{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T09:18:50Z","timestamp":1770887930883,"version":"3.50.1"},"reference-count":17,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2021,3,20]],"date-time":"2021-03-20T00:00:00Z","timestamp":1616198400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2021,11,17]]},"abstract":"<jats:p>\u00a0Normal Pressure Hydrocephalus (NPH), an Atypical Parkinsonian syndrome, is a neurological syndrome that mainly affects elderly people. This syndrome shows the symptoms of Parkinson\u2019s disease (PD), such as walking impairment, dementia, impaired bladder control, and mental impairment. The Magnetic Resonance Imaging (MRI) is the aptest modality for the detection of the abnormal build-up of cerebrospinal fluid in the brain\u2019s cavities or ventricles, which is the major cause of NPH. This work aims to develop an automated biomarker for NPH segmentation and classification (NPH-SC) that efficiently detect hydrocephalus using a deep learning-based approach. Removal of non-cerebral tissues (skull, scalp, and dura) and noise from brain images by skull stripping, unsharp-mask based edge sharpening, segmentation by marker-based watershed algorithm, and labelling are performed to improve the accuracy of the CNN based classification system. The brain ventricles are extracted using the external and internal markers and then fed into the convolutional neural networks (CNN) for classification. This automated NPH-SC model achieved a sensitivity of 96%, a specificity of 100%, and a validation accuracy of 97%. The prediction system, with the help of a CNN classifier, is used for the calculation of test accuracy of the system and obtained promising 98% accuracy.<\/jats:p>","DOI":"10.3233\/jifs-189852","type":"journal-article","created":{"date-parts":[[2021,3,23]],"date-time":"2021-03-23T13:29:45Z","timestamp":1616506185000},"page":"5299-5307","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":8,"title":["A Novel deep learning approach for the automated diagnosis of normal pressure hydrocephalus"],"prefix":"10.1177","volume":"41","author":[{"given":"B","family":"Rudhra","sequence":"first","affiliation":[{"name":"Indian Institute of Information Technology and Management, Trivandrum, India"}]},{"given":"G","family":"Malu","sequence":"additional","affiliation":[{"name":"Indian Institute of Information Technology and Management, Trivandrum, India"}]},{"given":"Elizabeth","family":"Sherly","sequence":"additional","affiliation":[{"name":"Indian Institute of Information Technology and Management, Trivandrum, India"}]},{"given":"Robert","family":"Mathew","sequence":"additional","affiliation":[{"name":"Anugraha Neurocare, Trivandrum, India"}]}],"member":"179","published-online":{"date-parts":[[2021,3,20]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"DemyanchukA. PushkinaE. RusskikhN. ShtokaloD. and MishinovS. Hydrocephalus verification on brain magnetic resonance images with deep convolutional neural networks and \u201ctransfer learning\u201d technique (2019). http:\/\/arxiv.org\/abs\/1909.10473."},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","unstructured":"DrevelegasA. Imaging of brain tumors with histological correlations Imaging Brain Tumors with Histol Correl (2011) 1\u2013432. doi:10.1007\/978-3-540-87650-2","DOI":"10.1007\/978-3-540-87650-2"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1097\/MD.0000000000021229"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1117\/12.2216511"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00701-020-04447-x"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.nicl.2018.11.015"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","unstructured":"IrieR. OtsukaY. HagiwaraA. KamagataK. KamiyaK. SuzukiM. WadaA. MaekawaT. FujitaS. 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