Design of Web Ranking Module using Genetic Algorithm

Thada, Vikas; Jaglan, Vivek

doi:10.5120/17038-7346

Cited by 9 publications

(11 citation statements)

References 5 publications

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“…In contrast, a per-lesion IoU not surpassing 0.01 is regarded as a per-lesion detection negative. Within the framework of performance evaluation on the Validation only dataset, the derived metrics were Sensitivity of 0.83, FP rate of 3.4, and Dice Similarity Coefficient (DSC) [8] of 0.66.…”

Section: Experiments Resultsmentioning

confidence: 99%

A Novel Landmark-Based Semi-Supervised Deep Learning Method for Aneurysm Detection Using 3D TOF-MRA

Yang,

Kim,

Rieu

et al. 2024

Preprint

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Section: Experiments Resultsmentioning

confidence: 99%

A Novel Landmark-Based Semi-Supervised Deep Learning Method for Aneurysm Detection Using 3D TOF-MRA

Yang,

Kim,

Rieu

et al. 2024

Preprint

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“…The coefficient ranged from 0 to 1, with a value of 0 indicating no overlap, and a value of 1 indicating perfect agreement between the segmentations. Typically, a dice coefficient above 0.7 is considered acceptable for medical image segmentation tasks ( 20 ). Once the segmentation was completed, the acromion and glenoid contours were automatically localized as the center of the images and cropped to 255×255 pixels for further classification.…”

Section: Methodsmentioning

confidence: 99%

A two-step neural network-based guiding system for obtaining reliable radiographs for critical shoulder angle measurement

Alike,

Li,

Hou

et al. 2024

Quant Imaging Med Surg

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Background The critical shoulder angle (CSA) has been reported to be highly associated with rotator cuff tears (RCTs) and an increased risk of RCT re-tears. However, the measurement of the CSA is greatly affected by the malpositioning of the shoulder. To address this issue, a two-step neural network-based guiding system was developed to obtain reliable CSA radiographs, and its feasibility and accuracy was evaluated. Methods A total of 1,754 shoulder anteroposterior (AP) radiographs were retrospectively acquired to train and validate a two-step neural network-based guiding system to obtain reliable CSA radiographs. The study included patients aged 18 years or older who underwent X-rays and/or computed tomography (CT) scans of the shoulder. Patients who had undergone shoulder surgery, had a confirmed fracture, or were diagnosed with a musculoskeletal tumor or glenoid defect were excluded from the study. The system consisted of a two-step neural network that in the first step, localized the region of interest of the shoulder, and in the second step, classified the radiography according to type [i.e., ‘forward’ when the non-overlapping coracoid process is above the glenoid rim, ‘backward’ when the non-overlapping coracoid process is below or aligned with the glenoid rim, a ratio of the transverse to longitudinal diameter of the glenoid projection (RTL) ≤0.25, or a RTL >0.25]. The performance of the model was assessed in an offline, prospective manner, focusing on the sensitivity and specificity for the forward, backward, RTL ≤0.25, or RTL >0.25 types (denoted as Sens F, B, −, + and Spec F, B, −, + , respectively), and Cohen’s kappa was also reported. Results Of 273 cases in the offline prospective test, the Sens F , Sens B , Sens − , and Sens + were 88.88% [95% confidence interval (CI): 50.67–99.41%], 94.11% (95% CI: 82.77–98.47%), 96.96% (95% CI: 91.94–99.02%), and 95.06% (95% CI: 87.15–98.40%), respectively. The Spec F , Spec B , Spec − , and Spec + were 98.48% (95% CI: 95.90–99.51%), 99.55% (95% CI: 97.12–99.97%), 95.04% (95% CI: 89.65–97.81%), and 97.39% (93.69–99.03%), respectively. A high classification rate (93.41%; 95% CI: 89.14–96.24%) and almost perfect agreement (Cohen’s kappa: 0.903, 95% CI: 0.86–0.95) were achieved. Conclusions The guiding system can rapidly and accurately classify the types of AP shoulder radiography, thereby guiding the adjustment of patient positioning. This will facilitate the rapid obtainment of reliable CSA radiography to measure the CSA on proper AP radiographs.

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“…The model evaluation process consists of two stages: the first involves comparison with another established segmentation method in the literature, as illustrated in the green block of Fig 3 , using the Dice Sørensen coefficient D c , a similarity measure commonly used in segmentation assessments [ 93 – 95 ]. The D c provides a quantitative assessment of the quality of the segmentation generated by the proposed method in comparison to the existing approach.…”

Section: Foundation and Criteria Of The Methodologymentioning

confidence: 99%

Unsupervised model for structure segmentation applied to brain computed tomography

dos Santos,

Scoczynski Ribeiro Martins,

Amorim Nogueira

et al. 2024

PLoS ONE

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This article presents an unsupervised method for segmenting brain computed tomography scans. The proposed methodology involves image feature extraction and application of similarity and continuity constraints to generate segmentation maps of the anatomical head structures. Specifically designed for real-world datasets, this approach applies a spatial continuity scoring function tailored to the desired number of structures. The primary objective is to assist medical experts in diagnosis by identifying regions with specific abnormalities. Results indicate a simplified and accessible solution, reducing computational effort, training time, and financial costs. Moreover, the method presents potential for expediting the interpretation of abnormal scans, thereby impacting clinical practice. This proposed approach might serve as a practical tool for segmenting brain computed tomography scans, and make a significant contribution to the analysis of medical images in both research and clinical settings.

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Design of Web Ranking Module using Genetic Algorithm

Cited by 9 publications

References 5 publications

A Novel Landmark-Based Semi-Supervised Deep Learning Method for Aneurysm Detection Using 3D TOF-MRA

A Novel Landmark-Based Semi-Supervised Deep Learning Method for Aneurysm Detection Using 3D TOF-MRA

A two-step neural network-based guiding system for obtaining reliable radiographs for critical shoulder angle measurement

Unsupervised model for structure segmentation applied to brain computed tomography

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