Classification of vertebral column disorders and lumbar discs disease using attribute weighting algorithm with mean shift clustering

Ünal, Yavuz; Polat, Kemal; Koçer, Hasan Erdinç

doi:10.1016/j.measurement.2015.09.013

Cited by 14 publications

(2 citation statements)

References 24 publications

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“…The pre-processing mechanism also does not include validation and normalization. Thus, the calculation complexity is high [35][36][37][38][39]. Therefore, there is a strong motivation to perform this research.…”

Section: Problem Identification and Research Motivationmentioning

confidence: 99%

Lumbar Spine Disease Detection: Enhanced CNN Model With Improved Classification Accuracy

Ruchi,

Singh,

Singla

et al. 2023

IEEE Access

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Back pain is an issue affecting millions of people throughout the world. Research on back pain root cause detection is immense. The lumbar spine is a lower back region of the backbone that could also be responsible for back pain. Research on issues related to lumbar spine disease (LSD) is limited. The lumbar spine is critical for the human body as it supports the weight of the body. Many diseases can affect the lumbar spine adversely. This research is directed towards the detection of LSDs using optimized feature extraction and selection phases. Furthermore, the linearity-based model is used for feature selection, selecting only the best possible features to reduce the missed classification degree. The flow of the proposed research work is divided into phases. In the first phase, data collection is performed. Data collection in the proposed work includes both real-time and benchmark magnetic resonance imaging (MRI) datasets. The MRI dataset collected from the hospital is validated by medical experts. After data collection, pre-processing is applied. This step removes the noise from the collected dataset. The pre-processing mechanism is performed using histogram equalization, median filtering, validation, and normalization. After the pre-processing phase, background subtraction and region of interest (ROI) detection are performed using the region-cut mechanism. Optimal feature extraction is achieved using a differential spider monkey optimization (SMO), and feature selection is performed using a linearity-based convolutional neural network (CNN) model. In the end, ensemble-based classification is used for disease prediction. The validation of the result is conducted through classification accuracy, specificity, sensitivity, and F-Score. The high classification accuracy of 96% is achieved with multi support vector machine (MSVM), 94% with random forest (RF), 93.5% with a decision tree (DT), and 91% with the Naïve Bayes (NB) approach, proving the validity of the proposed approach. INDEX TERMS Lumbar Spine, SMO, Linearity model, ROI, MRI dataset I. INTRODUCTIONThe lumbar spine is at the lower end of the spinal cord and is required for balancing the weight of the human body, as discussed in [1]. The spinal cord area is shielded by five durable and flexible vertebrae that ensure the dispersion of axial forces, as discussed in [2]. The spinal cord goes through the vertebrae and terminates at the level of the L1 and L2 vertebrae. The cauda equina begins at the termination of the spinal cord and descends through the remainder of the canal, as discussed in [3]. The lumbar spine is made up of bones, cartilage, nerves, and muscles. Each of these components This article has been accepted for publication in IEEE Access.

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Section: Problem Identification and Research Motivationmentioning

confidence: 99%

Lumbar Spine Disease Detection: Enhanced CNN Model With Improved Classification Accuracy

Ruchi,

Singh,

Singla

et al. 2023

IEEE Access

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“…For example, the study (Unal, Polat & Kocer, 2014) uses a combination of pairwise fuzzy C-means based feature weighting and SVM to classify samples into three classes of hernia, normal, and spondylolisthesis and achieves 96.4% accuracy. Similarly, the authors classified samples into normal and abnormal classes for vertebral column disorder in Unal, Polat & Kocer (2016) and achieved an accuracy of 99.3%. The classification has been done using the combination of MSCBAW (mean shift clustering-based attribute weighting) and RBDNN (radial basis function-neural network) in the study.…”

Section: Performance Comparison With Other Approachesmentioning

confidence: 99%

Diagnosis of vertebral column pathologies using concatenated resampling with machine learning algorithms

Reshi

Ashraf

Rustam

et al. 2021

PeerJ Computer Science

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Medical diagnosis through the classification of biomedical attributes is one of the exponentially growing fields in bioinformatics. Although a large number of approaches have been presented in the past, wide use and superior performance of the machine learning (ML) methods in medical diagnosis necessitates significant consideration for automatic diagnostic methods. This study proposes a novel approach called concatenated resampling (CR) to increase the efficacy of traditional ML algorithms. The performance is analyzed leveraging four ML approaches like tree-based ensemble approaches, and linear machine learning approach for automatic diagnosis of inter-vertebral pathologies with increased. Besides, undersampling, over-sampling, and proposed CR techniques have been applied to unbalanced training dataset to analyze the impact of these techniques on the accuracy of each of the classification model. Extensive experiments have been conducted to make comparisons among different classification models using several metrics including accuracy, precision, recall, and F1 score. Comparative analysis has been performed on the experimental results to identify the best performing classifier along with the application of the re-sampling technique. The results show that the extra tree classifier achieves an accuracy of 0.99 in association with the proposed CR technique.

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Comparative Study of Pattern Recognition Techniques in the Classification of Vertebral Column Diseases

Rojas-López,

Uriarte-Arcia,

Rodríguez-Molina

et al. 2023

Communications in Computer and Information Science

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Classification of vertebral column disorders and lumbar discs disease using attribute weighting algorithm with mean shift clustering

Cited by 14 publications

References 24 publications

Lumbar Spine Disease Detection: Enhanced CNN Model With Improved Classification Accuracy

Lumbar Spine Disease Detection: Enhanced CNN Model With Improved Classification Accuracy

Diagnosis of vertebral column pathologies using concatenated resampling with machine learning algorithms

Comparative Study of Pattern Recognition Techniques in the Classification of Vertebral Column Diseases

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