scholarly journals Brain Tumor Detection and Segmentation: A Survey

2020 ◽  
Vol 10 (4) ◽  
pp. 55
Author(s):  
Kalifa Shantta ◽  
Otman Basir
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Survival Rates ◽  
Automatic Detection ◽  
Tumor Detection ◽  
Detection Techniques ◽  
Pros And Cons ◽  
Accuracy Performance ◽  
Image Modality

<p class="Abstract">Even with the enormous progress in medical technology, brain tumor detection is still an extremely tedious and complex task for the physicians. The early and accurate detection of brain tumors enables effective and efficient therapy and thus can result in increased survival rates. Automatic detection and classification of brain tumors have the potential to achieve efficiency and a higher degree of predictable accuracy. However, it is well established that the accuracy performance of automatic detection and classification techniques varies from technique to technique, and tends to be image modality dependent. This paper reviews the state-of-the-art detection techniques and highlights their pros and cons.</p>

scholarly journals Accurate Brain Tumor Recognition Using Double-Weighted Feature Extraction Labelling Model with Priority Weighted Feature Selection

2021 ◽  
Vol 38 (5) ◽  
pp. 1377-1383
Author(s):  
Revathi Vankayalapati ◽  
Akka Lakshmi Muddana
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Survival Rates ◽  
Prediction Method ◽  
Tumor Detection ◽  
Brain Images ◽  
Tumor Recognition

In clinical practice and patient survival rates, early diagnosis of brain tumors plays a key role. Different forms of brain tumors and their properties and treatments are available. Therefore, tumor detection is complicated, time consuming and error-prone with manual brain tumor detection. Therefore, high-precision automated, computerized diagnostics are currently necessary. Feature extraction is a tumor prediction method for capturing the visual content of a picture. The extraction of features is the process through which the raw image is reduced and decisions like the pattern classification are facilitated. The MRI brain images are considered to be classified as a robust and more accurate classification that is able to serve as an expert assistant for healthcare practitioners. In this research, a new method for selecting and extracting features is introduced. The paper proposes to take into account the most important features for the classification of tumor and non-tumor cells using a Double-Weighted Feature Extraction Labelling Model with Priority Weighted Feature Selection (DWLM-PWFS). This approach combines the tumor's intensity, texture, shape and diagnostic properties. The selection of features with the technique proposed is most helpful for analyzing data according to grouping class variable and ensuring reduced feature setting with high classification accuracy. In contrast to the conventional model, the model proposed is shown to be highly efficient in comparison with traditional models.

scholarly journals Brain Tumor Diagnosis Support System: A Decision Fusion Framework

2020 ◽  
Vol 15 (3) ◽  
pp. 30
Author(s):  
Kalifa Shantta ◽  
Otman Basir
Keyword(s):  
Brain Tumors ◽  
Human Error ◽  
Survival Rates ◽  
Automatic Detection ◽  
Decision Fusion ◽  
High Accuracy ◽  
Classification Performance ◽  
Multiple Tumor ◽  
Detection And Diagnosis ◽  
Accuracy Performance

The early and accurate detection of brain tumors is important in providing effective and efficient therapy and thus can result in increased survival rates.  Current image-based tumor detection and diagnosis methods depend heavily on the interpretation of the neuro specialists and/or radiologists.  Therefore, it is quite possible for the interpretation process to be time-consuming, and prone to human error and subjectivity. Automatic detection and classification of brain tumors have the potential to achieve efficiency and higher degree of predictable accuracy. However, it is well established that the accuracy performance of automatic detection and classification techniques varies from technique to technique, and tends to be image modality dependent. Thus, it is prudent to explore the variability in the performance of these techniques as a means to achieve consistent high accuracy performance. This paper presents a framework for fusing multiple tumor classifiers. The fusion process is based on the Dempster Shafer evidence fusion theory. Several tumor classifiers are employed. Experimental results will be presented to validate the efficiency of the proposed framework. It is concluded that fusing the classification decisions made by the various classifiers it is conceivable that efficient and consistent high accuracy classification performance can be attained.

