A Computer-Based Brain Tumor Detection Approach with Advanced Image Processing and Probabilistic Neural Network Methods

Ural, Berkan

doi:10.1007/s40846-017-0353-y

Cited by 26 publications

(16 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In 2017, Ural 23 has implemented brain tumor detection technique aided by computer in MRI image was made. The main reason of this research work intends in detecting and localizing the brain tumor areas by using the PNN method and advanced image processing techniques.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Brain tumor segmentation and classification via adaptive CLFAHE with hybrid classification

Bojaraj

Jayanthi

2020

Int J Imaging Syst Tech

View full text Add to dashboard Cite

This article exploits a new brain tumor classification model that includes five steps like (a) denoising, (b) skull stripping, (c) segmentation, (d) feature extraction and (e) classification. Initially, the image is subjected under the denoising process, where the noise removal procedure is carried out by employing the entropy‐based trilateral filter. Then, the denoised image is applied to the skull stripping process via Otsu thresholding and morphology segmentation. Subsequently, the next step is the segmentation, where the image is segmented by deploying the adaptive CLFAHE (contrast limited fuzzy adaptive histogram equalization) technique. Once the segmentation is completed, gray‐level co‐occurrence matrix (GLCM) based features are extracted. Finally, the extracted features are processed under hybrid classification model to attain enhanced classification rate. Here, hybrid classification hybrids two classifiers namely deep belief network (DBN) and Bayesian regularization classifier. The vital contribution of this research work exists in the optimal selection of hidden neurons in the DBN. Along with this, the membership function (bounding limits) of fuzzy logic is optimally selected. For this, a new lion exploration based whale optimization (LE‐WO) algorithm is proposed in this article that hybrids the concept of (lion algorithm) LA and (whale optimization algorithm) WOA. Finally, the performance of proposed LE‐WO is compared over the other methods in terms of accuracy, sensitivity, specificity, precision, negative predictive value (NPV), F1_score and Matthews correlation coefficient (MCC), False positive rate (FPR), False negative rate (FNR) and false discovery rate (FDR) and proves the betterments of proposed work. From the outcomes, the accuracy measure of proposed model at 60th population size is 1.98%, 1.81%, 1.32%, 3.46% and 0.75% better than PSO, FF, GWO, WOA and LA, respectively. Similarly, in 80th population size, the performance of the implemented model is 4.47%, 5.04%, 3.96%, 6.29% and 1.37% superior to PSO, FF, GWO, WOA and LA, respectively. Thus, the betterment of the adopted scheme is validated in an effective manner.

show abstract

Section: Methodsmentioning

confidence: 99%

“…Ineffective cost and Different classes will overlap in feature space are the main challenge of this research work. PNN 23 has significantly booted performance and better segmentation performance. The main limitations of this case are bit slower in classifying new cases and require more memory space.…”

Section: Methodsmentioning

confidence: 99%

Brain tumor segmentation and classification via adaptive CLFAHE with hybrid classification

Bojaraj

Jayanthi

2020

Int J Imaging Syst Tech

View full text Add to dashboard Cite

show abstract

“…Finally, the deep neural network is employed to classify the MR brain image based on the selected features and obtained 92% accuracy. Ural [ 39 ] initially enhanced the brain MRI using different image processing techniques. Also, different segmentation process has been mixed for boosting the performance of the solution.…”

Section: Related Workmentioning

confidence: 99%

MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers

Kang

Ullah

Gwak

2021

Sensors

346

140

View full text Add to dashboard Cite

Brain tumor classification plays an important role in clinical diagnosis and effective treatment. In this work, we propose a method for brain tumor classification using an ensemble of deep features and machine learning classifiers. In our proposed framework, we adopt the concept of transfer learning and uses several pre-trained deep convolutional neural networks to extract deep features from brain magnetic resonance (MR) images. The extracted deep features are then evaluated by several machine learning classifiers. The top three deep features which perform well on several machine learning classifiers are selected and concatenated as an ensemble of deep features which is then fed into several machine learning classifiers to predict the final output. To evaluate the different kinds of pre-trained models as a deep feature extractor, machine learning classifiers, and the effectiveness of an ensemble of deep feature for brain tumor classification, we use three different brain magnetic resonance imaging (MRI) datasets that are openly accessible from the web. Experimental results demonstrate that an ensemble of deep features can help improving performance significantly, and in most cases, support vector machine (SVM) with radial basis function (RBF) kernel outperforms other machine learning classifiers, especially for large datasets.

show abstract

“…The authors in [29] applied the same concept and compared their results with k-means, expectation-maximization, mean shift, and fuzzy c-means. Furthermore, the authors in [30] enhanced the previous work by adding an extra layer that was based on an integrated set of image processing algorithms, while the other method was based on a modified and improved probabilistic neural network structure. Simulation results showed the efficiency of this algorithm to accurately detect and identify the tumor.…”

Section: Related Workmentioning

confidence: 99%

3D-MRI Brain Tumor Detection Model Using Modified Version of Level Set Segmentation Based on Dragonfly Algorithm

et al. 2020

View full text Add to dashboard Cite

Accurate brain tumor segmentation from 3D Magnetic Resonance Imaging (3D-MRI) is an important method for obtaining information required for diagnosis and disease therapy planning. Variation in the brain tumor’s size, structure, and form is one of the main challenges in tumor segmentation, and selecting the initial contour plays a significant role in reducing the segmentation error and the number of iterations in the level set method. To overcome this issue, this paper suggests a two-step dragonfly algorithm (DA) clustering technique to extract initial contour points accurately. The brain is extracted from the head in the preprocessing step, then tumor edges are extracted using the two-step DA, and these extracted edges are used as an initial contour for the MRI sequence. Lastly, the tumor region is extracted from all volume slices using a level set segmentation method. The results of applying the proposed technique on 3D-MRI images from the multimodal brain tumor segmentation challenge (BRATS) 2017 dataset show that the proposed method for brain tumor segmentation is comparable to the state-of-the-art methods.

show abstract

A Computer-Based Brain Tumor Detection Approach with Advanced Image Processing and Probabilistic Neural Network Methods

Cited by 26 publications

References 30 publications

Brain tumor segmentation and classification via adaptive CLFAHE with hybrid classification

Brain tumor segmentation and classification via adaptive CLFAHE with hybrid classification

MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers

3D-MRI Brain Tumor Detection Model Using Modified Version of Level Set Segmentation Based on Dragonfly Algorithm

Contact Info

Product

Resources

About