Feature Extraction and Identification of Rheumatoid Nodules Using Advanced Image Processing Techniques.
Background/Objectives: Accurate detection and classification of nodules in medical images, particularly rheumatoid nodules, are critical due to the varying nature of these nodules, where their specific type is often unknown before analysis. This study addresses the challenges of multi-class predicti...
| Published in: | Rheumato Vol. 4; no. 4; pp. 176 - 193 |
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| Main Authors: | , |
| Format: | diagnostic images equations & formulas research tables/charts Journal Article |
| Published: |
MDPI
Dec2024
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=181939050&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 181939050 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 26740621 N1MW jtl: Rheumato issn: 26740621 maglogo: N pubinfo: dt: Dec2024 vid: 4 iid: 4 pid: 97109 pub: MDPI artinfo: ui: 181939050 181939050 181939050 10.3390/rheumato4040014 181939050 ppf: 176 ppct: 17 formats: tig: atl: Feature Extraction and Identification of Rheumatoid Nodules Using Advanced Image Processing Techniques. aug: au: Mubeen, Azmath Dulhare, Uma N. affil: Department of Computer Science, University College for Women, Koti, Hyderabad 500095, India sug: subj: Rheumatoid Nodule Diagnosis Rheumatoid Nodule Classification Image Processing, Computer Assisted Methods Diagnostic Imaging Methods Diagnosis, Computer Assisted Sensitivity and Specificity Quality Improvement Human Diagnosis, Differential Rheumatoid Nodule Symptoms Rheumatoid Nodule Pathology Convolutional Neural Networks Evaluation Particle Swarm Optimization Precision Prediction Algorithms Detection Algorithms ab: Background/Objectives: Accurate detection and classification of nodules in medical images, particularly rheumatoid nodules, are critical due to the varying nature of these nodules, where their specific type is often unknown before analysis. This study addresses the challenges of multi-class prediction in nodule detection, with a specific focus on rheumatoid nodules, by employing a comprehensive approach to feature extraction and classification. We utilized a diverse dataset of nodules, including rheumatoid nodules sourced from the DermNet dataset and local rheumatologists. Method: This study integrates 62 features, combining traditional image characteristics with advanced graph-based features derived from a superpixel graph constructed through Delaunay triangulation. The key steps include image preprocessing with anisotropic diffusion and Retinex enhancement, superpixel segmentation using SLIC, and graph-based feature extraction. Texture analysis was performed using Gray-Level Co-occurrence Matrix (GLCM) metrics, while shape analysis was conducted with Fourier descriptors. Vascular pattern recognition, crucial for identifying rheumatoid nodules, was enhanced using the Frangi filter. A Hybrid CNN–Transformer model was employed for feature fusion, and feature selection and hyperparameter tuning were optimized using Gray Wolf Optimization (GWO) and Particle Swarm Optimization (PSO). Feature importance was assessed using SHAP values. Results: The proposed methodology achieved an accuracy of 85%, with a precision of 0.85, a recall of 0.89, and an F1 measure of 0.87, demonstrating the effectiveness of the approach in detecting and classifying rheumatoid nodules in both binary and multi-class classification scenarios. Conclusions: This study presents a robust tool for the detection and classification of nodules, particularly rheumatoid nodules, in medical imaging, offering significant potential for improving diagnostic accuracy and aiding in the early identification of rheumatoid conditions. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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