Modified support vector machines for MR brain images recognition

Ladgham, Anis; Torkhani, Ghada; Sakly, Anis; Mtibaa, Abdellatif

doi:10.1109/codit.2013.6689515

Cited by 16 publications

(9 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These approaches exploit a variety of well-established engines, including decision forests [12], conditional random fields [45], and support vector machines [18].…”

Section: Brain Tumor Segmentationmentioning

confidence: 99%

Fully-automated deep learning-powered system for DCE-MRI analysis of brain tumors

Nalepa

Lorenzo

Marcinkiewicz³

et al. 2020

Artificial Intelligence in Medicine

View full text Add to dashboard Cite

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays an important role in diagnosis and grading of brain tumor. Although manual DCE biomarker extraction algorithms boost the diagnostic yield of DCE-MRI by providing quantitative information on tumor prognosis and prediction, they are time-consuming and prone to human error. In this paper, we propose a fullyautomated, end-to-end system for DCE-MRI analysis of brain tumors. Our deep learning-powered technique does not require any user interaction, it yields reproducible results, and it is rigorously validated against benchmark (BraTS'17 for tumor segmentation, and a test dataset released by the Quantitative Imaging * Corresponding author Biomarkers Alliance for the contrast-concentration fitting) and clinical (44 lowgrade glioma patients) data. Also, we introduce a cubic model of the vascular input function used for pharmacokinetic modeling which significantly decreases the fitting error when compared with the state of the art, alongside a realtime algorithm for determination of the vascular input region. An extensive experimental study, backed up with statistical tests, showed that our system delivers state-of-the-art results (in terms of segmentation accuracy and contrastconcentration fitting) while requiring less than 3 minutes to process an entire input DCE-MRI study using a single GPU.

show abstract

“…These approaches exploit a variety of well-established engines, including decision forests [12], conditional random fields [45], and support vector machines [18].…”

Section: Brain Tumor Segmentationmentioning

confidence: 99%

Fully-automated deep learning-powered system for DCE-MRI analysis of brain tumors

Nalepa

Lorenzo

Marcinkiewicz³

et al. 2020

Artificial Intelligence in Medicine

View full text Add to dashboard Cite

show abstract

“…With its excellent generalization ability, support vector machine (SVM) is adopted for classification. SVM is a typical kind of machine learning algorithm based on statistical learning theory, which was developed in 1990s [27], the main idea of a SVM is finding the hyperplane with the maximum margin to the training samples [28], [29]. Assume x is a n-dimensional vector, x i (i = 1,2,...n) denotes the training samples, n is the total number of samples, and y stands for the labels of training samples, whose value can be 1 and −1.…”

Section: Classificationmentioning

confidence: 99%

“…The loss function of classifiers is defined by geometric interval in equation (19) [27]. Moreover, the loss function is defined in equation 20, where A is applied to adjust the weight of the empirical error and ε i stands for the empirical error [26]- [28].…”

Section: Classificationmentioning

confidence: 99%

See 1 more Smart Citation

A Noninvasive System for the Automatic Detection of Gliomas Based on Hybrid Features and PSO-KSVM

et al. 2019

View full text Add to dashboard Cite

Due to their location, malignant brain tumors are one of humanity's greatest killers, among these tumors, gliomas are the most common. The early detection of gliomas can contribute to the design of proper treatment schemes and, thus, improve the survival rate of patients. However, it is a challenging task to detect the gliomas within the complex structure of the brain. The conventional artificial diagnosis is timeconsuming and relies on the clinical experience of radiologists. To detect gliomas more efficiently, this paper proposes a noninvasive automatic diagnosis system for gliomas based on the machine learning methods. First, image standardization, including size normalization and background removal, is applied to produce standard images; then, the modified dynamic histogram equalization is implemented to enhance the low-contrast standard brain images, and skull removal based on outlier detection is presented. Furthermore, hybrid features, including gray-level co-occurrence matrix, pyramid histogram of the oriented gradient, modified completed local binary pattern, and intensity-based features are extracted together from the enhanced images, and their dimensions are reduced by principal component analysis. Kernel support vector machine (KSVM) combined with the particle swarm optimization is eventually adopted to train classifiers; in this paper, brain magnetic resonance imaging images are labeled with normal, glioma, and other. The experimental results show that the accuracy, sensitivity, and specificity of the proposed method can reach 98.36%, 99.17%, and 97.83%, respectively, which indicates that the proposed method performs better than many current systems. INDEX TERMS Modified CLBP, PSO-KSVM, Glioma detection, hybrid features, skull removal. FIGURE 3. The process of background removal based on an adaptive threshold.

show abstract

“…16 For the labeled data, supervised techniques are used such as decision forests 17,18 in which several decision trees are combined to predict the outcome. High dimensional effective algorithms like support vector machines (SVMs) 19 are used for classification and regression problems. Extremely randomized trees, 20 self-organizing maps 21 are used for exploratory data analysis and visualization.…”

Section: Introductionmentioning

confidence: 99%

Enc‐Unet: A novel method for Glioma segmentation

Hussain

Sachdeva

Ahuja

et al. 2022

Int J Imaging Syst Tech

View full text Add to dashboard Cite

The diagnosis' treatment planning, follow-up and prognostication of Gliomas is significantly enhanced on Magnetic Resonance Imaging. In the present research, deep learning-based variant of convolutional neural network methodology is proposed for glioma segmentation where pretrained autoencoder acts as backbone to the 3D-Unet which performs the segmentation task as well as image restoration. Further, Unet accepts input as the combination of three non-native MR images (T2, T1CE, and FLAIR) to extract maximum and superior features for segmenting tumor regions. Further, weighted dice loss employed, focusses on segregating tumor region into three regions of interest namely whole tumor with oedema (WT), enhancing tumor (ET), and tumor core (TC). The optimizer preferred in the proposed methodology is Adam and the learning rate is initially set to 1e À 4, progressively reduced by a cosine decay after 50 epochs. The learning parameters are reduced to a larger extent (up to 9.8 M as compared to 27 M). The experimental results show that the proposed model achieved Dice similarity coefficients: 0.

show abstract

Modified support vector machines for MR brain images recognition

Cited by 16 publications

References 10 publications

Fully-automated deep learning-powered system for DCE-MRI analysis of brain tumors

Fully-automated deep learning-powered system for DCE-MRI analysis of brain tumors

A Noninvasive System for the Automatic Detection of Gliomas Based on Hybrid Features and PSO-KSVM

Enc‐Unet: A novel method for Glioma segmentation

Contact Info

Product

Resources

About