Brain tumor classification from multi-modality MRI using wavelets and machine learning

Khalid, Usman; Rajpoot, Kashif

doi:10.1007/s10044-017-0597-8

Cited by 196 publications

(81 citation statements)

References 26 publications

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“…DWT as a mathematical research study presents a multi-scale image processing in tree decomposition [14], Fig. 1 clarifies the tree decomposition which is called the Mallat -tree algorithm [15,16].…”

Section: Discrete Wavelet Transformmentioning

confidence: 99%

Brain Diagnoses Detection Using Whale Optimization Algorithm Based on Ensemble Learning Classifier

Fouad¹,

Moftah²,

Hefny³

2020

IJIES

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Brain cancer importance emanates from the importance of the brain as an organ and its functions. It has a great effect on the whole human body. Identification brain cancer according to its type, it refers to a multiclass classification problem in the machine learning world. In the real-world, object detection and classification face numerous challenges. The object has a large variation in appearances. In this research, a Haar Discrete Wavelet transforms hybrid with the Histogram of Oriented Gradients (HDWT-HOG) features descriptors are proposed by the local gradients in MR image as shape information. The whale optimization algorithm (WOA) plays a great role to reduce the numbers of HOG and Harr features from 38,640 to 120 features only which are less than .01% from all features. This reduction doesn't affect the system performance but it saves time in the classification phase. The test image is matched with its learned class by performing a Bagging ensemble learning classifier. Bagging achieves 96.4% in average accuracy but when Boosting is used, it achieves 95.8%.

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Section: Discrete Wavelet Transformmentioning

confidence: 99%

Brain Diagnoses Detection Using Whale Optimization Algorithm Based on Ensemble Learning Classifier

Fouad¹,

Moftah²,

Hefny³

2020

IJIES

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“…K. Usman, and K. Rajpoot [17] proposed a brain tumour segmentation, and classification scheme for Multi-modality MRI scans. From the pre-processed images, intensity, local neighbourhood, intensity variance and the wavelet texture were extracted.…”

Section: Literature Surveymentioning

confidence: 99%

Image Analysis for MRI Based Brain Tumour Detection Using Hybrid Segmentation and Deep Learning Classification Technique

Rao¹,

Lingappa²

2019

IJIES

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Indefinite and uncontrollable growth of cells leads to tumours in the human brain. A proper treatment and early diagnosis of brain tumours are essential to prevent permanent damage to the brain. In medical image diagnosis, the tumour segmentation and classification schemes are used for identifying the tumour and non-tumour cells in the brain. The automatic classification is a challenging task which utilizes the traditional methods due to its more execution time and ineffective decision making. To overcome this problem, this research proposes an automatic tumour classification method named as Hybrid Kernel based Fuzzy C-Means clustering-Convolutional Neural Network (Hybrid KFCM-CNN) method. The performance of Hybrid KFCM-CNN method is validated using T1-Weighted Contrast Enhanced Magnitude Resonance Imaging (T1-W CEMRI) database. A tumour portion is segmented from the MRI brain image by using Hybrid KFCM method. After the segmentation process, statistical features and super pixels based SIFT, Discrete Cosine Transform (DCT) methods were performed on the segmented image to enhance feature subsets. The best feature values were given to the CNN classifier as an input; it is classified into different types of brain tumours such as Meningioma, Glioma and Pituitary. The experimental outcome shows that the Hybrid KFCM-CNN method improved the accuracy of brain tumour classification up to 14.06 % than existing classifiers: SVM and CNN.

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“…On the other hand, tumor type clas-sification by human inspection is an extremely time consuming and error prone task, which highly depends on the experience and skills of the radiologist. This fact has resulted in a recent surge of interest [4][5][6][7][8][9] in designing highly accurate automated image processing systems for brain tumor classification. Among different available medical imaging technologies, Magnetic Resonance Imaging (MRI) is more favored for brain tumor type classification due to its harmless nature and is also the focus of this paper.…”

Section: Introductionmentioning

confidence: 99%

Brain Tumor Type Classification via Capsule Networks

Afshar

Mohammadi

Plataniotis

2018

2018 25th IEEE International Conference on Image Processing (ICIP)

407

196

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Brain tumor is considered as one of the deadliest and most common form of cancer both in children and in adults. Consequently, determining the correct type of brain tumor in early stages is of significant importance to devise a precise treatment plan and predict patient's response to the adopted treatment. In this regard, there has been a recent surge of interest in designing Convolutional Neural Networks (CNNs) for the problem of brain tumor type classification. However, CNNs typically require large amount of training data and can not properly handle input transformations. Capsule networks (referred to as CapsNets) are brand new machine learning architectures proposed very recently to overcome these shortcomings of CNNs, and posed to revolutionize deep learning solutions. Of particular interest to this work is that Capsule networks are robust to rotation and affine transformation, and require far less training data, which is the case for processing medical image datasets including brain Magnetic Resonance Imaging (MRI) images. In this paper, we focus to achieve the following four objectives: (i) Adopt and incorporate CapsNets for the problem of brain tumor classification to design an improved architecture which maximizes the accuracy of the classification problem at hand; (ii) Investigate the over-fitting problem of CapsNets based on a real set of MRI images; (iii) Explore whether or not CapsNets are capable of providing better fit for the whole brain images or just the segmented tumor, and; (iv) Develop a visualization paradigm for the output of the CapsNet to better explain the learned features. Our results show that the proposed approach can successfully overcome CNNs for the brain tumor classification problem.

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Brain tumor classification from multi-modality MRI using wavelets and machine learning

Cited by 196 publications

References 26 publications

Brain Diagnoses Detection Using Whale Optimization Algorithm Based on Ensemble Learning Classifier

Brain Diagnoses Detection Using Whale Optimization Algorithm Based on Ensemble Learning Classifier

Image Analysis for MRI Based Brain Tumour Detection Using Hybrid Segmentation and Deep Learning Classification Technique

Brain Tumor Type Classification via Capsule Networks

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