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

## TL;DR

This paper introduces Duple-MONDNet, a deep learning model that improves early detection of motor neuron disease using color and texture features from brain images.

## Contribution

A novel dual feature extraction framework combining color and texture analysis for early MND detection using a mobile net model.

## Key findings

- Duple-MONDNet achieved a detection rate of 99.66% for MND.
- The model outperformed BPNN, CNN, SVM-RFE, and MLP with higher F1 scores.
- Color and texture features from DTI images were effectively used for classification.

## Abstract

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.

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.

The proposed deep MONDNet is suitable because it achieved a detection rate of 99.66% and can identify MND in its early stages.

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.