An Automated Brain Tumor Detection in MRI using Firefly Optimized Segmentation

In the medical field brain tumor detection is an important application. The existing techniques of segmentation has various limitations. Existing techniques ignored the medical images which have poor quality or low brightness. Segmentation becomes the challenging issue as the image contains non-uniform object texture, cluttered objects, different image content and image noise. New technique of segmentation is proposed by research to detect tumor from MR images using firefly algorithm, then tumor is segmented and its features are extracted from MR image. The main goal of Research to design an algorithm for MRI based brain tumor segmentation using firefly algorithm and to improve the accuracy of the tumor detection. Fireflies produce a reaction in their body which produce light , this chemical reaction is called bioluminescent. By using firefly technique it is possible to detect and localize tumor accurately. For comparative analysis, various parameters are used to demonstrate the superiority of proposed method over the conventional ones.

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Improving Brain Tumor Segmentation in Multi-sequence MR Images Using Cross-Sequence MR Image Generation

Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - Lecture Notes in Computer Science ◽

10.1007/978-3-030-46643-5_3 ◽

2020 ◽

pp. 27-36

Author(s):

Guojing Zhao ◽

Jianpeng Zhang ◽

Yong Xia

Keyword(s):

Brain Tumor ◽

Tumor Segmentation ◽

Image Generation ◽

Mr Images ◽

Mr Image ◽

Brain Tumor Segmentation

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Brain MR Image Classification for Glioma Tumor detection using Deep Convolutional Neural Network Features

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405616666200311122429 ◽

2020 ◽

Vol 16 ◽

Author(s):

Ghazanfar Latif ◽

D.N.F. Awang Iskandar ◽

Jaafar Alghazo ◽

M. Mohsin Butt

Keyword(s):

Brain Tumor ◽

Image Classification ◽

High Grade Glioma ◽

Tumor Detection ◽

Tumor Classification ◽

Tumor Segmentation ◽

Low Grade ◽

Mr Images ◽

Mr Image ◽

Glioma Tumor

Background: Detection of brain tumor is a complicated task which requires specialized skills and interpretation techniques. Accurate brain tumor classification and segmentation from MR images provide an essential choice for medical treatments. The different objects within an MR image have similar size, shape, and density which makes the tumor classification and segmentation even more complex. Objectives: Classification of the brain MR images into tumorous and non-tumorous using deep features and different classifiers to get higher accuracy. Methods: In this study, a novel four-step process is proposed; pre-processing for image enhancement and compression, feature extraction using convolutional neural networks (CNN), classification using the multilayer perceptron and finally, tumor segmentation using enhanced fuzzy c-means method. Results: The system is tested on 65 cases in four modalities consisting of 40,300 MR Images obtained from the BRATS-2015 dataset. These include images of 26 Low-Grade Glioma (LGG) tumor cases and 39 High-Grade Glioma (HGG) tumor cases. The proposed CNN features-based classification technique outperforms the existing methods by achieving an average accuracy of 98.77% and a noticeable improvement in the segmentation results are measured. Conclusion: The proposed method for brain MR image classification to detect Glioma Tumor detection can be adopted as it gives better results with high accuracies.

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Bidirectional ConvLSTMXNet for Brain Tumor Segmentation of MR Images

Tehnički glasnik ◽

10.31803/tg-20210204162414 ◽

2021 ◽

Vol 15 (1) ◽

pp. 37-42

Author(s):

M. Ravikumar ◽

B.J. Shivaprasad

Keyword(s):

Deep Learning ◽

Brain Tumor ◽

Tumor Segmentation ◽

Mr Images ◽

Mr Image ◽

Brain Tumor Segmentation ◽

Data Set ◽

Tumour Segmentation ◽

Brain Tumour Segmentation

In recent years, deep learning based networks have achieved good performance in brain tumour segmentation of MR Image. Among the existing networks, U-Net has been successfully applied. In this paper, it is propose deep-learning based Bidirectional Convolutional LSTM XNet (BConvLSTMXNet) for segmentation of brain tumor and using GoogLeNet classify tumor & non-tumor. Evaluated on BRATS-2019 data-set and the results are obtained for classification of tumor and non-tumor with Accuracy: 0.91, Precision: 0.95, Recall: 1.00 & F1-Score: 0.92. Similarly for segmentation of brain tumor obtained Accuracy: 0.99, Specificity: 0.98, Sensitivity: 0.91, Precision: 0.91 & F1-Score: 0.88.

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