Support Vector Machine Multiclass using Polynomial Kernel for Osteoporosis Detection

Adyanti, Deasy Alfiah; Novitasari, Dian Candra Rini; Fanani, Aris

doi:10.5220/0008522303840390

Cited by 1 publication

(4 citation statements)

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“…In addition, the method we proposed reached a testing accuracy of 90.48% for osteoporosis and nonosteoporosis classes. This performance is better than the previous studies [ 4 , 5 , 29 , 31 ] but slightly worse than previous works [ 10 , 22 ].…”

Section: Discussioncontrasting

confidence: 78%

“…Due to these issues, conventional segmentation methods for osteoporosis detection are ineffective. Several studies on osteoporosis detection using image segmentation reported higher accuracy [ 10 , 28 ] than studies without segmentation [ 4 , 5 ].…”

Section: Discussionmentioning

confidence: 99%

“…Several studies have been conducted to obtain important features for detecting osteoporosis using image segmentation on dental radiographs combined with ML. Using support vector machine (SVM) multiclass, a previous study [ 4 ] classified data into normal bone, osteopenia, and osteoporosis. Categorization was established through an examination of alterations in the trabecular pattern of the mandible bone.…”

Section: Discussionmentioning

confidence: 99%

“…Another thriving application that utilizes dental radiographic images is osteoporosis detection [ 1 , 2 ]. Osteoporosis is a metabolic bone disorder characterized by the loss of cortical thickness and the reduced number and size of trabeculae [ 3 , 4 ]. Risk factors that can cause osteoporosis include age, gender, and life activities.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Automatic Segmentation of Periapical Radiograph Using Color Histogram and Machine Learning for Osteoporosis Detection

Widyaningrum

Sela

Pulungan

et al. 2023

International Journal of Dentistry

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Osteoporosis leads to the loss of cortical thickness, a decrease in bone mineral density (BMD), deterioration in the size of trabeculae, and an increased risk of fractures. Changes in trabecular bone due to osteoporosis can be observed on periapical radiographs, which are widely used in dental practice. This study proposes an automatic trabecular bone segmentation method for detecting osteoporosis using a color histogram and machine learning (ML), based on 120 regions of interest (ROI) on periapical radiographs, and divided into 60 training and 42 testing datasets. The diagnosis of osteoporosis is based on BMD as evaluated by dual X-ray absorptiometry. The proposed method comprises five stages: the obtaining of ROI images, conversion to grayscale, color histogram segmentation, extraction of pixel distribution, and performance evaluation of the ML classifier. For trabecular bone segmentation, we compare K-means and Fuzzy C-means. The distribution of pixels obtained from the K-means and Fuzzy C-means segmentation was used to detect osteoporosis using three ML methods: decision tree, naive Bayes, and multilayer perceptron. The testing dataset was used to obtain the results in this study. Based on the performance evaluation of the K-means and Fuzzy C-means segmentation methods combined with 3 ML, the osteoporosis detection method with the best diagnostic performance was K-means segmentation combined with a multilayer perceptron classifier, with accuracy, specificity, and sensitivity of 90.48%, 90.90%, and 90.00%, respectively. The high accuracy of this study indicates that the proposed method provides a significant contribution to the detection of osteoporosis in the field of medical and dental image analysis.

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