NMDNet: Explainable AI driven neuro muscular detection and classification using deep learning networks

Abstract

Neuromuscular disorders consist of a broad range of disease that affects muscles and nerves, which often lead to significant disability. To effectively manage these diseases, early diagnosis is crucial, but gold standard techniques such as electromyography and genetic testing are invasive, time-consuming, and costly. However, ultrasound, being non-invasive and cost-efficient can be a reliable alternative, but manual interpretation is a major limitation of this method. Our study presents a deep learning-based approach to automate the diagnosis of neuromuscular disorders. Our study leveraged Convolutional Neural Networks (CNNs) for feature extraction, combined with dense network for the classification task. To address the black-box nature of deep learning models, Local Interpretable Model-Agnostic Explanations (LIME) was employed, which segments that part of ultrasound images that influence the prediction. The proposed model achieved an accuracy of 94% with and a ROC-AUC score of 0.97, an F1 score of 0.80, a precision of 0.83 and a recall of 0.77 which demonstrates the ability of model to accurately differentiate between the healthy and pathological muscle ultrasound images. The interpretability mechanism (LIME) was successfully able to segment those part of ultrasound images that were influencing the model's prediction giving insights into prediction mechanism and enhancing trust on model. Our approach eliminates the need of image segmentation and need for manual feature extraction by employing end to end Convolutional Neural Network (CNN) model achieving 94% accuracy with balanced precision and recall. Also, we employed explainable mechanism which enhances the trust on the model which make it more adaptable for clinical applications. Our study presents a promising deep-learning based alternate for the diagnostic of neuromuscular disorder using ultrasound images with high accuracy. The integration of an interpretability mechanism enhances the trust on model making it suitable for integration in clinical applications. By automating the diagnostic mechanism neuromuscular disorders can be detected early which will help in strategize the management of neuromuscular disorders eventually enhancing the patient health.

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