scholarly journals Brain Tumor Detection Based on Features Extracted and Classified Using a Low-Complexity Neural Network

2021 ◽  
Vol 38 (3) ◽  
pp. 547-554
Author(s):  
Vasileios Papageorgiou
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Low Complexity ◽  
Automatic Classification ◽  
Tumor Detection ◽  
Cross Entropy ◽  
Testing Accuracy ◽  
Jensen Shannon Divergence

Brain tumor detection or brain tumor classification is one of the most challenging problems in modern medicine, where patients suffering from benign or malignant brain tumors are usually characterized by low life expectancy making the necessity of a punctual and accurate diagnosis mandatory. However, even today, this kind of diagnosis is based on manual classification of magnetic resonance imaging (MRI), culminating in inaccurate conclusions especially when they derive from inexperienced doctors. Hence, trusted, automatic classification schemes are essential for the reduction of humans’ death rate due to this major chronic disease. In this article, we propose an automatic classification tool, using a computationally economic convolutional neural network (CNN), for the purposes of a binary problem concerning MRI images depicting the existence or the absence of brain tumors. The proposed model is based on a dataset containing real MRI images of both classes with nearly perfect validation-testing accuracy and low computational complexity, resulting a very fast and reliable training-validation process. During our analysis we compare the diagnostic capacity of three alternative loss functions, validating the appropriateness of cross entropy function, while underlining the capability of an alternative loss function named Jensen-Shannon divergence since our model accomplished nearly excellent testing accuracy, as with cross-entropy. The multiple validation tests applied, enhancing the robustness of the produced results, render this low-complexity CNN structure as an ideal and trustworthy medical aid for the classification of small datasets.

scholarly journals AUTOMATIC SEGMENTATION AND CLASSIFICATION OF BRAIN TUMOR USING DEEP LEARNING

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
K.Ganga Durga Prasad ◽  
A.J.N. Murthy ◽  
G Narasimha ◽  
New Sinha
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Convolutional Neural Networks ◽  
Automatic Segmentation ◽  
Tumor Detection ◽  
Tumor Type ◽  
The Brain

The brain tumors, are the maximum not unusual place and threatening disease, main to a totally quick lifestyles of their maximum grade. Thus, remedy making plans is a key level to enhance the lifestyles of sufferers. Normally, distinct photo strategies which includes CT, MRI and ultrasound photo are used to hit upon the tumor in a brain. on this approach MRI photos are used to diagnose brain tumor guide type of tumor vs non-tumor is a tough challenge for radiologosts. we gift an approach for detection and type of tumors with inside the brain. The computerized brain tumor type could be very hard challenge in brain tumor. In this approach, computerized brain tumor detection is executedwith the aid of usingthe use of Convolutional Neural Networks (CNN) type.Our proposed automation gadgetcould take an MRI and examine it to locate bengin (non-cancerous) or malignant (cancerous).

A Review of Various Machine Learning Techniques for Brain Tumor Detection from MRI Images

2020 ◽  
Vol 16 (8) ◽  
pp. 937-945
Author(s):  
Aaishwarya Sanjay Bajaj ◽  
Usha Chouhan
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Ct Scan ◽  
Imaging Techniques ◽  
Critical Evaluation ◽  
Tumor Detection ◽  
Machine Learning Techniques ◽  
X Rays ◽  
Detection Techniques ◽  
The Brain

