scholarly journals MRI Brain Classification Using the Quantum Entropy LBP and Deep-Learning-Based Features

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 1033
Author(s):  
Ali M. Hasan ◽  
Hamid A. Jalab ◽  
Rabha W. Ibrahim ◽  
Farid Meziane ◽  
Ala’a R. AL-Shamasneh ◽  
...  
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Short Term Memory ◽  
Local Binary Patterns ◽  
Quantum Entropy ◽  
Brain Scans ◽  
Quantum Calculus ◽  
Mri Brain ◽  
Brain Scan ◽  
The Brain

Brain tumor detection at early stages can increase the chances of the patient’s recovery after treatment. In the last decade, we have noticed a substantial development in the medical imaging technologies, and they are now becoming an integral part in the diagnosis and treatment processes. In this study, we generalize the concept of entropy difference defined in terms of Marsaglia formula (usually used to describe two different figures, statues, etc.) by using the quantum calculus. Then we employ the result to extend the local binary patterns (LBP) to get the quantum entropy LBP (QELBP). The proposed study consists of two approaches of features extractions of MRI brain scans, namely, the QELBP and the deep learning DL features. The classification of MRI brain scan is improved by exploiting the excellent performance of the QELBP–DL feature extraction of the brain in MRI brain scans. The combining all of the extracted features increase the classification accuracy of long short-term memory network when using it as the brain tumor classifier. The maximum accuracy achieved for classifying a dataset comprising 154 MRI brain scan is 98.80%. The experimental results demonstrate that combining the extracted features improves the performance of MRI brain tumor classification.

scholarly journals Segmentation of MRI Brain Tumor Image using Optimization based Deep Convolutional Neural networks (DCNN)

2021 ◽  
Vol 11 (1) ◽  
pp. 380-390
Author(s):  
Pradipta Kumar Mishra ◽  
Suresh Chandra Satapathy ◽  
Minakhi Rout
Keyword(s):  
Brain Tumor ◽  
Tumor Segmentation ◽  
Gray Wolf ◽  
Deep Convolutional Neural Networks ◽  
Brain Tumor Segmentation ◽  
Swarm Optimization ◽  
Brain Image ◽  
Mri Brain ◽  
Whale Optimization ◽  
The Brain

Abstract Segmentation of brain image should be done accurately as it can help to predict deadly brain tumor disease so that it can be possible to control the malicious segments of brain image if known beforehand. The accuracy of the brain tumor analysis can be enhanced through the brain tumor segmentation procedure. Earlier DCNN models do not consider the weights as of learning instances which may decrease accuracy levels of the segmentation procedure. Considering the above point, we have suggested a framework for optimizing the network parameters such as weight and bias vector of DCNN models using swarm intelligent based algorithms like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gray Wolf Optimization (GWO) and Whale Optimization Algorithm (WOA). The simulation results reveals that the WOA optimized DCNN segmentation model is outperformed than other three optimization based DCNN models i.e., GA-DCNN, PSO-DCNN, GWO-DCNN.

The Brain Tumor Segmentation Using Fuzzy C-Means Technique

2018 ◽  
pp. 2402-2419
Author(s):  
Jyotsna Rani ◽  
Ram Kumar ◽  
Fazal A. Talukdar ◽  
Nilanjan Dey
Keyword(s):  
Image Segmentation ◽  
Brain Tumor ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Fuzzy C Means ◽  
Mri Brain ◽  
Area Of Interest ◽  
Fuzzy C Means Clustering ◽  
Comparative Results ◽  
The Brain

Image segmentation is a technique which divides an image into its constituent regions or objects. Segmentation continues till we reach our area of interest or the specified object of target. This field offers vast future scope and challenges for the researchers. This proposal uses the fuzzy c mean technique to segment the different MRI brain tumor images. This proposal also shows the comparative results of Thresholding, K-means clustering and Fuzzy c- means clustering. Dice coefficient and Jaccards measure is used for accuracy of the segmentation in this proposal. Experimental results demonstrate the performance of the designed method.

