Brain Image Classification Using Learning Machine Approach and Brain Structure Analysis

Veeramuthu, A.; Swaminathan, M. S.; Darsini, V. Priya

doi:10.1016/j.procs.2015.04.030

Cited by 28 publications

(10 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The thing that isolates it from neural systems is the huge realizing, which is utilized to explain the advancement of new technology in an ANN to process bigger measures of information and to improve its precision of the data classification [ 36 ]. Convolution Neural Network (CNN) is an exceptional sort of neural system for preparing information in image, text, and sound structures that have worked effectively in their usage [ 25 ]. The expression “Convolution Neural Network” built up a measurable activity called convolution, to show their network.…”

Section: Methodsmentioning

confidence: 99%

Brain Tumor Detection and Classification by MRI Using Biologically Inspired Orthogonal Wavelet Transform and Deep Learning Techniques

Arif

Ajesh

Shamsudheen

et al. 2022

Journal of Healthcare Engineering

View full text Add to dashboard Cite

Radiology is a broad subject that needs more knowledge and understanding of medical science to identify tumors accurately. The need for a tumor detection program, thus, overcomes the lack of qualified radiologists. Using magnetic resonance imaging, biomedical image processing makes it easier to detect and locate brain tumors. In this study, a segmentation and detection method for brain tumors was developed using images from the MRI sequence as an input image to identify the tumor area. This process is difficult due to the wide variety of tumor tissues in the presence of different patients, and, in most cases, the similarity within normal tissues makes the task difficult. The main goal is to classify the brain in the presence of a brain tumor or a healthy brain. The proposed system has been researched based on Berkeley’s wavelet transformation (BWT) and deep learning classifier to improve performance and simplify the process of medical image segmentation. Significant features are extracted from each segmented tissue using the gray-level-co-occurrence matrix (GLCM) method, followed by a feature optimization using a genetic algorithm. The innovative final result of the approach implemented was assessed based on accuracy, sensitivity, specificity, coefficient of dice, Jaccard’s coefficient, spatial overlap, AVME, and FoM.

show abstract

Section: Methodsmentioning

confidence: 99%

Brain Tumor Detection and Classification by MRI Using Biologically Inspired Orthogonal Wavelet Transform and Deep Learning Techniques

Arif

Ajesh

Shamsudheen

et al. 2022

Journal of Healthcare Engineering

View full text Add to dashboard Cite

show abstract

“…The information extracted from these images is used to train and test the classifiers. A machine learning-based approach is presented for brain image classification and brain structure analysis [24]. First, Discrete Wavelet Transform (DWT) is applied to break down the tumor image.…”

Section: Related Workmentioning

confidence: 99%

A Two-Tier Framework Based on GoogLeNet and YOLOv3 Models for Tumor Detection in MRI

Ali¹,

Khan²,

Abbas³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Medical Image Analysis (MIA) is one of the active research areas in computer vision, where brain tumor detection is the most investigated domain among researchers due to its deadly nature. Brain tumor detection in magnetic resonance imaging (MRI) assists radiologists for better analysis about the exact size and location of the tumor. However, the existing systems may not efficiently classify the human brain tumors with significantly higher accuracies. In addition, smart and easily implementable approaches are unavailable in 2D and 3D medical images, which is the main problem in detecting the tumor. In this paper, we investigate various deep learning models for the detection and localization of the tumor in MRI. A novel twotier framework is proposed where the first tire classifies normal and tumor MRI followed by tumor regions localization in the second tire. Furthermore, in this paper, we introduce a well-annotated dataset comprised of tumor and normal images. The experimental results demonstrate the effectiveness of the proposed framework by achieving 97% accuracy using GoogLeNet on the proposed dataset for classification and 83% for localization tasks after finetuning the pre-trained you only look once (YOLO) v3 model.

show abstract

“…The results of SVM for the feel evaluation strategies were evaluated using statistical evaluation and receiver operating feature (ROC) analysis. Veeramuthua et al [24] had evolved a Computer Aided Diagnosis (CAD) to examine the shape of the brain pics. They had used multi-stage DWT to decomposed mind snap shots and then extracted image capabilities.…”

Section: B Brain Tumor Classification Algorithmsmentioning

confidence: 99%

Diagnosis and Classification of Brain Tumor

Singh*,

Prakash

2019

IJEAT

View full text Add to dashboard Cite

The aim of this paper is to provide an outline on cerebrum (brain) tumor diagnoses for folks that are new to virtual medical image processing. The strange improvement of cells within the brain is brain Tumor. Detection of brain tumor at early stage is feasible with image processing and the development of device learning. For early detection of extraordinary adjustments in tumor cells, Computer tomography, Magnetic resonance imaging strategies are used. Early detection and identification of tumor is the handiest manner to get remedy. Brain tumor is classed into two kinds benign and malignant tumor. Various imaging processing strategies had been proposed in latest couple of years for detection and class of brain tumor. Automatic segmentation method the usage of clustering and convolution neural community gives nice consequences. The PCA has been used for reducing the features from the segmented area gives superb outcomes compared to other techniques. For classification of brain tumors diverse algorithms any such Support Vector Machine, Artificial Neural Network, K-Nearest Neighbor are reviewed. These strategies correctly paintings on CT and MRI images

show abstract

Brain Image Classification Using Learning Machine Approach and Brain Structure Analysis

Cited by 28 publications

References 5 publications

Brain Tumor Detection and Classification by MRI Using Biologically Inspired Orthogonal Wavelet Transform and Deep Learning Techniques

Brain Tumor Detection and Classification by MRI Using Biologically Inspired Orthogonal Wavelet Transform and Deep Learning Techniques

A Two-Tier Framework Based on GoogLeNet and YOLOv3 Models for Tumor Detection in MRI

Diagnosis and Classification of Brain Tumor

Contact Info

Product

Resources

About