Brain Tumor Classification Based on Singular Value Decomposition

Abbadi, Nidahl K. El; Kadhim, Neamah E.

doi:10.17148/ijarcce.2016.58116

Cited by 6 publications

(5 citation statements)

References 4 publications

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“…The algorithm first trained with normal brain MRI images, then in the second step the algorithm become capable to classify the brain MRI images into normal and abnormal image (that have a tumor). The accuracy of this algorithm was up to 97% [7].…”

Section: Literature Surveymentioning

confidence: 94%

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Detection and recognition of brain tumor based on DWT, PCA and ANN

Abbadi

Shoman

2020

IJEECS

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Brain tumor is one of more dangerous diesis that affected more than 100 persons every day. The challenge is how to detect and recognise benign and malignant tumor without surgery. In this paper, initially, brain images are filtered to remove unwanted particles, then a new method for automatic segmentation of lesion area is carried out based on mean and standard deviation. Combining both solidity property and morphological operation used to detect only the tumor from segmented image. Mathematical morphology such as close used to join narrow breaks regions in an object, fill the small holes and remove small objects. Features extracted from image by using wavelet transform, followed by applying principle component analysis (PCA) to reduce the dimensions of features. Classification of tumor based on neural network, where the inputs to the network are thirteen statistical features and textural features. The algorithm is trained with 20 of brain MRI images and tested with 45 brain MRI images. Accuracy for this method was encourage and reach near 100% in identifying normal and abnormal tissues from MRI images.

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Section: Literature Surveymentioning

confidence: 94%

“…Therefore, there is an important for automatic classification using different techniques. In these techniques the classifier first trained with known data (images) that belonging to number of classes, then the classifier became capable of classifying invisible images accurately into a particular class [7].…”

Section: Introductionmentioning

confidence: 99%

Detection and recognition of brain tumor based on DWT, PCA and ANN

Abbadi

Shoman

2020

IJEECS

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“…A hybrid model has been developed in which VGG-net, ResNet, and LSTM models are merged for tumor cell classification that provides 71% accuracy rate on Alex and ResNet and 84% on the VGG16-LSTM model [ 34 ]. The singular decomposition value is employed for tumor classification and provides 90% sensitivity (SE), 98% specificity, and 96.66% accuracy [ 35 ]. A new model has been proposed based on DWT for tumor classification and has achieved 93.94% accuracy [ 36 ].…”

Section: Related Workmentioning

confidence: 99%

A New Model for Brain Tumor Detection Using Ensemble Transfer Learning and Quantum Variational Classifier

Amin

Anjum

Sharif

et al. 2022

Computational Intelligence and Neuroscience

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A brain tumor is an abnormal enlargement of cells if not properly diagnosed. Early detection of a brain tumor is critical for clinical practice and survival rates. Brain tumors arise in a variety of shapes, sizes, and features, with variable treatment options. Manual detection of tumors is difficult, time-consuming, and error-prone. Therefore, a significant requirement for computerized diagnostics systems for accurate brain tumor detection is present. In this research, deep features are extracted from the inceptionv3 model, in which score vector is acquired from softmax and supplied to the quantum variational classifier (QVR) for discrimination between glioma, meningioma, no tumor, and pituitary tumor. The classified tumor images have been passed to the proposed Seg-network where the actual infected region is segmented to analyze the tumor severity level. The outcomes of the reported research have been evaluated on three benchmark datasets such as Kaggle, 2020-BRATS, and local collected images. The model achieved greater than 90% detection scores to prove the proposed model's effectiveness.

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“…As a result, the use of several strategies for automated categorization is critical. In such systems, the classifier is initially trained using branded data from a variety of classes, and thereafter it becomes proficiently reliable in categorizing unbranded images into a certain class 7 . The process of recognizing and defining the type of tumor is regarded as the utmost arduous task.…”

Section: Introductionmentioning

confidence: 99%

“…In such systems, the classifier is initially trained using branded data from a variety of classes, and thereafter it becomes proficiently reliable in categorizing unbranded images into a certain class. 7 The process of recognizing and defining the type of tumor is regarded as the utmost arduous task. The quantitative variables to be retrieved are influenced by a number of factors, including the features of the BT, the features of the area bordering the tumor, and the parameters of the imaging technology.…”

mentioning

confidence: 99%

An enhanced framework for identifying brain tumor using discrete wavelet transform, deep convolutional network, and feature fusion‐based machine learning techniques

Mehrotra,

Ansari,

Agrawal

et al. 2023

Int J Imaging Syst Tech

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Today, the histological study of biopsy specimens is still used to diagnose brain tumors (BTs). This existing procedure is intrusive, arduous, and liable to mistakes. These downsides highlight the standing of employing a completely computerized process for identifying the evolution of tumors in the brain. A primary BT affects an estimated 0.7 million persons in the United States now and more are expected to be detected in the coming years. The ability to categorize magnetic resonance (MR) brain images into ordinary and pathological categories has the boundless ability to significantly diminish the burden on the radiologist. Pre‐processing, extraction, and reduction of features along with their classification are the parameters of statistical‐based methodologies that have been frequently used for this purpose. In this work, an enhanced framework for the identification of the BT is proposed using discrete wavelet transform (DWT), deep convolutional network (DCN), and machine learning (ML). As DWT is primarily used for image compression and denoising applications however in the presented research work it has been utilized for extricating pivotal features from the MR images using the feature fusion technique. DCN is also utilized for the extraction of pivotal deep features which are then combined with the wavelet‐based features for the purpose of BT identification. The classification of tumorous and non‐tumorous MR images is done using ML applications. The results obtained from the proposed model exhibit an utmost accuracy of 99.5% with an area under curve of 1 in identifying tumorous and non‐tumorous MR images as compared to various state‐of‐the‐art models. The proposed model can be efficiently used for assisting radiologists and medical experts in validating their decisions for BT identification.

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Brain Tumor Classification Based on Singular Value Decomposition

Cited by 6 publications

References 4 publications

Detection and recognition of brain tumor based on DWT, PCA and ANN

Detection and recognition of brain tumor based on DWT, PCA and ANN

A New Model for Brain Tumor Detection Using Ensemble Transfer Learning and Quantum Variational Classifier

An enhanced framework for identifying brain tumor using discrete wavelet transform, deep convolutional network, and feature fusion‐based machine learning techniques

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