Background: This paper endeavors to identify an expedient approach for the detection of the brain tumor in MRI images. The detection of tumor is based on i) review of the machine learning approach for the identification of brain tumor and ii) review of a suitable approach for brain tumor detection. Discussion: This review focuses on different imaging techniques such as X-rays, PET, CT- Scan, and MRI. This survey identifies a different approach with better accuracy for tumor detection. This further includes the image processing method. In most applications, machine learning shows better performance than manual segmentation of the brain tumors from MRI images as it is a difficult and time-consuming task. For fast and better computational results, radiology used a different approach with MRI, CT-scan, X-ray, and PET. Furthermore, summarizing the literature, this paper also provides a critical evaluation of the surveyed literature which reveals new facets of research. Conclusion: The problem faced by the researchers during brain tumor detection techniques and machine learning applications for clinical settings have also been discussed.

scholarly journals Differential Deep Convolutional Neural Network Model for Brain Tumor Classification

2021 ◽  
Vol 11 (3) ◽  
pp. 352
Author(s):  
Isselmou Abd El Kader ◽  
Guizhi Xu ◽  
Zhang Shuai ◽  
Sani Saminu ◽  
Imran Javaid ◽  
...  
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Medical Image ◽  
Resonance Imaging ◽  
Feature Maps ◽  
Deep Cnn

The classification of brain tumors is a difficult task in the field of medical image analysis. Improving algorithms and machine learning technology helps radiologists to easily diagnose the tumor without surgical intervention. In recent years, deep learning techniques have made excellent progress in the field of medical image processing and analysis. However, there are many difficulties in classifying brain tumors using magnetic resonance imaging; first, the difficulty of brain structure and the intertwining of tissues in it; and secondly, the difficulty of classifying brain tumors due to the high density nature of the brain. We propose a differential deep convolutional neural network model (differential deep-CNN) to classify different types of brain tumor, including abnormal and normal magnetic resonance (MR) images. Using differential operators in the differential deep-CNN architecture, we derived the additional differential feature maps in the original CNN feature maps. The derivation process led to an improvement in the performance of the proposed approach in accordance with the results of the evaluation parameters used. The advantage of the differential deep-CNN model is an analysis of a pixel directional pattern of images using contrast calculations and its high ability to classify a large database of images with high accuracy and without technical problems. Therefore, the proposed approach gives an excellent overall performance. To test and train the performance of this model, we used a dataset consisting of 25,000 brain magnetic resonance imaging (MRI) images, which includes abnormal and normal images. The experimental results showed that the proposed model achieved an accuracy of 99.25%. This study demonstrates that the proposed differential deep-CNN model can be used to facilitate the automatic classification of brain tumors.

scholarly journals An improved framework for brain tumor analysis using MRI based on YOLOv2 and convolutional neural network

2021 ◽  
Author(s):  
Muhammad Irfan Sharif ◽  
Jian Ping Li ◽  
Javeria Amin ◽  
Abida Sharif
Keyword(s):  
Brain Tumor ◽  
Tumor Detection ◽  
Entropy Method ◽  
Feature Extraction And Selection ◽  
Tumor Region ◽  
Selection For ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain ◽  
Complicated Task

AbstractBrain tumor is a group of anomalous cells. The brain is enclosed in a more rigid skull. The abnormal cell grows and initiates a tumor. Detection of tumor is a complicated task due to irregular tumor shape. The proposed technique contains four phases, which are lesion enhancement, feature extraction and selection for classification, localization, and segmentation. The magnetic resonance imaging (MRI) images are noisy due to certain factors, such as image acquisition, and fluctuation in magnetic field coil. Therefore, a homomorphic wavelet filer is used for noise reduction. Later, extracted features from inceptionv3 pre-trained model and informative features are selected using a non-dominated sorted genetic algorithm (NSGA). The optimized features are forwarded for classification after which tumor slices are passed to YOLOv2-inceptionv3 model designed for the localization of tumor region such that features are extracted from depth-concatenation (mixed-4) layer of inceptionv3 model and supplied to YOLOv2. The localized images are passed toMcCulloch'sKapur entropy method to segment actual tumor region. Finally, the proposed technique is validated on three benchmark databases BRATS 2018, BRATS 2019, and BRATS 2020 for tumor detection. The proposed method achieved greater than 0.90 prediction scores in localization, segmentation and classification of brain lesions. Moreover, classification and segmentation outcomes are superior as compared to existing methods.

