Pneumonia detection and classification using neural network
| bracu.degree.level | Undergraduate | |
| bracu.type.group | Student Works | |
| datacite.rights | Open Access | |
| dc.contributor.advisor | Ajwad, Rasif | |
| dc.contributor.author | Showkat, Jobayer Bin | |
| dc.contributor.author | Alavi, Salsabil Hossain | |
| dc.contributor.author | Sadman, Sean | |
| dc.contributor.department | Department of Computer Science and Engineering | |
| dc.date.accessioned | 2025-10-05T10:07:39Z | |
| dc.date.available | 2025-10-05T10:07:39Z | |
| dc.date.copyright | 2020 | |
| dc.date.issued | 2020-10 | |
| dc.description | Cataloged from PDF version of thesis. | |
| dc.description | Includes bibliographical references (pages 22-23). | |
| dc.description | This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2020. | en_US |
| dc.description.abstract | Medical image classification is of vital importance when it comes to clinical treatment. Numerous imaging techniques exist in diagnosis of various diseases, but X-rays remain one of the most popular techniques. Given that, X-rays are inexpensive and the easiest to perform, it is one of the most frequently used radiology examinations for diagnosis. X-rays are performed in different parts of the body. In this paper, we will focus on chest X-rays which give us images of the lungs, heart, airways and the bones which are present in the chest and spine. Chest X-rays can also display fluid inside the lungs or the area surrounding the lungs. The image produced by chest X-rays can help doctors determine many lung-diseases. However, examining chest X-rays clinically can be tedious and complex. Therefore, computer aided detection can help to achieve more accurate and simpler ways of acquiring correct diagnosis. Computer aided detection techniques include the use of machine learning algorithms and deep learning methods. | en_US |
| dc.description.degree | Bachelor of Science in Computer Science | |
| dc.description.statementofresponsibility | Jobayer Bin Showkat | |
| dc.description.statementofresponsibility | Salsabil Hossain Alavi | |
| dc.description.statementofresponsibility | Sean Sadman | |
| dc.format.extent | 32 pages | |
| dc.identifier.other | ID 17301217 | |
| dc.identifier.other | ID 17141010 | |
| dc.identifier.other | ID 17201071 | |
| dc.identifier.uri | http://hdl.handle.net/10361/26819 | |
| dc.language.iso | en | en_US |
| dc.publisher | BRAC University | en_US |
| dc.rights | 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. | |
| dc.subject | Medical images | en_US |
| dc.subject | Image processing | en_US |
| dc.subject | Disease detection | en_US |
| dc.subject | Thoracic diseases | en_US |
| dc.subject | Deep learning | en_US |
| dc.subject | Neural networks | en_US |
| dc.subject | Computer aided detection | en_US |
| dc.subject.lcsh | Diagnostic imaging--Data processing. | |
| dc.subject.lcsh | Neural networks (Computer science). | |
| dc.subject.lcsh | Pneumonia--Diagnosis. | |
| dc.title | Pneumonia detection and classification using neural network | en_US |
| dc.type | Thesis | en_US |