Feature extraction and classification of thorax x-ray image in the assessment of osteoporosis

Riandini,; Delimayanti, Mera Kartika

doi:10.1109/eecsi.2017.8239102

Cited by 8 publications

(7 citation statements)

References 9 publications

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“…Vertical root fracture [72,73] Deep learning Periapical pathosis [21], dental tumors [74], tooth numbering [75][76][77][78], tooth detection and identification [79][80][81], periodontal bone loss [32,82,83] Disease classification Classical image analysis approaches Tooth detection [84,85], osteoporosis assessment [86], dental caries [87] Machine learning Dental caries [88], proximal dental caries [14], molar and pre-molar teeth [89], osteoporosis [90], dental caries [15], periapical lesions [16,17], dental restorations [22], periapical roots [91], teeth with root [92], sagittal patterns [93] Deep learning Tooth numbering [94][95][96][97][98][99], dental implant stages [100], implant fixture [101], bone loss [18], periapical periodontitis [102][103][104][105], dental decay [106], approximal dental caries [19] Disease segmentation C...…”

Section: Disease Detection Machine Learningmentioning

confidence: 99%

“…The K-means classifier was employed for classification based on estimated parameters [ 85 ]. For osteoporosis assessment using thorax X-ray images, feature extraction was performed using GLCM followed by KNN [ 86 ]. Another early attempt evaluated two machine-learning algorithms; support vector machine (SVM) and K nearest neighbors (KNN) were used for dental caries classification based on features extracted using the GLCM algorithm [ 87 ].…”

Section: Approaches To Dental Disease Diagnosis Using X-ray Imagingmentioning

confidence: 99%

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A Comprehensive Review of Recent Advances in Artificial Intelligence for Dentistry E-Health

et al. 2023

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Artificial intelligence has made substantial progress in medicine. Automated dental imaging interpretation is one of the most prolific areas of research using AI. X-ray and infrared imaging systems have enabled dental clinicians to identify dental diseases since the 1950s. However, the manual process of dental disease assessment is tedious and error-prone when diagnosed by inexperienced dentists. Thus, researchers have employed different advanced computer vision techniques, and machine- and deep-learning models for dental disease diagnoses using X-ray and near-infrared imagery. Despite the notable development of AI in dentistry, certain factors affect the performance of the proposed approaches, including limited data availability, imbalanced classes, and lack of transparency and interpretability. Hence, it is of utmost importance for the research community to formulate suitable approaches, considering the existing challenges and leveraging findings from the existing studies. Based on an extensive literature review, this survey provides a brief overview of X-ray and near-infrared imaging systems. Additionally, a comprehensive insight into challenges faced by researchers in the dental domain has been brought forth in this survey. The article further offers an amalgamative assessment of both performances and methods evaluated on public benchmarks and concludes with ethical considerations and future research avenues.

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Section: Disease Detection Machine Learningmentioning

confidence: 99%

Section: Approaches To Dental Disease Diagnosis Using X-ray Imagingmentioning

confidence: 99%

A Comprehensive Review of Recent Advances in Artificial Intelligence for Dentistry E-Health

et al. 2023

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“…To obtain greater performance HOG and LPQ methods were also used. This technique used back-based optimization to carefully select features based on Fusion at the score and decision level of the model which achieved an overall accuracy of 89.66% Delimayanti et al [7] explained Previous research has shown that it is possible to predict or detect osteoporosis condition by evaluating the thickness of the clavicle cortex as observed in thorax X-ray images, but this approach is limited by its dependence on subjective visual assessments and the resolution of the X-ray images. So, to address these issues, this paper explores the use of algorithms based on image processing to classify Employing Gray Level Co-occurrence Matrix (GLCM) to analyse X-ray images, and K-Nearest Neighbour (KNN) as feature extraction techniques.…”

Section: Literature Reviewmentioning

confidence: 99%

Transfer Learning-Based Osteoporosis Classification Using Simple Radiographs

Dodamani¹,

Danti²

2023

Int. J. Onl. Eng.

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Osteoporosis is a condition that affects the entire skeletal system, resulting in decreased density of bone mass and the weakening of bone tissue's micro-architecture. This leads to weaker bones that are more susceptible to fractures. Detecting and measuring bone mineral density has always been a critical area of focus for researchers in the diagnosis of bone diseases such as osteoporosis. However, existing algorithms used for osteoporosis diagnosis encounter challenges in obtaining accurate results due to X-ray image noise and variations in bone shapes, especially in low contrast conditions. Therefore, the development of efficient algorithms that can mitigate these challenges and improve the accuracy of osteoporosis diagnosis is essential. In this research paper, a comparative analysis was conducted Assessing the accuracy and efficiency of the latest deep learning CNN model, such as VGG16, VGG19, DenseNet121, Resnet50 and InceptionV3 in detecting to Classify Normal and Osteoporosis cases. The study employed 830 X-ray images of Spine, Hand, Leg, Knee, and Hip, comprising of Normal (420) and Osteoporosis (410) cases. Various performance metrics were utilized to evaluate each model, and the findings indicate that DenseNet121 exhibited superior performance with an accuracy rate of 93.4% with Achieving an error rate of 0.07 and a validation loss of only 0.57 in comparison with other models considered in this study.

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“…A research done by [8] on thorax x-ray image using k-nearest neighbour (KNN) and GLCM feature extraction methods shows an accuracy of 97.83% for 46 images. While for nodule detection image classification done by [4] shows that support vector machine (SVM) trained with features managed to reduce high number of false positive.…”

Section: Fig 1 -Chest X-ray Image With Thorax Diseases [6]mentioning

confidence: 99%

Chest X-Ray Image Classification on Common Thorax Diseases using GLCM and AlexNet Deep Features

Akmal¹,

Than²,

Abdullah³

et al. 2019

IJIE

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Image processing in medical has been progressing far than it ever did when it's one of the main techniques used in the biomedical imaging system and computer aided diagnosis systems. Few of the well-known medical imaging modalities are Magnetic Resonance Imaging (MRI), Computed Tomography (CT) Scan, X-Ray and Ultrasound. The output from these imaging modalities would later be reviewed by expert for an accurate result. Computer aided diagnosis not only save time as it can process thousand images just in a few minutes, it can also be used as tool to aid the expert as a second opinion, which in turn could save more lives. Thorax consist of several organs which include heart, lungs, thymus gland, muscles and other various internal structures such as diaphragm. Thorax disease refer to diseases that affect the chest and the most common symptom is the chest pain. Some of the common thoracic disease are atelectasis, pneumothorax, pneumonia, nodule, mass, infiltration, effusion and cardiomegaly. Figure 1 shows the thorax diseases observed in chest X-ray [1]. Correct diagnosis and disease treatment are important especially during the early stage, which is the most difficult part of the process due to similarity in appearance.

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Feature extraction and classification of thorax x-ray image in the assessment of osteoporosis

Cited by 8 publications

References 9 publications

A Comprehensive Review of Recent Advances in Artificial Intelligence for Dentistry E-Health

A Comprehensive Review of Recent Advances in Artificial Intelligence for Dentistry E-Health

Transfer Learning-Based Osteoporosis Classification Using Simple Radiographs

Chest X-Ray Image Classification on Common Thorax Diseases using GLCM and AlexNet Deep Features

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