CLASSIFICATION OF PARKINSON'S DISEASE IN BRAIN MRI IMAGES USING DEEP RESIDUAL CONVOLUTIONAL NEURAL NETWORK (DRCNN)
In our aging culture, neurodegenerative disorders like Parkinson's disease (PD) are among the most serious health issues. It is a neurological condition that has social and economic effects on individuals. It happens because the brain's dopamine-producing cells are unable to produce enough...
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| Format: | Article |
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Polish Association for Knowledge Promotion
2023-06-01
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| Series: | Applied Computer Science |
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| Online Access: | http://www.acs.pollub.pl/index.php?option=com_content&view=article&id=570:classification-of-parkinsons-disease-in-brain-mri-images-using-deep-residual-convolutional-neural-network-drcnn&catid=97:vol-19-no-22023&Itemid=171 |
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| author | Puppala PRANEETH Majety SATHVIKA Vivek KOMMAREDDY Madala SARATH Saran MALLELA K. Suvarna VANI Prasun CHKRABARTI |
| author_facet | Puppala PRANEETH Majety SATHVIKA Vivek KOMMAREDDY Madala SARATH Saran MALLELA K. Suvarna VANI Prasun CHKRABARTI |
| author_sort | Puppala PRANEETH |
| collection | DOAJ |
| description | In our aging culture, neurodegenerative disorders like Parkinson's disease (PD) are among the most serious health issues. It is a neurological condition that has social and economic effects on individuals. It happens because the brain's dopamine-producing cells are unable to produce enough of the chemical to support the body's motor functions. The main symptoms of this illness are eyesight, excretion activity, speech, and mobility issues, followed by depression, anxiety, sleep issues, and panic attacks. The main aim of this research is to develop a workable clinical decision-making framework that aids the physician in diagnosing patients with PD influence. In this research, the authors propose a technique to classify Parkinson’s disease by MRI brain images. Initially, the input data is normalized using the min-max normalization method, and then noise is removed from the input images using a median filter. The Binary Dragonfly algorithm is then used to select features. In addition, the Dense-UNet technique is used to segment the diseased part from brain MRI images. The disease is then classified as Parkinson's disease or health control using the Deep Residual Convolutional Neural Network (DRCNN) technique along with the Enhanced Whale Optimization Algorithm (EWOA) to achieve better classification accuracy. In this work, the Parkinson's Progression Marker Initiative (PPMI) public dataset for Parkinson's MRI images is used. Indicators of accuracy, sensitivity, specificity and precision are used with manually collected data to evaluate the effectiveness of the proposed methodology. |
| first_indexed | 2024-03-13T00:13:47Z |
| format | Article |
| id | doaj.art-a987601b5baf4307b8fa8fc94d7c26cc |
| institution | Directory Open Access Journal |
| issn | 1895-3735 2353-6977 |
| language | English |
| last_indexed | 2024-03-13T00:13:47Z |
| publishDate | 2023-06-01 |
| publisher | Polish Association for Knowledge Promotion |
| record_format | Article |
| series | Applied Computer Science |
| spelling | doaj.art-a987601b5baf4307b8fa8fc94d7c26cc2023-07-12T05:57:32ZengPolish Association for Knowledge PromotionApplied Computer Science1895-37352353-69772023-06-0119212514710.35784/acs-2023-19CLASSIFICATION OF PARKINSON'S DISEASE IN BRAIN MRI IMAGES USING DEEP RESIDUAL CONVOLUTIONAL NEURAL NETWORK (DRCNN)Puppala PRANEETH 0https://orcid.org/0009-0008-1230-9562Majety SATHVIKA 1https://orcid.org/0009-0009-3380-3137Vivek KOMMAREDDY 2https://orcid.org/0009-0006-8417-7058Madala SARATH3https://orcid.org/0009-0008-1258-3828Saran MALLELA 4https://orcid.org/0009-0004-4468-6136K. Suvarna VANI 5https://orcid.org/0000-0003-1899-9580Prasun CHKRABARTI6https://orcid.org/0009-0002-9892-7754Velagapudi