A PSO based method for detection of brain tumors from MRI

Chandra, Satish; Bhat, Rajesh Shreedhar; Singh, Harinder

doi:10.1109/nabic.2009.5393455

Cited by 35 publications

(14 citation statements)

References 16 publications

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“…In this section, some of the significant approaches proposed by the researchers have been presented. One of the most used method in Tumour detection is by using image segmentation [2]. Image segmentation is the task of dividing an image into homogeneous regions.…”

Section: Existing Methodsmentioning

confidence: 99%

“…The difference between Supervised and Unsupervised methods is that supervised methods make use of training data that has been manually labelled, while unsupervised methods do not. The advantage of supervised methods is that relevant patterns in the data are discovered automatically, rather than through manual experimentation and intuition [2]. Another mostly used existing method is based on the thresholding and region growing.…”

Section: Existing Methodsmentioning

confidence: 99%

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Design and Comparative Analysis of SVM and PR Neural Network for Classification of Brain Tumour in MRI

Rohilla¹,

Singal²

2014

IJCA

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Electroencephalograms (EEGs) or MRI are progressively emerging as a significant measure of brain activity and they possess immense potential for the diagnosis and treatment of mental and brain diseases and abnormalities. This research paper presents an automated system for efficient classification of brain tumours in MRI images using Support Vector Machine (SVM). It works on the principle of a vector which supports in training of classifier using train images. The results obtained from the above method are compared with those obtained from Artificial Neural Networks (ANNs). This method uses pattern recognition algorithms for the classification of tumour in MR images. First, a pattern recognition neural network is created and then trained. The trained pattern recognition neural network when fed with a test MR image, effectively classifies the images. The performance analysis shows that qualitative results obtained from the proposed model are comparable with those obtained by ANN. The experimental results demonstrate the effectiveness of the proposed system in classification of brain tumour in MR images. The implementation is done using MATLAB R2012b using Artificial Neural Network and Image Processing Toolbox.

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Section: Existing Methodsmentioning

confidence: 99%

Section: Existing Methodsmentioning

confidence: 99%

Design and Comparative Analysis of SVM and PR Neural Network for Classification of Brain Tumour in MRI

Rohilla¹,

Singal²

2014

IJCA

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“…segmentation in medical field broadly classifies in two forms such as partial automatic [30,31] and completely automatic [32,33]. Chandra S, Bhat R, Singh H [34] proposed restoration and clustering based segmentation of brain tumor. This both segmentation method produced better results compared to other segmentation method in medical fields.…”

Section: Related Workmentioning

confidence: 99%

A Framework with OTSU’S Thresholding Method for Fruits and Vegetables Image Segmentation

Tripathi¹,

Maktedar²

2018

IJCA

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An accurate technique for segmentation of fruits and vegetables image is vital and major challenges in computer vision. Various segmentation techniques are available in digital image processing. In this paper, we introduce a framework for fruits and vegetables background subtraction employing Otsu"s algorithm. This method is widely used in various image segmentation applications. The Otsu"s method is useful in subtraction of background under the partial effect of occlusion, cropping, noisy and blurred images. Our proposed method was experimented by employing fruit and vegetable images acquired locally. Our experimental results confirm that, Otsu"s threshold based method is able to extract fruit and vegetable objects with good accuracy.

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“…Another medical field in which PSO has been used, consists detecting several disease such as cancer. Detection of brain tumors (Chandraet al [7]), extraction breast cancer patterns (Yeh et al [8]) and classification rule construction for breast cancer data sets (Gandhi et al [9]) are put in this field. With using a hybrid of PSO and ACO, a rulebased system has been proposed by Mangat, for rule mining in medical domain in [10], also a hybrid model for medical data classi¿cation by integrating a case-based reasoning approach and a particle swarm optimization model is developed by Chang et al in [11].…”

Section: Introductionmentioning

confidence: 99%

An ensemble PSO-based approach for diagnosis of coronary artery disease

Hedeshi

Abadeh

2011

2011 International Symposium on Artificial Intelligence and Signal Processing (AISP)

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The leading causes of heart failure are diseases that damage the heart. One of the most well-known diseases that cause heart failure is Coronary Artery Disease. Diagnosis of Coronary Artery Disease is an important medical problem. Many researchers have tried to develop intelligent medical systems to increase the ability of physicians in detecting this disease. Particle Swarm Optimization (PSO) has been successfully applied in data mining field to extract rule based classification systems. A new ensemble PSO-based approach to extract a set of rules for diagnosis of coronary artery disease is presented in this paper. The boosting method considers the cooperation between fuzzy rules that generate with PSO meta-heuristic. We called this approach as "EP-DC". We have evaluated our new classification approach via the well-known Cleveland data set. Results show that the proposed learning method can detect the coronary artery disease with an acceptable accuracy. In addition, the discovered rules have also considerable comprehensibility.

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A PSO based method for detection of brain tumors from MRI

Cited by 35 publications

References 16 publications

Design and Comparative Analysis of SVM and PR Neural Network for Classification of Brain Tumour in MRI

Design and Comparative Analysis of SVM and PR Neural Network for Classification of Brain Tumour in MRI

A Framework with OTSU’S Thresholding Method for Fruits and Vegetables Image Segmentation

An ensemble PSO-based approach for diagnosis of coronary artery disease

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