A Deep Learning Approach for Nerve Injury Classification in Brachial Plexopathies Using Magnetic Resonance Neurography with Modified Hiking Optimization Algorithm.
Authors
Affiliations (4)
Affiliations (4)
- School of Computer Science and Technology, Zhejiang Normal University, Jinhua321004, China (A.D.); Mathematics and Computer Science department, University of Ahmed DRAIA, 01000, Adrar, Algeria (A.D.). Electronic address: [email protected].
- Department of Mathematics, Faculty of Science, Zagazig University, Zagazig44519, Egypt (M.A.E.); Faculty of Computer Science and Engineering, Galala University, Suez435611, Egypt (M.A.E.); Artificial Intelligence Research Center (AIRC), Ajman University, Ajman346, United Arab Emirates (M.A.E.). Electronic address: [email protected].
- Technology of Radiology and Medical Imaging Program, Faculty of Applied Health Sciences Technology, Galala University, Suez435611, Egypt (M.G.K., H.G.E.M.A.H.). Electronic address: [email protected].
- Department of Diagnostic, Interventional Radiology and Molecular Imaging, Faculty of Medicine, Ain Shams University, Cairo11591, Egypt (H.G.E.M.A.H.); Technology of Radiology and Medical Imaging Program, Faculty of Applied Health Sciences Technology, Galala University, Suez435611, Egypt (M.G.K., H.G.E.M.A.H.). Electronic address: [email protected].
Abstract
Brachial plexopathies (BPs) encompass a complex spectrum of nerve injuries affecting motor and sensory function in the upper extremities. Diagnosis is challenging due to the intricate anatomy and symptom overlap with other neuropathies. Magnetic Resonance Neurography (MRN) provides advanced imaging but requires specialized interpretation. This study proposes an AI-based framework that combines deep learning (DL) with the modified Hiking Optimization Algorithm (MHOA) enhanced by a Comprehensive Learning (CL) technique to improve the classification of nerve injuries (neuropraxia, axonotmesis, neurotmesis) using MRN data. The framework utilizes MobileNetV4 for feature extraction and MHOA for optimized feature selection across different MRI sequences (STIR, T2, T1, and DWI). A dataset of 39 patients diagnosed with BP was used. The framework classifies injuries based on Seddon's criteria, distinguishing between normal and abnormal conditions as well as injury severity. The model achieved excellent performance, with 1.0000 accuracy in distinguishing normal from abnormal conditions using STIR and T2 sequences. For injury severity classification, accuracy was 0.9820 in STIR, outperforming the original HOA and other metaheuristic algorithms. Additionally, high classification accuracy (0.9667) was observed in DWI. The proposed framework outperformed traditional methods and demonstrated high sensitivity and specificity. The proposed AI-based framework significantly improves the diagnosis of BP by accurately classifying nerve injury types. By integrating DL and optimization techniques, it reduces diagnostic variability, making it a valuable tool for clinical settings with limited specialized neuroimaging expertise. This framework has the potential to enhance clinical decision-making and optimize patient outcomes through precise and timely diagnoses.