Enhanced medical image analysis: leveraging CUDA for fast and accurate Pneumonia detection with optimized CNNs
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.
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| Langue: | English |
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Brac University
2024
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| Accès en ligne: | http://hdl.handle.net/10361/24360 |
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10361-243602024-10-21T21:01:29Z Enhanced medical image analysis: leveraging CUDA for fast and accurate Pneumonia detection with optimized CNNs Alvi, Md.Waseq Alauddin Alam, Md. Ashraful Department of Computer Science and Engineering, Brac University Disease detection Pneumonia detection Image analysis Convolutional neural network Healthcare diagnostics CUDA NVIDIA Deep learning Diagnostic imaging--Data processing. Image processing--Digital techniques. Pneumonia. Computational intelligence. Neural networks (Computer science). This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024. Cataloged from PDF version of thesis. Includes bibliographical references (pages 33-34). Pneumonia, a known leading child killer and a general health burden, continues to be a major concern due to its high morbidity and mortality rates in the developing world, which calls for prompt and accurate diagnosis. This paper aims at proposing a novel medical image analysis framework that can be used in the enhancement of pneumonia from Chest X-ray images in terms of speed and accuracy. Building on the capability of the Convolutional Neural Networks (CNNs) that have been tuned using NVIDIA CUDA, this strategy enhances the computational capabilities and enables real time analysis. Hence, it meant that we were training a novel deep learning model which was fit for the specific task we were undertaking involving identification of bacterial, viral pneumonia in addition to normal cases. The model finds feature extraction and considers incorporation of advanced layers and/or architectures. By paralleling the codes with Cuda we were able to reduce the time it takes to train and make prediction on models while at the same time not being compromising on the quality of the models. In addition, Our experimental results show that, our CUDA-optimized CNN outperforms and achieve equal or higher accuracy against the traditional methods, all this in a drastically shorter time. There is potential for deploying associated high-resolution diagnostic equipment in clinical environment, specifically in situation where decisions are needed quickly. Our self-contrary contributions signify the effectiveness as well as effectiveness of deep learning and high-performance computing to augment the medical diagnostic technique and would open the area to extensive applications of medical image analysis in the future. Md.Waseq Alauddin Alvi B.Sc. in Computer Science 2024-10-21T06:15:57Z 2024-10-21T06:15:57Z ©2024 2024-05 Thesis ID 20101153 http://hdl.handle.net/10361/24360 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. 45 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Disease detection Pneumonia detection Image analysis Convolutional neural network Healthcare diagnostics CUDA NVIDIA Deep learning Diagnostic imaging--Data processing. Image processing--Digital techniques. Pneumonia. Computational intelligence. Neural networks (Computer science). |
| spellingShingle |
Disease detection Pneumonia detection Image analysis Convolutional neural network Healthcare diagnostics CUDA NVIDIA Deep learning Diagnostic imaging--Data processing. Image processing--Digital techniques. Pneumonia. Computational intelligence. Neural networks (Computer science). Alvi, Md.Waseq Alauddin Enhanced medical image analysis: leveraging CUDA for fast and accurate Pneumonia detection with optimized CNNs |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024. |
| author2 |
Alam, Md. Ashraful |
| author_facet |
Alam, Md. Ashraful Alvi, Md.Waseq Alauddin |
| format |
Thesis |
| author |
Alvi, Md.Waseq Alauddin |
| author_sort |
Alvi, Md.Waseq Alauddin |
| title |
Enhanced medical image analysis: leveraging CUDA for fast and accurate Pneumonia detection with optimized CNNs |
| title_short |
Enhanced medical image analysis: leveraging CUDA for fast and accurate Pneumonia detection with optimized CNNs |
| title_full |
Enhanced medical image analysis: leveraging CUDA for fast and accurate Pneumonia detection with optimized CNNs |
| title_fullStr |
Enhanced medical image analysis: leveraging CUDA for fast and accurate Pneumonia detection with optimized CNNs |
| title_full_unstemmed |
Enhanced medical image analysis: leveraging CUDA for fast and accurate Pneumonia detection with optimized CNNs |
| title_sort |
enhanced medical image analysis: leveraging cuda for fast and accurate pneumonia detection with optimized cnns |
| publisher |
Brac University |
| publishDate |
2024 |
| url |
http://hdl.handle.net/10361/24360 |
| work_keys_str_mv |
AT alvimdwaseqalauddin enhancedmedicalimageanalysisleveragingcudaforfastandaccuratepneumoniadetectionwithoptimizedcnns |
| _version_ |
1814307595084627968 |