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Sample datasets of brain tumor MRI Images Normal Brain MRI (1 to 4) Benign tumor MRI (5 to 8) Malignant tumor MRI (9 to 12)
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Brain tumor segmentation in multimodal MRI has great significance in clinical diagnosis and treatment. The utilization of multimodal information plays a crucial role in brain tumor segmentation. However, most existing methods focus on the extraction and selection of deep semantic features, while ignoring some features with specific meaning and impo...
Aiming at the problems of uneven brain tumor data classification and insufficient feature extraction, an improved brain tumor segmentation (BTS) method using deep learning network is proposed in this study. Here, we use U-net as to be the main network architecture, combined with the advantages of the residual network Resnet, which uses skip connect...
Citations
... The hybrid CNN method is faster than other deep-learning methods while maintaining high classification accuracy. In the same field, Hao Dong et al. proposed [21] a 2D fully convoluted segmentation network and Sadia et al. [22] introduced an automated brain tumor detection process on 200 T2 weighted MRI images. The proposed method surpassed earlier research methodologies, with 98.57% accuracy for Discrete Wavelet Transform (DWT), Principle Component Analysis (PCA), and Kernel Support Vector Machine (KSVM) (IPOL). ...
Abnormal growth of cells in the brain and its surrounding tissues is known as a brain tumor. There are two types, one is benign (non-cancerous) and another is malignant (cancerous) which may cause death. The radiologists' ability to diagnose malignancies is greatly aided by magnetic resonance imaging (MRI). Brain cancer diagnosis has been considerably expedited by the field of computer-assisted diagnostics, especially in machine learning and deep learning. In our study, we categorize three different kinds of brain tumors using four transfer learning techniques. Our models were tested on a benchmark dataset of 3064 MRI pictures representing three different forms of brain cancer. Notably, ResNet-50 outperformed other models with a remarkable accuracy of 99.06%. We stress the significance of a balanced dataset for improving accuracy without the use of augmentation methods. Additionally, we experimentally demonstrate our method and compare with other classification algorithms on the CE-MRI dataset using evaluations like F1-score, AUC, precision and recall.
Feature extraction is stated to be one of the most important aspects in the field of machine learning, image processing and pattern recognition. In these fields, as the dimension of the data increases, it becomes highly important to provide a reliable analysis for growing. The process of feature extraction mostly starts from basic dataset, and slowly, it builds features derived from the data to make an informative learning step for human reading. In order to present this process of learning, we thought of providing a position paper which will discuss all the criteria, information, methodology and existing work in the area of feature extraction in different fields and domain. A clear, descriptive analysis is presented that discusses the feature extraction methods for selecting significant features that will improve the quality of the results.
From the year 2000 onwards, deep learning methodology has gained widespread acceptance in the area in medical image processing, medical image analysis, and bioinformatics. The result of this transformation is that deep learning has significantly improved the methods of identification, estimation, and diagnosis in the application of medical fields, including neuroscience, brain tumors, lung cancer, abdominal cavity, heart, retina, and others. Deep learning is also being used to improve the accuracy of medical imaging. The idea of this article is to examine key deep learning issues that are relevant to brain tumor research, in the applications for deep learning like segmentation, classification, prediction, and evaluation. This article provides an overview of a large number of scientific study in deep learning for brain tumor analysis.KeywordsMalignantMRIDeep learningSupport vectorCNNConditional random field
A paradigm shift is observed in medical domain when it comes to automated identification of several human ailments. Many a research activities are still being carried out in this context towards accuracy and robustness. Brain related disease identification has always been a challenge in this direction. Although handful of benchmark schemes exist related to brain ailment (mainly seizure) identification, however, the specific case of seizure identification for pregnant women has been little explored. In this paper, such a scheme has been proposed that identifies seizure based on the magnetic resonance (MR) digital images especially of pregnant women. The proposed scheme first transforms the MR image through efficient two dimensional discrete orthonormal Stockwell transform (2D-DOST) to obtain meaningful vectors. Further, for generating the final feature vector, the regularized discriminant analysis (RDA) is used. This is followed by the task of identification through classification. There are two classes in this case namely, seizure and no-seizure. This identification is achieved through the utilization of random vector functional link network (RVFL). Along with the typical kernel extension it is dubbed KRVFL. Suitable experimental analysis is conducted that reveals satisfactory results in support of the proposed work with an overall rate of accuracy being 94.5%, 93%, and 92% for the specific samples (seizure during pregnancy), and two other sample sets respectively. The performance measure is done through a k-fold cross validation calculation. Performance comparison with other competent schemes shows that the proposed scheme is marginally efficient.
