A comprehensive framework for classification of brain tumour images using SVM and curvelet transform

Karthik, R.; Menaka, R.; Chellamuthu, C.

doi:10.1504/ijbet.2015.068054

Cited by 18 publications

(6 citation statements)

References 21 publications

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“…Later, a watershed-based segmentation procedure was applied to localize the lesion region [21]. From the extracted ROI, a three level wavelet decomposition has been carried out with different basis functions.…”

Section: Methodsmentioning

confidence: 99%

A critical appraisal on wavelet based features from brain MR images for efficient characterization of ischemic stroke injuries

Karthik

Menaka

2016

ELCVIA

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Ischemic stroke is a severe neuro disorder typically characterized by a block inside a blood vessel supplying blood to the brain. It remains the third leading cause of death, after a heart attack and cancer. Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) were the major imaging modalities used in diagnosing this disorder. While the CT imaging can be used at the primary stage, MRI proves to be a necessary aid for progressive diagnostic planning in the treatment of stroke injuries. Developing a fully automatic approach for lesion segmentation is a challenging issue due to the complex nature of the lesion structures. This research aims at examining the properties of such complex structures. It analyses the characteristics of the normal brain tissues and abnormal lesion structures using a three-level wavelet decomposition procedure. Four different wavelet functions namely Daubechies, Symlet, Coiflet and De-Meyer were applied to the different datasets and the resulting observations were examined based on their feature statistics. Experiments indicate that the feature statistics obtained using the Daubechies and the De-Meyer wavelets were able to distinguish between normal brain tissues and abnormal lesion structures.

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Section: Methodsmentioning

confidence: 99%

A critical appraisal on wavelet based features from brain MR images for efficient characterization of ischemic stroke injuries

Karthik

Menaka

2016

ELCVIA

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“…Then, unlabeled information becomes labeled information based on the estimated features during the testing process. Several studies have utilized learning for brain tumor identification such as self-organized maps (SOM) [21], fuzzy c-means (FCM) [22], K-means [23], support vector machine (SVM), and artificial neural networks (ANN) [24], which are illustrated as follows:…”

Section: Machine Learning-based Techniquesmentioning

confidence: 99%

IoT framework for brain tumor detection based on optimized modified ResNet 18 (OMRES)

et al. 2022

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Brain tumors are a serious health issue that affects many people’s lives. Such a tumor, which is either benign or malignant, can be fatal if malignant cells are not correctly diagnosed. According to the most recent human health care analysis system, the number of brain tumor patients has climbed dramatically and is now the 10th top cause of death. As a result, detecting brain tumors in their early stages can considerably improve the patient’s prospects of complete recovery and therapy. Thanks to improvements in information and communication technology, the Internet of things (IoT) has reached an evolutionary stage in the development of the modern health care environment. This paper provides a detailed examination of brain tumor detection approaches. Moreover, two different scenarios for detecting brain tumors will be proposed. On one hand, the first scenario depends on applying a deep convolutional neural network directly to brain images. On the other hand, the second scenario presents an IoT-based framework that adopts a multiuser detection system by sending the images to the cloud for early detection of brain tumors, which makes the system accessible to anyone and anywhere for accurate brain tumor categorization. The proposed CNN structure can be considered a modified version of the pre-trained ResNet18 CNN. Additionally, two key hyper-parameters are used to fine-tune the OMRES model, firstly different optimizers are tested using different learning rates, batch sizes, and a constant number of epochs, and secondly, the impact of changing dropout rates is made. Finally, comparisons between the OMRES model and traditional pre-trained models are discussed. Based on simulation findings, the RMSProp algorithm with a dropout rate of 0.5 verifies the best outcomes over other algorithms, where the suggested model achieves superior improvement with the highest rated accuracy of 98.67% compared to the conventional CNNs.

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“…Liao et al [29] have developed a new technique that has helped extract image characteristics for the identification of texture here graylevel Co-Occurrence Matrix (GL CM) statistical technique for the study of texture characteristics using spatial pixel correlation. Huang et al [30] implemented an object extraction technique. Vidyarthi and Mittal [31] developed a novel texturedependent extraction feature algorithm to extract relevant and informative features from the tumor-affected brain MR Images.…”

Section: Extracting Featuresmentioning

confidence: 99%

Survey of brain tumor segmentation with deep neural networks

Kumar¹,

Sarath²

2022

ijhs

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A brain tumour is a serious condition that, if not diagnosed and treated early on, can lead to death. Researchers have proposed a variety of traditional and recently developed deep learning based segmentation and classification approaches for determining the condition of the tumor. Deep learning is found to be efficient and robust for classification and segmentation as it detects the fine-to-coarse information about the tumors. The main component of deep learning is layered neural network architecture popularly known as convolutional neural network. Distinct information from brain images can be acquired and analysed depending on the architecture. In order to achieve high segmentation and classification accuracy, more research is required in this area. In this paper presents a review of state-of-the-art deep learning methods for brain tumor segmentation and deep learning neural networks, clearly highlighting their building blocks and different strategies. Finally, this article implying about present status on segmentation and classification of tumor-based image processing through deep learning models.

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A comprehensive framework for classification of brain tumour images using SVM and curvelet transform

Cited by 18 publications

References 21 publications

A critical appraisal on wavelet based features from brain MR images for efficient characterization of ischemic stroke injuries

A critical appraisal on wavelet based features from brain MR images for efficient characterization of ischemic stroke injuries

IoT framework for brain tumor detection based on optimized modified ResNet 18 (OMRES)

Survey of brain tumor segmentation with deep neural networks

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