Glioma brain tumor detection and segmentation using weighting random forest classifier with optimized ant colony features

Rajagopal, R.

doi:10.1002/ima.22331

Cited by 38 publications

(20 citation statements)

References 9 publications

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“…The EL-based methods, however, have natural advantages 9 when dealing with multi-classification problems with high-dimensional features. [9][10][11][12] The typical ELbased algorithms, such as XGBoost 12 and random forest (RF), 13 show the performance that is very dependent on the quality of selected feature combination. 14 Basic features used in medical images are radiomics features.…”

Section: Introductionmentioning

confidence: 99%

Withdrawn: A hybrid feature selection‐based approach for brain tumor detection and automatic segmentation on multiparametric magnetic resonance images

Chen

Ban

et al. 2021

Medical Physics

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Purpose To develop a novel method based on feature selection, combining convolutional neural network (CNN) and ensemble learning (EL), to achieve high accuracy and efficiency of glioma detection and segmentation using multiparametric MRIs. Methods We proposed an evolutionary feature selection‐based hybrid approach for glioma detection and segmentation on 4 MR sequences (T2‐FLAIR, T1, T1Gd, and T2). First, we trained a lightweight CNN to detect glioma and mask the suspected region to process large batch of MRI images. Second, we employed a differential evolution algorithm to search a feature space, which composed of 416‐dimensions radiomics features extracted from four sequences of MRIs and 128‐dimensions high‐order features extracted by the CNN, to generate an optimal feature combination for pixel classification. Finally, we trained an EL classifier using the optimal feature combination to segment whole tumor (WT) and its subregions including non‐enhancing tumor (NET), peritumoral edema (ED), and enhancing tumor (ET) in the suspected region. Experiments were carried out on 300 glioma patients from the BraTS2019 dataset using fivefold cross‐validation, and the model was independently validated using the rest 35 patients from the same database. Results The approach achieved a detection accuracy of 98.8% using four MRIs. The Dice coefficients (and standard deviations) were 0.852 ± 0.057, 0.844 ± 0.046, and 0.799 ± 0.053 for segmentation of WT (NET+ET+ED), tumor core (NET+ET), and ET, respectively. The sensitivities and specificities were 0.873 ± 0.074, 0.863 ± 0.072, and 0.852 ± 0.082; the specificities were 0.994 ± 0.005, 0.994 ± 0.005, and 0.995 ± 0.004 for the WT, tumor core, and ET, respectively. The performances and calculation times were compared with the state‐of‐the‐art approaches, our approach yielded a better overall performance with average processing time of 139.5 s per set of four sequence MRIs. Conclusions We demonstrated a robust and computational cost‐effective hybrid segmentation approach for glioma and its subregions on multi‐sequence MR images. The proposed approach can be used for automated target delineation for glioma patients.

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Section: Introductionmentioning

confidence: 99%

Withdrawn: A hybrid feature selection‐based approach for brain tumor detection and automatic segmentation on multiparametric magnetic resonance images

Chen

Ban

et al. 2021

Medical Physics

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“…27 A weighted random forest classifier was used in a well-known work to detect gliomas. 28 The methods were able to achieve state-of-the-art performance in their respective datasets. A more challenging problem is the classification based on tumour subtypes and tumour grades.…”

Section: Related Workmentioning

confidence: 98%

Brain tumour classification using siamese neural network and neighbourhood analysis in embedded feature space

Deepak

Ameer

2021

Int J Imaging Syst Tech

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The application of deep transfer learning techniques has been successful in developing accurate systems for brain tumour classification on large-scale medical image databases. For small databases, feature learning by deep neural networks is not robust. The systems based on domain-specific hand-crafted features have limited accuracy. In this paper, the authors focus on developing accurate models that could be trained effectively using a smaller number of data samples. A siamese neural network (SNN) is designed to extract features from brain magnetic resonance imaging (MRI) images. The SNN is realised using a 3-layer, fully connected neural network. The designed SNN has lesser complexity and fewer parameters than deep transfer-learned convolutional neural networks (CNN). A nearest neighbourhood analysis, using Euclidean and Mahalanobis distances, is conducted on the SNN encoded feature space.The encoded feature space is two dimensional, such that the neighbourhood analysis is computationally less intensive. For the neighbourhood analysis, a k-nearest neighbour (k-NN) model is utilised. The proposed method is evaluated using three publicly available datasets, namely, Radiopaedia, Harvard and Figshare repositories. The respective classification accuracy on cross-validation is 92.6%, 98.5% and 92.6%. Other metrics used for the performance evaluation include F-score, Specificity and balanced accuracy. The underlying network architecture and the design choice of network layers allow the implementation of the SNN in environments with low computational resources. The SNN features are found to be more effective than the hand-designed features, and the deep transfer learned features for the stated problem.

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“…The forest corresponds to a collection of trees where the classification result is decided whether by weighted voting or majority voting. The RF is widely used in solving classification problems, 56,[66][67][68][69] particularly for classifying DR retinal images. 70,71 The RF has four main parameters 72 which are the number of trees in the forest, the maximum depth of the tree, the number of features considered for splitting a node and the size of the training set.…”

Section: Random Forest (Rf)mentioning

confidence: 99%

Mobile‐aided screening system for proliferative diabetic retinopathy

Boukadida

Elloumi

Akil

et al. 2021

Int J Imaging Syst Tech

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NeoVascularization (NV) occurs in the Proliferative Diabetic Retinopathy (PDR) stage, where the development progress of new vessels presents a high risk for severe vision loss and blindness. Therefore, early NV detection is primordial to preserve patient's vision. Several automated methods have been proposed to detect the NV on retinograph-captured fundus images. However, their employment is constrained by the reduced ophthalmologist-per-person ratio and the expensive equipment for image capturing. This paper presents a novel method for NV detection in smartphone-captured fundus images. The implementation of the method on a smartphone having an optical lens for fundus capturing leads to a Mobiles-Aided-Screening system of PDR (MAS-PDR). The challenge is to ensure accurate and robust detection even with moderate quality of fundus image, on reduced execution time.Within this objective, we identify the major criteria of neovascularized vessels which are tortuosity, width, bifurcation, and density. Our main contribution consists in proposing a sharp feature to reflect each criterion on reduced computational complexity processing. Therefore, the features are provided to a random forest classifier to deduce the PDR stage.A dataset raised from publicly databases is used on a 10-cross validation process where 98.69% accuracy, 97.73% sensitivity, and 99.12% specificity are achieved. To evaluate the robustness, the same experimentation is repeated after applying motion blur filters to the fundus image dataset, where 98.91% accuracy, 96.75% sensitivity, and 100% specificity are deduced. Moreover, NV screening is performed under 3 s when executed in smartphone devices demonstrating the appropriateness of our method to MAS-PDR.

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Glioma brain tumor detection and segmentation using weighting random forest classifier with optimized ant colony features

Cited by 38 publications

References 9 publications

Withdrawn: A hybrid feature selection‐based approach for brain tumor detection and automatic segmentation on multiparametric magnetic resonance images

Withdrawn: A hybrid feature selection‐based approach for brain tumor detection and automatic segmentation on multiparametric magnetic resonance images

Brain tumour classification using siamese neural network and neighbourhood analysis in embedded feature space

Mobile‐aided screening system for proliferative diabetic retinopathy

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