RETRACTED ARTICLE: A brain tumor image segmentation technique in image processing using ICA-LDA algorithm with ARHE model

Saravanan, S.; Karthigaivel, R.; Magudeeswaran, V.

doi:10.1007/s12652-020-01875-6

Cited by 20 publications

(9 citation statements)

References 14 publications

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“…The performance of any application can be valued by validating the chief metrics like sensitivity, accuracy, f ‐measure, precision, and execution time. Here, to know the improvement measure of the tumor segmentation system from the existing models, some comparisons were made with old schemes like Fuzzy k‐means model (FKM), 25 Optimized Laplacian (OL), 26 Component‐Analysis and Linear Discriminate Independent Model (CALDIM), 27 and Supervised & Un‐Supervised Learning (S‐USL) 28 …”

Section: Resultsmentioning

confidence: 99%

“…Moreover, the projected FFbU has utilized only 10 s as the maximum time to segment the Bt from the trained MRI. But the old methods like the Convolutional model 29 have required a maximum duration of 64 s for the segmentation process and CALDIM 27 has needed 17 s to complete the segmentation function. So, considering that the proposed FFbU technique has gained the best outstanding results within a short duration.…”

Section: Resultsmentioning

confidence: 99%

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An automated brain tumor segmentation framework using a novel fruit fly UNet

Boda¹,

Cherian

Kumar

2022

Concurrency and Computation

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Summary Brain image analysis and segmentation are the most difficult tasks in medical image processing because of image complexity. Moreover, MRI images are mostly utilized to predict different brain‐based diseases; if the images are complex, the disease prediction accuracy is very low. To overcome this problem, the current research has planned to design a novel fruit fly‐based UNet (FFbU) framework to detect the Tumor accurately. Moreover, the fitness of the fruit fly was upgraded in the UNet pooling module that has tended to gain the finest results. Initially, the standard datasets were gathered from the net source and trained to the system. Consequently, the training error is removed in the primary layer of FFbU then the error‐cleared data is entered into the UNet dense layer for tumor detection and segmentation. Finally, the proposed model is executed in a MATLAB environment, and the proficiency of the designed FFbU model is estimated in terms of accuracy, recall, precision, Dice, and Jaccard. In addition, the planned novel FFbU model has the ability to predict and segment different tumor types.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

An automated brain tumor segmentation framework using a novel fruit fly UNet

Boda¹,

Cherian

Kumar

2022

Concurrency and Computation

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“…When compared to traditional scores, ML-based models do not always improve performance in terms of accuracy [ 29 ]. However, several advantages may become apparent in the long term [ 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 ]. Specifically, compared to the static nature of traditional scores, the performance of the RAIN-ML prediction model is dynamic, thanks to its evolutive learning feature allowing the model to improve its classification algorithm by learning strategies at the increased enrollment time and number of recruited patients.…”

Section: Discussionmentioning

confidence: 99%

Prediction of All-Cause Mortality Following Percutaneous Coronary Intervention in Bifurcation Lesions Using Machine Learning Algorithms

Burrello

Gallone

Burrello

et al. 2022

JPM

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Stratifying prognosis following coronary bifurcation percutaneous coronary intervention (PCI) is an unmet clinical need that may be fulfilled through the adoption of machine learning (ML) algorithms to refine outcome predictions. We sought to develop an ML-based risk stratification model built on clinical, anatomical, and procedural features to predict all-cause mortality following contemporary bifurcation PCI. Multiple ML models to predict all-cause mortality were tested on a cohort of 2393 patients (training, n = 1795; internal validation, n = 598) undergoing bifurcation PCI with contemporary stents from the real-world RAIN registry. Twenty-five commonly available patient-/lesion-related features were selected to train ML models. The best model was validated in an external cohort of 1701 patients undergoing bifurcation PCI from the DUTCH PEERS and BIO-RESORT trial cohorts. At ROC curves, the AUC for the prediction of 2-year mortality was 0.79 (0.74–0.83) in the overall population, 0.74 (0.62–0.85) at internal validation and 0.71 (0.62–0.79) at external validation. Performance at risk ranking analysis, k-center cross-validation, and continual learning confirmed the generalizability of the models, also available as an online interface. The RAIN-ML prediction model represents the first tool combining clinical, anatomical, and procedural features to predict all-cause mortality among patients undergoing contemporary bifurcation PCI with reliable performance.

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“…Advanced image processing techniques like image filtering, image enhancement, morphological processing can improve the detection of periodontal diseases 12 . For the classification of medical images as abnormal and normal cases morphological segmentation plays the major role 13 …”

Section: Related Surveymentioning

confidence: 99%

Detection of periodontal bone loss in mandibular area from dental panoramic radiograph using image processing techniques

Vigil

Bharathi

2021

Concurrency and Computation

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Medical imaging plays the vital role in diagnosis of abnormalities. Periodontitis is a common chronic inflammatory disease damaging the soft tissue that untreated can lead to loss of bone, which supports the teeth. The severity of periodontitis is correlated to pocket depth in alveolar area. Even if analyses of the pocket depth can be manually performed, an automatic assistive tool can drastically help radiologists conduct more accurate analyses. In the proposed research, image processing algorithms were developed to compute pocket depth and diagnose the periodontitis. Radiologists manually segmented 350 panoramic radiographic images, dividing them into normal and periodontitis. The same dataset is used in our work to validate the Classification algorithm. The images are preprocessed with median filter and histogram equalization to improve the contrast and then segmented using two‐dimensional‐Otsu thresholding method into teeth and bony area in the mandibular region. Normal pocket depth of 3 mm as reported by American Academy of Periodontology is equivalently converted to pixel height in the radiographic images. Based on this pocket depth rule based classification method classifies the images into normal and periodontitis. The proposed work achieved 91.34% accuracy, 92.8% sensitivity, and 95.47% F‐score in classifying the dental panoramic radiography.

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RETRACTED ARTICLE: A brain tumor image segmentation technique in image processing using ICA-LDA algorithm with ARHE model

Cited by 20 publications

References 14 publications

An automated brain tumor segmentation framework using a novel fruit fly UNet

An automated brain tumor segmentation framework using a novel fruit fly UNet

Prediction of All-Cause Mortality Following Percutaneous Coronary Intervention in Bifurcation Lesions Using Machine Learning Algorithms

Detection of periodontal bone loss in mandibular area from dental panoramic radiograph using image processing techniques

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