A Study on Brain Tumor Detection and Segmentation Using Deep Learning Techniques

2020 ◽  
Vol 17 (4) ◽  
pp. 1925-1930
Author(s):  
Ambeshwar Kumar ◽  
R. Manikandan ◽  
Robbi Rahim
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Brain Tumor ◽  
Tumor Detection ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques ◽  
Convolution Algorithm ◽  
The Brain

It’s a new era technology in the field of medical engineering giving awareness about the various healthcare features. Deep learning is a part of machine learning, it is capable of handling high dimensional data and is efficient in concentrating on the right features. Tumor is an unbelievably complex disease: a multifaceted cell has more than hundred billion cells; each cell acquires mutation exclusively. Detection of tumor particles in experiment is easily done by MRI or CT. Brain tumors can also be detected by MRI, however, deep learning techniques give a better approach to segment the brain tumor images. Deep Learning models are imprecisely encouraged by information handling and communication designs in biological nervous system. Classification plays an significant role in brain tumor detection. Neural network is creating a well-organized rule for classification. To accomplish medical image data, neural network is trained to use the Convolution algorithm. Multilayer perceptron is intended for identification of a image. In this study article, the brain images are categorized into two types: normal and abnormal. This article emphasize the importance of classification and feature selection approach for predicting the brain tumor. This classification is done by machine learning techniques like Artificial Neural Networks, Support Vector Machine and Deep Neural Network. It could be noted that more than one technique can be applied for the segmentation of tumor. The several samples of brain tumor images are classified using deep learning algorithms, convolution neural network and multi-layer perceptron.

scholarly journals God Spots in the Brain: Nine Categories of Unasked, Unanswered Questions

Religions ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 468 ◽  
Author(s):  
W. R. Klemm
Keyword(s):  
Academic Discipline ◽  
Functional Anatomy ◽  
Future Research ◽  
Brain Scans ◽  
Brain Scan ◽  
Network Operations ◽  
Religious Thoughts ◽  
Self Learning ◽  
The Brain

Neurotheology is an emerging academic discipline that examines mind-brain relationships in terms of the inter-relatedness of neuroscience, spirituality, and religion. Neurotheology originated from brain-scan studies that revealed specific correlations between certain religious thoughts and localized activated brain areas known as “God Spots.” This relatively young scholarly discipline lacks clear consensus on its definition, ideology, purpose, or prospects for future research. Of special interest is the consideration of the next steps using brain scans to develop this field of research. This review proposes nine categories of future research that could build on the foundation laid by the prior discoveries of God Spots. Specifically, this analysis identifies some sparsely addressed issues that could be usefully explored with new kinds of brain-scan studies: neural network operations, the cognitive neuroscience of prayer, biology of belief, measures of religiosity, role of the self, learning and memory, religious and secular cognitive commonalities, static and functional anatomy, and recruitment of neural processing circuitry. God Spot research is poised to move beyond observation to robust hypothesis generation and testing.

scholarly journals MRI-based brain tumor segmentation using FPGA-accelerated neural network

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Siyu Xiong ◽  
Guoqing Wu ◽  
Xitian Fan ◽  
Xuan Feng ◽  
Zhongcheng Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Energy Efficiency ◽  
Deep Learning ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Cad Systems ◽  
Segmentation Accuracy ◽  
The Brain

Abstract Background Brain tumor segmentation is a challenging problem in medical image processing and analysis. It is a very time-consuming and error-prone task. In order to reduce the burden on physicians and improve the segmentation accuracy, the computer-aided detection (CAD) systems need to be developed. Due to the powerful feature learning ability of the deep learning technology, many deep learning-based methods have been applied to the brain tumor segmentation CAD systems and achieved satisfactory accuracy. However, deep learning neural networks have high computational complexity, and the brain tumor segmentation process consumes significant time. Therefore, in order to achieve the high segmentation accuracy of brain tumors and obtain the segmentation results efficiently, it is very demanding to speed up the segmentation process of brain tumors. Results Compared with traditional computing platforms, the proposed FPGA accelerator has greatly improved the speed and the power consumption. Based on the BraTS19 and BraTS20 dataset, our FPGA-based brain tumor segmentation accelerator is 5.21 and 44.47 times faster than the TITAN V GPU and the Xeon CPU. In addition, by comparing energy efficiency, our design can achieve 11.22 and 82.33 times energy efficiency than GPU and CPU, respectively. Conclusion We quantize and retrain the neural network for brain tumor segmentation and merge batch normalization layers to reduce the parameter size and computational complexity. The FPGA-based brain tumor segmentation accelerator is designed to map the quantized neural network model. The accelerator can increase the segmentation speed and reduce the power consumption on the basis of ensuring high accuracy which provides a new direction for the automatic segmentation and remote diagnosis of brain tumors.

scholarly journals Recognization and Systematization of MR Images using K Means Clustering and DNN

