| _version_ | 1866902310881329152 |
|---|---|
| author | Sindhuja Boda Rajyalaxmi Gadusu Deepika Mallupally Akhila Shyamala Heena Begum |
| author_facet | Sindhuja Boda Rajyalaxmi Gadusu Deepika Mallupally Akhila Shyamala Heena Begum |
| contents | <p>Abstract— Parkinson's disease is a chronic, progressive <br>neurodegenerative disorder caused by the gradual damage <br>of dopamine-producing neurons in the substantia nigra. It <br>poses certain burdens on individuals suffering from it. As <br>the advancement of medical diagnostics continues, newer <br>methods of detection through machine learning analyze <br>different modalities of medical data such as speech patterns, <br>gait <br>analysis, <br>electroencephalography signals, and <br>advanced imaging technologies. Among these latest <br>methods, DaTScan is one of the most effective tools. This <br>type of SPECT (Single-Photon Emission Computed <br>Tomography) imaging will allow detailed views of the <br>dopamine transporter activity in the brain, providing <br>crucial information for the definite diagnosis of <br>Parkinson's Disease. Our project aims to create an innovative <br>web-based platform that utilizes DaTScan images together with <br>advanced machine learning techniques to transform the way <br>Parkinson’s is detected. Through this platform, individuals <br>seeking to determine whether they have Parkinson’s Disease will <br>be able to easily upload their DaTScan images, which will then be <br>analyzed by state-of-the-art algorithms skilled to pick out <br>dopamine transporter irregularities suggestive of the disease. This <br>system guarantees quick and precise diagnoses, which detail the <br>number of stages the condition has reached (5 stages), and will <br>have complete visual reports to be viewed and downloaded to aid <br>decision-making clinically through Expert connect.By developing <br>this very wide, friendly, and scalable platform, we are trying to <br>make advanced tools available much more widely for the detection <br>of Parkinson's so that people can reach early diagnosis and have <br>better results for patients via seamless clinical integration</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15387246 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Parkinson's Disease Detection using Machine Learning on DaTScan image Sindhuja Boda Rajyalaxmi Gadusu Deepika Mallupally Akhila Shyamala Heena Begum <p>Abstract— Parkinson's disease is a chronic, progressive <br>neurodegenerative disorder caused by the gradual damage <br>of dopamine-producing neurons in the substantia nigra. It <br>poses certain burdens on individuals suffering from it. As <br>the advancement of medical diagnostics continues, newer <br>methods of detection through machine learning analyze <br>different modalities of medical data such as speech patterns, <br>gait <br>analysis, <br>electroencephalography signals, and <br>advanced imaging technologies. Among these latest <br>methods, DaTScan is one of the most effective tools. This <br>type of SPECT (Single-Photon Emission Computed <br>Tomography) imaging will allow detailed views of the <br>dopamine transporter activity in the brain, providing <br>crucial information for the definite diagnosis of <br>Parkinson's Disease. Our project aims to create an innovative <br>web-based platform that utilizes DaTScan images together with <br>advanced machine learning techniques to transform the way <br>Parkinson’s is detected. Through this platform, individuals <br>seeking to determine whether they have Parkinson’s Disease will <br>be able to easily upload their DaTScan images, which will then be <br>analyzed by state-of-the-art algorithms skilled to pick out <br>dopamine transporter irregularities suggestive of the disease. This <br>system guarantees quick and precise diagnoses, which detail the <br>number of stages the condition has reached (5 stages), and will <br>have complete visual reports to be viewed and downloaded to aid <br>decision-making clinically through Expert connect.By developing <br>this very wide, friendly, and scalable platform, we are trying to <br>make advanced tools available much more widely for the detection <br>of Parkinson's so that people can reach early diagnosis and have <br>better results for patients via seamless clinical integration</p> |
| title | Parkinson's Disease Detection using Machine Learning on DaTScan image |
| url | https://doi.org/10.5281/zenodo.15387246 |