Rough set theory and feed forward neural network based brain tumor detection in magnetic resonance images

Rajesh, T.; Malar, R. Suja Mani

doi:10.1109/icanmeet.2013.6609287

Cited by 26 publications

(15 citation statements)

References 6 publications

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“…So, the algorithm is likely to identify only the significant part of the irregularity if the tumor is split into several factions [2]. Classification of MRI brain images with the help of an automated system having diverse pathological disorders is shown in another report [3]. The method discussed in this work shows that the irregularity is categorized into malignant and benignin an unmanned method.…”

Section: Relatedworkmentioning

confidence: 87%

“…region [1] region [2] region [3] region [4] region [5] region [6] region [7] region [8] region [9] With an I3 processor 4 GB RAM PC, it is not plausible to figure the associated segment progressively when N=1, 2 or 3. Our new calculation figures associated segment inside of 2 seconds for a picture size of 2008K with N=25.…”

Section: Tableiii Original Image Divisions Into 3*3regionsmentioning

confidence: 99%

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Automated Brain Tumor Detection and Segmentation from MRI Images using Adaptive Connected Component Pixel Segmentation

Sahayaraj*,

Subash,

Pratha

et al. 2019

IJITEE

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Magnetic resource imaging (MRI) imagesare used in examining the soft tissues which include brain tumors, ligament and tendon injury, spinal cord injury. Gray scale image processing is good for basic segmentation application.The exact location of brain tumor and its length is hard to find.This paper proposes an efficient method to segment the brain tumor. The result shows good segmentation accuracy.

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Section: Relatedworkmentioning

confidence: 87%

Section: Tableiii Original Image Divisions Into 3*3regionsmentioning

confidence: 99%

Automated Brain Tumor Detection and Segmentation from MRI Images using Adaptive Connected Component Pixel Segmentation

Sahayaraj*,

Subash,

Pratha

et al. 2019

IJITEE

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“…However, deep learning models do not discuss the uncertainties amongst the raw data, affecting accuracy. Therefore, numerous Fuzzy or Rough based deep learning approaches are introduced to handle this uncertainty problem for clustering and classification [1], [24], [43]- [45], [53], [54]. Deng et al [1], proposed hierarchically approaches to fuses the fuzzy logic and neural network that simultaneously leaned feature representations altogether for robust data classification.…”

Section: Related Work a Fuzzy-rough C-mean Clustering (Frcm)mentioning

confidence: 99%

“…Rajesh and Malar [54], proposed Rough Set based neural network for classification. In this approach, they first extract feature from input images using Rough set theory (RST), and then the selected are given as input to feed forward and A l−1 for a given data points a i as follows…”

Section: Related Work a Fuzzy-rough C-mean Clustering (Frcm)mentioning

confidence: 99%

A Semi-Supervised CNN With Fuzzy Rough C-Mean for Image Classification

2019

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While deep learning (DL) has proven to be a powerful paradigm for the classification of the large-scale big image data set. Deep learning network such as CNN requires a large number of labeled samples for training the network. However, often times the labeled data are difficult, expensive, and timeconsuming. In this paper, we propose a semi-supervised approach to fused fuzzy-rough C-mean clustering with convolutional neural networks (CNNs) to knowledge learning from simultaneously intra-model and inter-model relationships forming final data representation to be classified, which can be achieved by better performance. Conception behind this is to reduce the uncertainty in terms of vagueness and indiscernibility by using fuzzy-rough C-mean clustering and specifically removing noise samples by using CNN from raw data. The framework of our proposed semi-supervised approach uses all the training data with abundant unlabeled data with few labeled data to train FRCNN model. To show the effectiveness of our model, we used four benchmark large-scale image datasets and also compared it with the state-of-the-art supervised, unsupervised, and semi-supervised learning methods for image classification. INDEX TERMS Semi-supervised learning, convolutional neural network, fuzzy rough C-mean clustering, feature learning, and image classification.

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“…The modified FCM algorithm is a fast alternative to the traditional FCM technique. Rajesh and Malar [11] proposed brain MR image classification based on Rough set theory and feed-forward neural network classifier. The features are extracted from MRI images using Rough set theory.…”

Section: Related Workmentioning

confidence: 99%

Brain Tumor Detection based on Machine Learning Algorithms

Sharma¹,

Kaur²,

Gujral³

2014

IJCA

108

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Automated defect detection in medical imaging has become the emergent field in several medical diagnostic applications. Automated detection of tumor in Magnetic Resonance Imaging (MRI) is very crucial as it provides information about abnormal tissues which is necessary for planning treatment. The conventional method for defect detection in magnetic resonance brain images is human inspection. This method is impractical for large amount of data. So, automated tumor detection methods are developed as it would save radiologist time. The MRI brain tumor detection is complicated task due to complexity and variance of tumors. In this paper, tumor is detected in brain MRI using machine learning algorithms. The proposed work is divided into three parts: preprocessing steps are applied on brain MRI images, texture features are extracted using Gray Level Co-occurrence Matrix (GLCM) and then classification is done using machine learning algorithm.

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Rough set theory and feed forward neural network based brain tumor detection in magnetic resonance images

Cited by 26 publications

References 6 publications

Automated Brain Tumor Detection and Segmentation from MRI Images using Adaptive Connected Component Pixel Segmentation

Automated Brain Tumor Detection and Segmentation from MRI Images using Adaptive Connected Component Pixel Segmentation

A Semi-Supervised CNN With Fuzzy Rough C-Mean for Image Classification

Brain Tumor Detection based on Machine Learning Algorithms

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