Brain tumor classification using the fused features extracted from expanded tumor region

2022 ◽  
Vol 72 ◽  
pp. 103356
Author(s):  
Coşku Öksüz ◽  
Oğuzhan Urhan ◽  
Mehmet Kemal Güllü
Keyword(s):  
Brain Tumor ◽  
Tumor Classification ◽  
Tumor Region

scholarly journals Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition

PLoS ONE ◽  
2015 ◽  
Vol 10 (10) ◽  
pp. e0140381 ◽  
Author(s):  
Jun Cheng ◽  
Wei Huang ◽  
Shuangliang Cao ◽  
Ru Yang ◽  
Wei Yang ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Tumor Classification ◽  
Tumor Region

scholarly journals Correction: Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition

PLoS ONE ◽  
2015 ◽  
Vol 10 (12) ◽  
pp. e0144479 ◽  
Author(s):  
Jun Cheng ◽  
Wei Huang ◽  
Shuangliang Cao ◽  
Ru Yang ◽  
Wei Yang ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Tumor Classification ◽  
Tumor Region

Combining Wavelet Texture Features and Deep Neural Network for Tumor Detection and Segmentation Over MRI

2019 ◽  
Vol 28 (4) ◽  
pp. 571-588 ◽  
Author(s):  
Srinivasalu Preethi ◽  
Palaniappan Aishwarya
Keyword(s):  
Brain Tumor ◽  
Classification Scheme ◽  
Texture Features ◽  
The Body ◽  
Tumor Classification ◽  
Flower Pollination Algorithm ◽  
Fuzzy C Means Clustering ◽  
Tumor Region ◽  
Occurrence Matrix ◽  
The Brain

Abstract A brain tumor is one of the main reasons for death among other kinds of cancer because the brain is a very sensitive, complex, and central portion of the body. Proper and timely diagnosis can prolong the life of a person to some extent. Consequently, in this paper, we have proposed a brain tumor classification scheme on the basis of combining wavelet texture features and deep neural networks (DNNs). Normally, the system comprises four modules: (i) feature extraction, (ii) feature selection, (iii) tumor classification, and (iv) segmentation. Primarily, we eliminate the noise from the image. Then, the feature matrix is produced by combining wavelet texture features [gray-level co-occurrence matrix (GLCM)+wavelet GLCM]. Following that, we select the relevant features with the help of the oppositional flower pollination algorithm (OFPA) because a high number of features are major obstacles for classification. Then, we categorize the brain image based on the selected features using the DNN. After the classification procedure, the projected scheme extracts the tumor region from the tumor images with the help of the possibilistic fuzzy c-means clustering (PFCM) algorithm. The experimentation results show that the proposed system attains the better result associated with the available methods.

A Survey on Detection and Classification of Brain Tumor Using Image Processing Techniques

2020 ◽  
pp. 967-973
Author(s):  
V. Deepika ◽  
T. Rajasenbagam
Keyword(s):  
Image Processing ◽  
Brain Tumor ◽  
Soft Tissues ◽  
Tumor Detection ◽  
Tumor Classification ◽  
Normal Brain ◽  
Image Processing Techniques ◽  
Normal Brain Function ◽  
Processing Techniques ◽  
The Brain

A brain tumor is an uncontrolled growth of abnormal brain tissue that can interfere with normal brain function. Although various methods have been developed for brain tumor classification, tumor detection and multiclass classification remain challenging due to the complex characteristics of the brain tumor. Brain tumor detection and classification are one of the most challenging and time-consuming tasks in the processing of medical images. MRI (Magnetic Resonance Imaging) is a visual imaging technique, which provides a information about the soft tissues of the human body, which helps identify the brain tumor. Proper diagnosis can prevent a patient's health to some extent. This paper presents a review of various detection and classification methods for brain tumor classification using image processing techniques.

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