Early detection of parkinson’s disease using image processing and artificial neural network
This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.
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| Idioma: | English |
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BRAC University
2018
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| Acesso em linha: | http://hdl.handle.net/10361/10151 |
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10361-101512022-01-26T10:18:21Z Early detection of parkinson’s disease using image processing and artificial neural network Rumman, Mosarrat Tasneem, Abu Nayeem Farzana, Sadia Alam, Md. Ashraful Department of Computer Science and Engineering, BRAC University Image processing Artificial neural network Parkinson’s disease Early detection This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018. Cataloged from PDF version of thesis. Includes bibliographical references (pages 43-45). Early detection of Parkinson‟s Disease (PD) is very crucial for effective management and treatment of the disease. Dopaminergic images such as Single Photon Emission Tomography (SPECT) using 123I-Ioflupane can substantially detect Parkinson‟s Disease at an early stage. However, till today, these images are mostly interpreted by humans which can manifest interobserver variability and inconsistency. To improve the imaging diagnosis of PD, we propose a model in this paper, for early detection of Parkinson‟s disease using Image Processing and Artificial Neural Network (ANN). The model used 200 SPECT images, 100 of healthy normal and 100 of PD, obtained from Parkinson‟s Progression Marker‟s Initiative (PPMI) database and processed them to find the area of Caudate and Putamen which is the Region of Interest (ROI) for this study. The area values were then fed to the ANN which is hypothesized to mimic the pattern recognition of a human observer. The simple but fast ANN built, could classify subjects with and without PD with an accuracy of 94%, sensitivity of 100% and specificity of 88%. Hence it can be inferred that the proposed system has the potential to be an effective way to aid the clinicians in the accurate diagnosis of Parkinson‟s disease. Mosarrat Rumman Abu Nayeem Tasneem Sadia Farzana B. Computer Science and Engineering 2018-05-15T06:02:36Z 2018-05-15T06:02:36Z 2018 2018-04 Thesis ID 14101080 ID 14101121 ID 14101128 http://hdl.handle.net/10361/10151 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 |
Image processing Artificial neural network Parkinson’s disease Early detection |
| spellingShingle |
Image processing Artificial neural network Parkinson’s disease Early detection Rumman, Mosarrat Tasneem, Abu Nayeem Farzana, Sadia Early detection of parkinson’s disease using image processing and artificial neural network |
| description |
This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018. |
| author2 |
Alam, Md. Ashraful |
| author_facet |
Alam, Md. Ashraful Rumman, Mosarrat Tasneem, Abu Nayeem Farzana, Sadia |
| format |
Thesis |
| author |
Rumman, Mosarrat Tasneem, Abu Nayeem Farzana, Sadia |
| author_sort |
Rumman, Mosarrat |
| title |
Early detection of parkinson’s disease using image processing and artificial neural network |
| title_short |
Early detection of parkinson’s disease using image processing and artificial neural network |
| title_full |
Early detection of parkinson’s disease using image processing and artificial neural network |
| title_fullStr |
Early detection of parkinson’s disease using image processing and artificial neural network |
| title_full_unstemmed |
Early detection of parkinson’s disease using image processing and artificial neural network |
| title_sort |
early detection of parkinson’s disease using image processing and artificial neural network |
| publisher |
BRAC University |
| publishDate |
2018 |
| url |
http://hdl.handle.net/10361/10151 |
| work_keys_str_mv |
AT rummanmosarrat earlydetectionofparkinsonsdiseaseusingimageprocessingandartificialneuralnetwork AT tasneemabunayeem earlydetectionofparkinsonsdiseaseusingimageprocessingandartificialneuralnetwork AT farzanasadia earlydetectionofparkinsonsdiseaseusingimageprocessingandartificialneuralnetwork |
| _version_ |
1814308796980264960 |