Version 1
: Received: 10 September 2024 / Approved: 11 September 2024 / Online: 11 September 2024 (16:19:52 CEST)
How to cite:
Al-Barazanchi, K. K.; Al-Timemy, A. H.; Kadhim, Z. M. Bag of Feature-Based Ensemble Subspace KNN Classifier in Muscle Ultrasound Diagnosis of Diabetic Peripheral Neuropathy. Preprints2024, 2024090925. https://doi.org/10.20944/preprints202409.0925.v1
Al-Barazanchi, K. K.; Al-Timemy, A. H.; Kadhim, Z. M. Bag of Feature-Based Ensemble Subspace KNN Classifier in Muscle Ultrasound Diagnosis of Diabetic Peripheral Neuropathy. Preprints 2024, 2024090925. https://doi.org/10.20944/preprints202409.0925.v1
Al-Barazanchi, K. K.; Al-Timemy, A. H.; Kadhim, Z. M. Bag of Feature-Based Ensemble Subspace KNN Classifier in Muscle Ultrasound Diagnosis of Diabetic Peripheral Neuropathy. Preprints2024, 2024090925. https://doi.org/10.20944/preprints202409.0925.v1
APA Style
Al-Barazanchi, K. K., Al-Timemy, A. H., & Kadhim, Z. M. (2024). Bag of Feature-Based Ensemble Subspace KNN Classifier in Muscle Ultrasound Diagnosis of Diabetic Peripheral Neuropathy. Preprints. https://doi.org/10.20944/preprints202409.0925.v1
Chicago/Turabian Style
Al-Barazanchi, K. K., Ali Hussein Al-Timemy and Zahid Mohamed Kadhim. 2024 "Bag of Feature-Based Ensemble Subspace KNN Classifier in Muscle Ultrasound Diagnosis of Diabetic Peripheral Neuropathy" Preprints. https://doi.org/10.20944/preprints202409.0925.v1
Abstract
Muscle ultrasound quantification is a valuable complementary diagnostic tool for diabetic peripheral neuropathy (DPN). DPN significantly impacts the lives of individuals with diabetes, leading to pain, lower limb amputation, and disability, affecting patient quality of life. This work develops a computer-aided diagnostic (CAD) system based on muscle ultrasound that integrates the bag of features (BOF) and an ensemble subspace k-nearest neighbour (KNN) algorithm for DPN detection. The BOF creates a histogram of visual word occurrences to represent the muscle ultrasound images and trains an ensemble classifier through cross-validation, determining optimal parameters to improve classification accuracy for the ensemble diagnosis system. The dataset includes ultrasound images of six muscles from 53 subjects, consisting of 27 control and 26 patient cases. An empirical analysis was conducted for each binary classifier based on muscle type to select the best vocabulary tree properties or K values for BOF. The result indicates that ensemble subspace KNN classification, based on the bag of features, achieved an accuracy of 97.23%. This study suggests muscle ultrasound as a promising diagnostic tool with the potential for image recognition and interpretation. CAD systems can accurately diagnose muscle pathology, helping to overcome limitations and identify issues in individuals with diabetes.
Keywords
bag of features; ensemble subspace KNN; muscle ultrasound; diabetic peripheral neuropathy; speeded up robust features
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
