Automatic Detection and classification of Scoliosis from Spine X-rays using Transfer Learning

Amin, Arslan; Abbas, Moneeb; Salam, Anum Abdul

doi:10.1109/icodt255437.2022.9787480

Cited by 8 publications

(7 citation statements)

References 25 publications

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“…On the other hand, this study achieved positive results by using an interpretable and explainable, trustworthy CapsNet model. It is critical that the used CapsNet model provided better results than recent models of [74,75,77,[78][79][80][81][82]. • This study provided a general decision support framework by building a hybrid approach with CapsNet and Fuzzy Logic.…”

Section: Discussionmentioning

confidence: 99%

“…In this way, it was aimed to determine the state of the CapsNet model against some competitors from the literature. The CapsNet model was compared with a total of ten models from the literature: ConvNet [74], BoostNet [74], ResNet-50 [75], EfficientNet-b7 [77], U-Net [78], CNN [79], ESNN [80], DCNN [81], ResNet-18 [82], and DenseNet-201 [82]. These models were chosen because of their effectiveness and timeliness.…”

Section: B Comparative Evaluation For Measurementmentioning

confidence: 99%

“…These models were chosen because of their effectiveness and timeliness. In the evaluation flow, all competitive models were built with their reported designs (including even feature extraction methods if existing) in the associated studies [74,75,[77][78][79][80][81][82]. Although some models were used for different problems in their original studies, their reported designs were adapted to the problem / dataset in this study.…”

Section: B Comparative Evaluation For Measurementmentioning

confidence: 99%

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Development of a CapsNet and Fuzzy Logic Decision Support System for Diagnosing the Scoliosis and Planning Treatments via Schroth Method

Goral

Köse

2022

IEEE Access

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Scoliosis is a disease caused by the spine curving. It is treatable but physiotherapists may do different measurements for curvature angles. That's a problem affecting the treatment planning. This study aims to develop a Deep Learning-based decision support system, which diagnoses scoliosis and plans treatments via Schroth method. The system has an interpretable and explainable CapsNet model processing x-ray image to detect 68-point vertebrae and make Cobb angle measurements. By using angle values and patient parameters, treatment is planned through an automated Schroth definition and the Fuzzy Logic. In the evaluations, the CapsNet had dominating findings (some of them are MSE: 0.0038, PCC: 0.93, Accuracy: 0.98). The Fuzzy Logic model was accurate at exercise plans for past cases. Also, physiotherapists and patients had positive feedback for the system usage, trustworthiness, diagnosis, treatment planning and tracking. As a conclusion, the system ensures advancements for automated diagnosis and treatment of scoliosis.

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Section: Discussionmentioning

confidence: 99%

Section: B Comparative Evaluation For Measurementmentioning

confidence: 99%

Section: B Comparative Evaluation For Measurementmentioning

confidence: 99%

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Development of a CapsNet and Fuzzy Logic Decision Support System for Diagnosing the Scoliosis and Planning Treatments via Schroth Method

Goral

Köse

2022

IEEE Access

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“…When it comes to scoliosis identification, deep learning algorithms provide a faster and more effective solution than manual X-ray investigation. Arslan Amin et al used a pre-trained EfficientNet model to achieve an accuracy of 86 % on the detection and classification of scoliosis from X-ray images [19]. Besides, Ariana Alejandra Andrews Interiano et al take a database of medical images from Honduran to transfer learning and fine-tuning in InceptionResNet, MobileNet, and EfficientNet.…”

Section: Related Workmentioning

confidence: 99%

An Approach to Classifying X-Ray Images of Scoliosis and Spondylolisthesis Based on Fine-Tuned Xception Model

Lu,

Nguyen

2024

IJACSA

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The vertebral column is a marvel of biological engineering and it considers a main part of the skeleton in vertebrate animals. In addition, it serves as the central axis of the human body comprising a series of interlocking vertebrae that provide structural support and flexibility. From basic works like bending and twisting to more complex actions such as walking and running, the spine's impact on human life is profound, underscoring its indispensable role in maintaining physical wellbeing and overall functionality. Moreover, in the hard-working schedule of people in modern life, a bunch of diseases impact on vertebral column such as spondylolisthesis and scoliosis. As a result, numerous researches were provided to take a hand in solving or avoiding these illnesses including machine learning. In this study, transfer learning and fine tuning were used for the classification of X-ray images on vertebrae sickness to avoid complex and wasted time in a medical examination process. The dataset for vertebrae illnesses X-ray images was collected at King

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“…Other symptoms are an uneven waist, and one side of the rib cage jutting forward. Based on the shape of the curve, scoliosis can be classified into two types, namely the C-curve and the S-curve [3]. Fig.…”

mentioning

confidence: 99%

Integrated Approach for Cobb Angle Estimation in X-Ray C-Curve Scoliosis Using Image Processing and Inverse Cosine Law

Mohd Nasir,

Jumadi,

Mun

2023

JEVA

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Cobb angle is the angle subtended by the most tilted vertebrae. It represents the degree of the spinal curvature and has been used to diagnose scoliosis. The severity of the scoliosis based on the cobb angle will determine the guide treatment and surgical planning. Manual measurement of Cobb angle using protractor or semi-manual using ONIS software is quite time consuming, and it is subjected to variations in interobserver and intraobserver measurements. This research proposed an algorithm to determine the Cobb angle using image processing and inverse cosine methods. The raw samples of X-ray images were obtained from Pusat Kesihatan Universiti (PKU), UTHM. Several interviews were conducted with radiologist at Pantai Hospital for verification purpose. Gaussian blur and unsharp mask were initially applied for noise removal in the preprocessing step. Three points were marked at the vertebrae image to assist for centroid estimation using Haar Cascade. The Cobb angle was estimated by applying the triangle formula derived from the inverse cosine law. In comparison to semi-manual method (Onis Software), the proposed technique has an error of less than 5 % and computes the Cobb angle 3.28 times quicker. In the future, it is suggested to explore other techniques such as deep learning for alternative data analysis for Cobb angle estimation.

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Automatic Detection and classification of Scoliosis from Spine X-rays using Transfer Learning

Cited by 8 publications

References 25 publications

Development of a CapsNet and Fuzzy Logic Decision Support System for Diagnosing the Scoliosis and Planning Treatments via Schroth Method

Development of a CapsNet and Fuzzy Logic Decision Support System for Diagnosing the Scoliosis and Planning Treatments via Schroth Method

An Approach to Classifying X-Ray Images of Scoliosis and Spondylolisthesis Based on Fine-Tuned Xception Model

Integrated Approach for Cobb Angle Estimation in X-Ray C-Curve Scoliosis Using Image Processing and Inverse Cosine Law

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