Brain Tumour Detection Using Pulse Coupled Neural Network (PCNN) and Back Propagation Network

Subashini, M. Monica; Sahoo, Sarat Kumar

doi:10.1049/cp.2012.2181

Cited by 15 publications

(8 citation statements)

References 7 publications

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“…The result produced tumor detection in the fifteenth image and normal in sixteenth. Finally they found PCNN can be an effective image analysis tool [22].…”

Section: Literature Reviewmentioning

confidence: 99%

Automated Brain Tumor Detection in Medical Brain Images and Clinical Parameters using Data Mining Techniques: A Review

Khan¹,

Singh²,

Maheshwari³

2014

IJCA

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Data mining is a growing field of research that intersects with many other fields such as Artificial Intelligent, Statistics, Visualization, Parallel Computing and Image Processing. Data mining techniques are good for Brain MRI image classification that can diagnose brain tumor and other diseases. In this paper we present an overview of the current research being carried out using the data mining techniques for the diagnosis of brain tumor. The goal of this study is to identify the most well performing data mining algorithms used on medical brain MRI and Clinical parameters. The following algorithms have been identified: Decision Trees, Support Vector Machine, Artificial Neural Networks and their Multilayer Perceptron model, and Fuzzy C-Means. Analyses show that it is very difficult to name a single data mining algorithm as the most suitable for the brain tumor detection or classification. At times some algorithms perform better than others, but there are cases when the properties of some of the above mentioned algorithm are combined together, they provide effective result. This paper also provides a critical evaluation of the literature reviewed, which reveals new facets of.

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“…The result produced tumor detection in the fifteenth image and normal in sixteenth. Finally they found PCNN can be an effective image analysis tool [22].…”

Section: Literature Reviewmentioning

confidence: 99%

Automated Brain Tumor Detection in Medical Brain Images and Clinical Parameters using Data Mining Techniques: A Review

Khan¹,

Singh²,

Maheshwari³

2014

IJCA

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“…Ensuring final results with significant information packages demonstrate the non plus ultra potentials of their total method. Subashini, M. Monica, and also Sarat Kumar Sahoo [6] talked about that the Brain tumor detection is an important program inside recent days. Professional medical troubles are intense if tumor is definitely discovered on the later on stage.…”

Section: Literature Surveymentioning

confidence: 99%

Improvement of Brain Tumor Feature based Segmentation using Decision based Alpha Trimmed Global Mean Filter

Sharma¹,

Singh²

2015

IJCA

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Detection of the brain tumor is an important application of medical image processing. The literature survey in this paper has shown that the many of the existing methods has unobserved the deprived quality images like images with amount of noise or poor brightness. Moreover the much of the existing work on tumor detection has abandoned the use of object based segmentation. The overall goal of this research work is to propose an efficient brain tumor detection using the feature detection androundness metric. To enhance the tumor detection rate further we have integrated the proposed object based tumor detection with the Decision based alpha trimmed global mean. The proposed technique has the ability to produce effective results even in case of high density of the noise.

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“…J.Li,S.Ghanavati et al [10] introduced the brain tumor detection technique by extracting the features like deformation, intensity, symmetry & texture features. M.Monica Subashini et al [11] represents the model Pulse coupled neural network by giving segmented MRI as input which detects the presence of tumor in the brain image. Shraiya Nancy et al [12] [17] represents the brain tumor detection using Computer-aided detection system.…”

Section: Literature Surveymentioning

confidence: 99%

DSS for Detection and Comparison of Brain Tumor on T2-Weighted MRI Images

Gaikwad¹,

Rokade²

2015

IJERT

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Previous work [1] introduced the method for the detection of tumors in human brain on the basis of T2-weighted MRI images. The author developed method which detect tumor area in human brain on the basis of bitmapped digital MRI Images. This paper showing the results of developed system. Using different brain disease MRI dataset the experiments were performed. The experiment results shows that the conceptually simple proposed method is effectively detect tumor blocks. The system also compares the tumor between two MRI images taken over different time period of a patient and shows percent rise or fall in tumor.

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Brain Tumour Detection Using Pulse Coupled Neural Network (PCNN) and Back Propagation Network

Cited by 15 publications

References 7 publications

Automated Brain Tumor Detection in Medical Brain Images and Clinical Parameters using Data Mining Techniques: A Review

Automated Brain Tumor Detection in Medical Brain Images and Clinical Parameters using Data Mining Techniques: A Review

Improvement of Brain Tumor Feature based Segmentation using Decision based Alpha Trimmed Global Mean Filter

DSS for Detection and Comparison of Brain Tumor on T2-Weighted MRI Images

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