{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,4]],"date-time":"2026-11-04T09:25:53Z","timestamp":1793784353770,"version":"4.6.1"},"reference-count":0,"publisher":"The Scientific and Technological Research Council of Turkey (TUBITAK-ULAKBIM) - DIGITAL COMMONS JOURNALS","issue":"1","license":[{"start":{"date-parts":[[2025,2,18]],"date-time":"2025-02-18T00:00:00Z","timestamp":1739836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Turkish Journal of Medical Sciences"],"abstract":"<jats:p>Background\/aim: Motor neuron disease (MND) is a devastating neuron ailment that affects the motor neurons that regulate muscular voluntary actions. It is a rare disorder that gradually destroys aspects of neurological function. In general, MND arises as a result of a combination of natural, behavioral, and genetic influences. However, early detection of MND is a challenging task and manual identification is time-consuming. To overcome this, a novel deep learning-based duple feature extraction framework is proposed for the early detection of MND.Materials and methods: Diffusion tensor imaging tractography (DTI) images were initially analyzed for color and textural features using dual feature extraction. Local binary pattern (LBP)-based methods were used to extract textural data from images by examining nearby pixel values. A color information feature was then added to the LBP-based feature during the classification phase for extracting color features. A flattened image was then fed into the MONDNet for classifying normal and abnormal cases of MND based on color and texture features.Results: The proposed deep MONDNet is suitable because it achieved a detection rate of 99.66% and can identify MND in its early stages.Conclusion: The proposed mobile net model achieved an overall F1 score of 13.26%, 6.15%, 5.56%, and 5.96% compared to the BPNN, CNN, SVM-RFE, and MLP algorithms, respectively.<\/jats:p>","DOI":"10.55730\/1300-0144.5952","type":"journal-article","created":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T11:42:40Z","timestamp":1740483760000},"page":"140-151","source":"Crossref","is-referenced-by-count":0,"title":["Duple-MONDNet: duple deep learning-based mobile net for motor neuron disease identification"],"prefix":"10.55730","volume":"55","author":[{"given":"SONY","family":"HELEN","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"JOSEPH","family":"JAWHAR","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"34691","published-online":{"date-parts":[[2025,2,18]]},"container-title":["Turkish Journal of Medical Sciences"],"original-title":[],"language":"en","deposited":{"date-parts":[[2026,11,4]],"date-time":"2026-11-04T08:29:10Z","timestamp":1793780950000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.tubitak.gov.tr\/medical\/vol55\/iss1\/16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,18]]},"references-count":0,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,2,18]]}},"URL":"https:\/\/doi.org\/10.55730\/1300-0144.5952","relation":{},"ISSN":["1300-0144"],"issn-type":[{"value":"1300-0144","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,18]]}}}