Detection of motor nervous disease using deep learning based Duple feature extraction network

BackgroundA motor nervous disease (MND) is a debilitating nervous disease that affects motor neurons that regulates the muscular voluntary movements. The disease gradually destroys parts of the neurological system. Generally, MND develops owing to a grouping of genetic, behavioural, and natural features.ObjectiveHowever, early detection of MND is challenging and manual identification requires a lot of time. Therefore, automated methods like deep learning structures are needed to detect MND quickly and more accurately than manual classification. In this work, a novel deep learning-based Duple feature extraction network is proposed for identifying MND in its early stages.MethodsInitially, the input DTI images are pre-processed utilizing a Gaussian adaptive bilateral filter (GAB) to improve the quality of the image. Then the pre-processed DTI images are fed into the dual feature extraction phase for colour and structural conversion. The Colour Information Feature (CIF) with Local and Global sampling (LOG) is integrated into the LinkNet module to extract colour features. Moreover, the Local Binary Pattern (LBP) with Edge sampling models is integrated into the MobileNet module to extract edge features. Afterward, the extracted colour and texture features of images are flattered and given as the input to a Deep Neural Network for classifying the MND levels.ResultsFrom the test results, the proposed Duple feature extraction network has yielded a 99.62% accuracy rate. The proposed DNN improves its F1-score by 1.32%, 2.1%, and 3.18% better than FNN, GNN, and GRU respectively. The proposed Duple-feature extraction network improves overall accuracy by 6.15%, 5.56%, 5.96%, and 6.68% compared to CNN, SVM-RFE, MLP, and Tri-planar CNN respectively.ConclusionThe novel deep learning-based Duple feature extraction framework shows promising results in early detection of motor nervous disease, significantly improving accuracy and f1-scores compared to existing models.