Duple-MONDNet: duple deep learning-based mobile net for motor neuron disease identification.

Abstract

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.