Artificial neural networks design for classification of brain tumour

Deepa, S. N.; Devi, B. Aruna

doi:10.1109/iccci.2012.6158908

Cited by 32 publications

(7 citation statements)

References 14 publications

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“…The Self-Neural Network of Self-Neural Networks (SOMNN) is carried out in a fragmentation phase. Deepa and Devi [10] projected a system that extracted the classification and portions of the tumor. The best texture feature was taken from the image tested using statistical features.…”

Section: Related Workmentioning

confidence: 99%

Deep Weber Dominant Local Order Based Feature Generator and Improved Convolution Neural Network for Brain Tumor Segmentation in MR Images

Joseph¹,

Thomas²,

Ramya³

2020

IJEAT

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This paper introduces a scheme for retrieving deep features to carry out the procedure of recognising brain tumors from MR image. Initially, the MR brain image is denoised through the Modified Decision Based Unsymmetric Trimmed Median Filter (MDBUTMF) after that the contrast of the image is improved through Contrast Limited Adaptive Histogram Equalization (CLAHE). Once the pre-processing task is completed, the next phase is to extract the feature. In order to acquire the features of pre-processed images, this article offers a feature extraction technique named Deep Weber Dominant Local Order Based Feature Generator (DWDLOBFG). Once the deep features are retrieved, the next stage is to separate the brain tumor. Improved Convolution Neural Network (ICNN) is used to achieve this procedure. To explore the efficiency of deep feature extraction and in-depth machine learning methods, four performance indicators were used: Sensitivity (SEN), Jaccard Index (JI), Dice Similarity Coefficient (DSC) and Positive Predictive Value (PPV). The investigational outputs illustrated that the DWDLOBFG and ICNN achieve best outputs than existing techniques.

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Section: Related Workmentioning

confidence: 99%

Deep Weber Dominant Local Order Based Feature Generator and Improved Convolution Neural Network for Brain Tumor Segmentation in MR Images

Joseph¹,

Thomas²,

Ramya³

2020

IJEAT

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“…A self-organizing map neural network (SOMNN) was applied in the classification stage. Deepa and Devi [25] proposed a system consisting of feature extraction, classification, and tumour segmentation. Optimal texture features were extracted from tested images using statistical features.…”

Section: Related Workmentioning

confidence: 99%

Two-phase multi-model automatic brain tumour diagnosis system from magnetic resonance images using convolutional neural networks

Abd-Ellah

Awad

Khalaf

et al. 2018

J Image Video Proc.

118

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Brain tumour is a serious disease, and the number of people who are dying due to brain tumours is increasing. Manual tumour diagnosis from magnetic resonance images (MRIs) is a time consuming process and is insufficient for accurately detecting, localizing, and classifying the tumour type. This research proposes a novel two-phase multi-model automatic diagnosis system for brain tumour detection and localization. In the first phase, the system structure consists of preprocessing, feature extraction using a convolutional neural network (CNN), and feature classification using the error-correcting output codes support vector machine (ECOC-SVM) approach. The purpose of the first system phase is to detect brain tumour by classifying the MRIs into normal and abnormal images. The aim of the second system phase is to localize the tumour within the abnormal MRIs using a fully designed five-layer region-based convolutional neural network (R-CNN). The performance of the first phase was assessed using three CNN models, namely, AlexNet, Visual Geometry Group (VGG)-16, and VGG-19, and a maximum detection accuracy of 99.55% was achieved with AlexNet using 349 images extracted from the standard Reference Image Database to Evaluate Response (RIDER) Neuro MRI database. The brain tumour localization phase was evaluated using 804 3D MRIs from the Brain Tumor Segmentation (BraTS) 2013 database, and a DICE score of 0.87 was achieved. The empirical work proved the outstanding performance of the proposed deep learning-based system in tumour detection compared to other non-deep-learning approaches in the literature. The obtained results also demonstrate the superiority of the proposed system concerning both tumour detection and localization.

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“…This paper implements that the segmentation results can be effective on 250 pixels per class in a 256 x 256 resolution of MR images with a multi-layer back propagation method between 10 to 20 of hidden units and this method also provides relatively fast training and testing. Deepa et.al [6] proposed a system of brain tumor classification and they exploit their capabilities using Back Propagation Network and Radial Basis Function Network. This can be helpful in identifying cancer or noncancer tumor regions with respect to the detection in the tumor region.…”

Section: E Back Propagation (Bp)mentioning

confidence: 99%

A Survey on Advanced Segmentation Techniques for Brain MRI Image Segmentation

Nilakant

Menon

Vikram

2017

International Journal on Advanced Science, Engineering and Information Technology

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This paper presents a survey of advanced methods for segmenting the MRI (Magnetic Resonance Imaging) image of the brain. Segmentation of the brain is a challenging task because it requires more emphasized methods to differentiate each of the regions present in the brain image. The intensity differences between the different regions in the brain MRI image are very less, making it difficult to automate the entire segmentation process. Hence, a thorough understanding of the existing segmentation algorithm is essential for accurate segmentation. The segmentation algorithms surveyed in this work are Neural Network Model, Self-Organizing Maps, Radial Basis Function, Back Propagation, Fuzzy C-Means, Deformable Models, Level Set Models, Genetic Algorithm, Differential Evolutionary Algorithm, Hybrid Clustering and Artificial Intelligence. Such a survey would be helpful for researchers working in the field of brain image segmentation. The paper discusses the complexities in the segmentation algorithm and also the challenges in segmenting the brain MRI images. The segmentation outputs and analysis of the existing literature has also been discussed. The major criteria and their advantages in the segmentation of each algorithm have been reported accordingly in the observations.

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Artificial neural networks design for classification of brain tumour

Cited by 32 publications

References 14 publications

Deep Weber Dominant Local Order Based Feature Generator and Improved Convolution Neural Network for Brain Tumor Segmentation in MR Images

Deep Weber Dominant Local Order Based Feature Generator and Improved Convolution Neural Network for Brain Tumor Segmentation in MR Images

Two-phase multi-model automatic brain tumour diagnosis system from magnetic resonance images using convolutional neural networks

A Survey on Advanced Segmentation Techniques for Brain MRI Image Segmentation

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