Ramakrishna Siddhartha Engineering College, Kanuru, Vijayawada, Andhra PradeshVelagapudi Ramakrishna Siddhartha Engineering College, Kanuru, Vijayawada, Andhra PradeshVelagapudi Ramakrishna Siddhartha Engineering College, Kanuru, Vijayawada, Andhra PradeshVelagapudi Ramakrishna Siddhartha Engineering College, Kanuru, Vijayawada, Andhra PradeshVelagapudi Ramakrishna Siddhartha Engineering College, Kanuru, Vijayawada, Andhra PradeshVelagapudi Ramakrishna Siddhartha Engineering College, Kanuru, Vijayawada, Andhra PradeshITM SLS Baroda University, VadodaraIn our aging culture, neurodegenerative disorders like Parkinson's disease (PD) are among the most serious health issues. It is a neurological condition that has social and economic effects on individuals. It happens because the brain's dopamine-producing cells are unable to produce enough of the chemical to support the body's motor functions. The main symptoms of this illness are eyesight, excretion activity, speech, and mobility issues, followed by depression, anxiety, sleep issues, and panic attacks. The main aim of this research is to develop a workable clinical decision-making framework that aids the physician in diagnosing patients with PD influence. In this research, the authors propose a technique to classify Parkinson’s disease by MRI brain images. Initially, the input data is normalized using the min-max normalization method, and then noise is removed from the input images using a median filter. The Binary Dragonfly algorithm is then used to select features. In addition, the Dense-UNet technique is used to segment the diseased part from brain MRI images. The disease is then classified as Parkinson's disease or health control using the Deep Residual Convolutional Neural Network (DRCNN) technique along with the Enhanced Whale Optimization Algorithm (EWOA) to achieve better classification accuracy. In this work, the Parkinson's Progression Marker Initiative (PPMI) public dataset for Parkinson's MRI images is used. Indicators of accuracy, sensitivity, specificity and precision are used with manually collected data to evaluate the effectiveness of the proposed methodology.http://www.acs.pollub.pl/index.php?option=com_content&view=article&id=570:classification-of-parkinsons-disease-in-brain-mri-images-using-deep-residual-convolutional-neural-network-drcnn&catid=97:vol-19-no-22023&Itemid=171parkinson’s diseasedeep residual convolutional neural networkdeep learninghealth control |
| spellingShingle | Puppala PRANEETH Majety SATHVIKA Vivek KOMMAREDDY Madala SARATH Saran MALLELA K. Suvarna VANI Prasun CHKRABARTI CLASSIFICATION OF PARKINSON'S DISEASE IN BRAIN MRI IMAGES USING DEEP RESIDUAL CONVOLUTIONAL NEURAL NETWORK (DRCNN) Applied Computer Science parkinson’s disease deep residual convolutional neural network deep learning health control |
| title | CLASSIFICATION OF PARKINSON'S DISEASE IN BRAIN MRI IMAGES USING DEEP RESIDUAL CONVOLUTIONAL NEURAL NETWORK (DRCNN) |
| title_full | CLASSIFICATION OF PARKINSON'S DISEASE IN BRAIN MRI IMAGES USING DEEP RESIDUAL CONVOLUTIONAL NEURAL NETWORK (DRCNN) |
| title_fullStr | CLASSIFICATION OF PARKINSON'S DISEASE IN BRAIN MRI IMAGES USING DEEP RESIDUAL CONVOLUTIONAL NEURAL NETWORK (DRCNN) |
| title_full_unstemmed | CLASSIFICATION OF PARKINSON'S DISEASE IN BRAIN MRI IMAGES USING DEEP RESIDUAL CONVOLUTIONAL NEURAL NETWORK (DRCNN) |
| title_short | CLASSIFICATION OF PARKINSON'S DISEASE IN BRAIN MRI IMAGES USING DEEP RESIDUAL CONVOLUTIONAL NEURAL NETWORK (DRCNN) |
| title_sort | classification of parkinson s disease in brain mri images using deep residual convolutional neural network drcnn |
| topic | parkinson’s disease deep residual convolutional neural network deep learning health control |
| url | http://www.acs.pollub.pl/index.php?option=com_content&view=article&id=570:classification-of-parkinsons-disease-in-brain-mri-images-using-deep-residual-convolutional-neural-network-drcnn&catid=97:vol-19-no-22023&Itemid=171 |
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