG19 ResNet50 ResNet101 Inception V3 PNEXAI Machine learning Artificial intelligence This thesis is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (pages 39-43). Pneumonia is one of those frightening diseases that has a high mortality rate among children and the elderly, with an estimated 2 million fatalities per year. Pneumonia affects the poorest people in Africa and Asia the most, due to a lack of medical surveillance in such areas. It is responsible for 28 percent of all child fatalities in Bangladesh each year, and the number is likely to be considerably higher. In recent years, a number of computer-assisted diagnostic methods have been developed to assist in the detection of pneumonia. In this study, an efficient model PNEXAI is proposed to identify pneumonia utilizing Chest X-Ray images. We gathered and classified data using VGG16, VGG19, ResNet 50, ResNet 101 and Inception v3. The accuracy rate of 97.17% was reached by VGG16, 97.69% by VGG19, 97.35%by ResNet50, 95.63% by ResNet101, and 94.86% by Inception V3, respectively. We then developed an ensemble model containing the top three classifications (VGG16, VGG19 and ResNet50) which delivered 98.46 % of best overall accuracy. Finally, to better comprehend our categorization, we included explainable artificial intelligence in our model. Nasrin Akhter M.Sc. in Computer Science 2023-10-15T10:46:36Z 2023-10-15T10:46:36Z ©2021 2021-06-08 Thesis ID 17366005 http://hdl.handle.net/10361/21826 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 54 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Pneumonia Chest x-ray Transfer learning Convolutional neural network VGG16 VGG19 ResNet50 ResNet101 Inception V3 PNEXAI Machine learning Artificial intelligence |
| spellingShingle |
Pneumonia Chest x-ray Transfer learning Convolutional neural network VGG16 VGG19 ResNet50 ResNet101 Inception V3 PNEXAI Machine learning Artificial intelligence Akhter, Nasrin A deep learning approach for pneumonia classification from chest X-Ray images with ensemble modelling and explainable AI |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2021. |
| author2 |
Alam, Md. Ashraful |
| author_facet |
Alam, Md. Ashraful Akhter, Nasrin |
| format |
Thesis |
| author |
Akhter, Nasrin |
| author_sort |
Akhter, Nasrin |
| title |
A deep learning approach for pneumonia classification from chest X-Ray images with ensemble modelling and explainable AI |
| title_short |
A deep learning approach for pneumonia classification from chest X-Ray images with ensemble modelling and explainable AI |
| title_full |
A deep learning approach for pneumonia classification from chest X-Ray images with ensemble modelling and explainable AI |
| title_fullStr |
A deep learning approach for pneumonia classification from chest X-Ray images with ensemble modelling and explainable AI |
| title_full_unstemmed |
A deep learning approach for pneumonia classification from chest X-Ray images with ensemble modelling and explainable AI |
| title_sort |
deep learning approach for pneumonia classification from chest x-ray images with ensemble modelling and explainable ai |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/21826 |
| work_keys_str_mv |
AT akhternasrin adeeplearningapproachforpneumoniaclassificationfromchestxrayimageswithensemblemodellingandexplainableai AT akhternasrin deeplearningapproachforpneumoniaclassificationfromchestxrayimageswithensemblemodellingandexplainableai |
| _version_ |
1814309229811466240 |