An efficient ensemble method for detecting spinal curvature type using deep transfer learning and soft voting classifier

Tavana, Parisa; Akraminia, Mahdi; Koochari, Abbas; Bagherifard, Abolfazl

doi:10.1016/j.eswa.2022.119290

Cited by 12 publications

(3 citation statements)

References 36 publications

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“…The results on this dataset showed an impressive performance, with the highest mean accuracy reaching 96.73% and a maximum accuracy of 98.02%. P. Tavana et al [24] suggested an effective ensemble technique for identifying the type of spinal curve through the utilization of Deep Transfer Learning (DTL) and a Soft Voting Classifier (SVC). The scoliosis resulted in a C-or S-shaped deformity.…”

Section: A Analysis Of Scoliosis Detectionmentioning

confidence: 99%

“…Although the framework's main purpose is to discover and recognize vertebral centroids in intraoperative images, its adaptability enables it to expand its applications to estimate other anatomical postures in various preoperative and intraoperative imaging conditions. P. Tavana et al[15] suggested using DL techniques to classify different types of spinal curvatures based on radiography images. Support Vector Machines (SVM), k-nearest Neighbours (kNN), and pre-trained neural network models such as Xception and MobileNet V2 were used in this work.…”

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confidence: 99%

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A Critical Analysis on Vertebra Identification and Cobb Angle Estimation Using Deep Learning for Scoliosis Detection

Kumar,

Gupta,

Abraham

2024

IEEE Access

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Scoliosis is a complicated spinal deformity, and millions of people are suffering from this disease worldwide. Early detection and accurate scoliosis assessment are vital for effective clinical management and patient outcomes. The Cobb Angle (CA) measurement is the most precise method for calculating scoliotic curvature, which plays an essential role in diagnosing and treating scoliosis. This letter has conducted a systematic review to analyze scoliosis detection by vertebra identification and CA estimation using the Preferred Reporting Item for Systematic Review and Meta-Analysis (PRISMA) guidelines. The major scientific databases such as Scopus, Web of Science (WoS), and IEEE Xplorer are explored, where 2017-2023 publications are considered. The article selection process is based on keywords like "Vertebra Identification," "CA Estimation," "Scoliosis Detection," "Deep Learning (DL)," etc. After rigorous analysis, 413 articles are extracted, and 44 are identified for final consideration. Further, several investigations based on the previous work are discussed along with its Proposed Solutions (PS).

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Section: A Analysis Of Scoliosis Detectionmentioning

confidence: 99%

mentioning

confidence: 99%

A Critical Analysis on Vertebra Identification and Cobb Angle Estimation Using Deep Learning for Scoliosis Detection

Kumar,

Gupta,

Abraham

2024

IEEE Access

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“…However, several advanced techniques involving ensemble learning are evaluated, including voting and stacking. The voting weighting system enables two or three machine learning methods to complement each other [37,40]. A stacked model enables the combination of various machine learning algorithms under the supervision of the metamodel, ultimately achieving the highest attainable accuracy [41].…”

Section: Forwarder Productivity Modelingmentioning

confidence: 99%

Tactical Forwarder Planning: A Data-Driven Approach for Timber Forwarding

Munis,

Almeida,

Camargo

et al. 2023

Forests

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Tactical planning in timber harvesting involves aspects related to forest macro-planning and, particularly, the allocation of resources and sequencing of activities, all of which affect the allocation of timber in forest yards and roads and the productivity of forest machines. Data-driven approaches encourage the use of information obtained from data to enhance decision-making efficiency and support the development of short-term strategies. Therefore, our investigation was intended to determine whether a data-driven approach can generate sufficient input for modeling forwarder productivity in timber forwarding in Pinus and Eucalyptus planted forests, to support tactical planning. We utilized 3812 instances of raw data that were generated over a 36-month period. The data were collected from 23 loggers who operated in Pinus and Eucalyptus planted forests. We applied 22 regression algorithms that applied a supervised learning method from an experimental machine learning approach to the data instances. We evaluated the fitted models using three performance metrics. Out of the tested algorithms, the default mode of light gradient boosting produced a root mean squared error of 14.80 m3 h−1, a mean absolute error of 2.70, and a coefficient of determination of 0.77. Therefore, data-driven methods adequately support forwarder productivity modeling in timber forwarding in planted forests and help forest managers with tactical planning.

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Stacked ensemble learning for facial gender classification using deep learning based features extraction

waris,

Da,

Liu

2024

Cluster Comput

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An efficient ensemble method for detecting spinal curvature type using deep transfer learning and soft voting classifier

Cited by 12 publications

References 36 publications

A Critical Analysis on Vertebra Identification and Cobb Angle Estimation Using Deep Learning for Scoliosis Detection

A Critical Analysis on Vertebra Identification and Cobb Angle Estimation Using Deep Learning for Scoliosis Detection

Tactical Forwarder Planning: A Data-Driven Approach for Timber Forwarding

Stacked ensemble learning for facial gender classification using deep learning based features extraction

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