Performance Improved Iteration-Free Artificial Neural Networks for Abnormal Magnetic Resonance Brain Image Classification

Hemanth, D. Jude; Vijila, C. Kezi Selva; Selvakumar, A. Immanuel; Anitha, J.

doi:10.1016/j.neucom.2011.12.066

Cited by 53 publications

(24 citation statements)

References 20 publications

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“…A supervised KNN and feed forward Artificial Neural Network (ANN) hybrid classifier is used for MRI classification. A probabilistic neural network model PNN with textural features also used for brain tumor characterization [14]. While a modification based on iteration of ANN is done in [15], which improves the performance rate of abnormal to normal brain MR image classification.…”

Section: Related Workmentioning

confidence: 99%

Performance analysis of Gabor-Wavelet based features in classification of high grade malignant brain tumors

Vidyarthi

Mittal

2015

2015 39th National Systems Conference (NSC)

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Incorporating machine learning based concepts and knowledge in medical science domain elaborates the interdisciplinary research areas. Using such the identification of the disease is accomplished at initial state of its generation with better accuracy as compared to naked eye. In past, lot of feature extraction approaches were proposed to extract the features in spectral domain. Among all approaches, two major approaches was Gabor filter based feature extraction and Wavelet based feature extraction mechanism. But it leads to the kiosk that which feature extraction mechanism is better for classification perspective. This paper focuses on machine learning based approach for performance analysis for the grade of malignant tumor types using a diverse feature set based on Gabor and Wavelet transformation. Further, various feature selection (FS) algorithms are taken into consideration for selection of the best feature set among the feature vector. For experimental purpose, several state-of-art classifiers are used for analysis of the performance for the classification of malignant brain tumors in magnetic resonance (MR) images. In experimentation several high grade malignant brain tumors like Central Neuro Cytoma (CNC), Glioblastoma Multiforme (GBM), Gliomas, Intra Ventricular Malignant Mass, and Metastasis are taken into consideration having 30 images of each tumor type. The classification accuracies achieved using various combinations of FS-Classifier's are presented in detail.

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Section: Related Workmentioning

confidence: 99%

Performance analysis of Gabor-Wavelet based features in classification of high grade malignant brain tumors

Vidyarthi

Mittal

2015

2015 39th National Systems Conference (NSC)

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“…Thus, the development of the immune system for tumor diagnosis and localization is important. A deeper and more comprehensive training focuses on playing an important role in brain sculpting, division, recording and classification of tumor tissue [20,16].…”

Section: Introductionmentioning

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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“…The human brain is prone to various diseases. The diagnosis of these disease require proper diagnosis from the scanning report of the radiologists through different imaging techniques [1]. Magnetic Resonance Imaging (MRI) provides a clear and proper neural tissue architecture of human brain.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Extreme Learning Machine for Classification of Brain MRIs

Satapathy,

Hota,

Pradhan

2019

IJEAT

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Magnetic Resonance Imaging (MRI) technique of brain is the most important aspect of diagnosis of brain diseases. The manual analysis of MR images and identifying the brain diseases is tedious and error prone task for the radiologists and physicians. In this paper 2-Dimensional Discrete Wavelet Transformation (2D DWT) is used for feature extraction and Principal Component Analysis (PCA) is used for feature reduction. The three types of brain diseases i.e. Alzheimer, Glioma and Multiple Sclerosis are considered for this work. The Two Hidden layer Extreme learning Machine (TELM) is used for classification of samples into normal or pathological. The performance of the TELM is compared with basic ELM and the simulation results indicate that TELM outperformed the basic ELM method. Accuracy, Recall, Sensitivity and F-score are considered as the classification performance measures in this paper

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Performance Improved Iteration-Free Artificial Neural Networks for Abnormal Magnetic Resonance Brain Image Classification

Cited by 53 publications

References 20 publications

Performance analysis of Gabor-Wavelet based features in classification of high grade malignant brain tumors

Performance analysis of Gabor-Wavelet based features in classification of high grade malignant brain tumors

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

Exploring the Extreme Learning Machine for Classification of Brain MRIs

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