Brain tumor detection & segmented classification of CT-scan and MRI image

2021 ◽  
pp. 290-297
Author(s):  
Sanjay Kumar ◽  
J.N. Singh ◽  
Naresh Kumar
Keyword(s):  
Brain Tumor ◽  
Ct Scan ◽  
Tumor Detection ◽  
Mri Image

scholarly journals PS1 - 176 Where Have All the Fat Cells Gone? A Comparative Analysis of Adiposity Patterns in Childhood Brain Tumor Survivors and Non-Cancer Controls

2016 ◽  
Vol 43 (S4) ◽  
pp. S11-S11 ◽  
Author(s):  
K.W. Wang ◽  
E. Kearsley ◽  
N. Falzone ◽  
A. Fleming ◽  
S. Burrow ◽  
...  
Keyword(s):  
Physical Activity ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Survival Rates ◽  
Visceral Adiposity ◽  
Activity Levels ◽  
Tumor Type ◽  
Technological Advances ◽  
Cardiometabolic Disorders ◽  
Fried Foods

Brain tumors are the most common solid tumors in children in Canada. While technological advances have increased their survival rates, survivors of childhood brain tumors (SCBT) often develop obesity, which can reduce lifespan and quality of life. While adiposity is a known factor for cardiometabolic disorders in the general population, adiposity patterns in SCBT have not been determined. This study aims to investigate how adiposity patterns differ between SCBT and non-cancer controls, and how lifestyle and treatment factors may contribute to these patterns. Methods: Fifty-nine SCBT and 108 non-cancer controls were recruited from the clinics at McMaster Children’s Hospital. Sociodemographic and lifestyle details were collected using standardized tools to assess diet, physical activity, and sleep. Brain tumor type, location and treatment details were obtained from medical records. Total and visceral adiposity were determined by total fat mass (FM) as well as waist-to-hip (WHR) and waist-to-height ratio (WHTR). Results: SCBT have higher total and visceral adiposity, while BMI is similar to controls. Female SCBT who received radiotherapy and/or chemotherapy have higher adiposity. A dietary pattern of white bread and fried foods with low dark bread was positively associated with adiposity. Lower physical activity levels, but not sleep durations, were associated with higher adiposity. Conclusion: SCBT have higher visceral and total adiposity than non-cancer controls. Sex, chemoradiotherapy, high fat diet, and physical inactivity, can contribute to these adiposity patterns. These results provide multiple points of entry to design interventions that reduce adiposity, and may improve long-term outcomes in SCBT.

scholarly journals Classification of Tumors in Brain MRI Images With Hybrid of Global and Local DWT Features using Decision Tree

2019 ◽  
Vol 8 (12) ◽  
pp. 3072-3077
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Decision Tree ◽  
Brain Mri ◽  
Extraction Process ◽  
Discrete Wavelet ◽  
Global Features ◽  
Tumor Identification ◽  
The Brain

Automated brain tumor identification and classification is still an open problem for research in the medical image processing domain. Brain tumor is a bunch of unwanted cells that develop in the brain. This growth of a tumor takes up space within skull and affects the normal functioning of brain. Automated segmentation and detection of brain tumors are important in MRI scan analysis as it provides information about neural architecture of brain and also about abnormal tissues that are extremely necessary to identify appropriate surgical plan. Automating this process is a challenging task as tumor tissues show high diversity in appearance with different patients and also in many cases they tend to appear very similar to the normal tissues. Effective extraction of features that represent the tumor in brain image is the key for better classification. In this paper, we propose a hybrid feature extraction process. In this process, we combine the local and global features of the brain MRI using first by Discrete Wavelet Transformation and then using texture based statistical features by computing Gray Level Co-occurrence Matrix. The extracted combined features are used to construct decision tree for classification of brain tumors in to benign or malignant class.

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