2020 ◽  
Vol 9 (6) ◽  
pp. 924-927
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Digital Health ◽  
High Accuracy ◽  
Tumor Segmentation ◽  
Mr Images ◽  
Brain Tumor Segmentation ◽  
Brain Tumor Diagnosis ◽  
The Brain

Brain tumors are the result of unusual growth and unrestrained cell disunity in the brain. Most of the medical image application lack in segmentation and labeling. Brain tumors can lead to loss of lives if they are not detected early and correctly. Recently, deep learning has been an important role in the field of digital health. One of its action is the reduction of manual decision in the diagnosis of diseases specifically brain tumor diagnosis needs high accuracy, where minute errors in judgment may lead to loss therefore, brain tumor segmentation is an necessary challenge in medical side. In recent time numerous ,methods exist for tumor segmentation with lack of accuracy. Deep learning is used to achieve the goal of brain tumor segmentation. In this work, three network of brain MR images segmentation is employed .A single network is compared to achieve segmentation of MR images using separate network .In this paper segmentation has improved and result is obtained with high accuracy and efficiency.

scholarly journals Enhanced Region Growing for Brain Tumor MR Image Segmentation

2021 ◽  
Vol 7 (2) ◽  
pp. 22
Author(s):  
Erena Siyoum Biratu ◽  
Friedhelm Schwenker ◽  
Taye Girma Debelee ◽  
Samuel Rahimeto Kebede ◽  
Worku Gachena Negera ◽  
...  
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
State Of The Art ◽  
Learning Algorithms ◽  
Region Growing ◽  
Developed Countries ◽  
Brain Images ◽  
Seed Point ◽  
Brain Image ◽  
The Brain

A brain tumor is one of the foremost reasons for the rise in mortality among children and adults. A brain tumor is a mass of tissue that propagates out of control of the normal forces that regulate growth inside the brain. A brain tumor appears when one type of cell changes from its normal characteristics and grows and multiplies abnormally. The unusual growth of cells within the brain or inside the skull, which can be cancerous or non-cancerous has been the reason for the death of adults in developed countries and children in under developing countries like Ethiopia. The studies have shown that the region growing algorithm initializes the seed point either manually or semi-manually which as a result affects the segmentation result. However, in this paper, we proposed an enhanced region-growing algorithm for the automatic seed point initialization. The proposed approach’s performance was compared with the state-of-the-art deep learning algorithms using the common dataset, BRATS2015. In the proposed approach, we applied a thresholding technique to strip the skull from each input brain image. After the skull is stripped the brain image is divided into 8 blocks. Then, for each block, we computed the mean intensities and from which the five blocks with maximum mean intensities were selected out of the eight blocks. Next, the five maximum mean intensities were used as a seed point for the region growing algorithm separately and obtained five different regions of interest (ROIs) for each skull stripped input brain image. The five ROIs generated using the proposed approach were evaluated using dice similarity score (DSS), intersection over union (IoU), and accuracy (Acc) against the ground truth (GT), and the best region of interest is selected as a final ROI. Finally, the final ROI was compared with different state-of-the-art deep learning algorithms and region-based segmentation algorithms in terms of DSS. Our proposed approach was validated in three different experimental setups. In the first experimental setup where 15 randomly selected brain images were used for testing and achieved a DSS value of 0.89. In the second and third experimental setups, the proposed approach scored a DSS value of 0.90 and 0.80 for 12 randomly selected and 800 brain images respectively. The average DSS value for the three experimental setups was 0.86.

scholarly journals Deep Learning in Oncology- A Case Study on Brain Tumor

2018 ◽  
Vol 3 (1) ◽  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Brain Tumor ◽  
Early Detection ◽  
Error Rate ◽  
High Capacity ◽  
Tumor Detection ◽  
Tumor Diagnosis ◽  
The Brain

The brain tumor detection continues to be a challenge owing to the complexity of its symptoms. The research era indicates the tumor diagnosis and identification of tumor exact indicators are still uncertain. These tumors can appear anywhere in the brain and have any kind of shape, size, and contrast. The brain tumor exploration with deep learning is a solution for flexible, high capacity and extreme efficiency. The deep learning is an application of the artificial intelligence with multiple layers helping to predict the outcome of the disease early detection. This paper presents an approach to recognize the indicators and show that deep learning drops error rate for brain tumor diagnoses by 80%.

scholarly journals Development and Porting of Mathematical Model to Design a PixTra Algorithm for Detection of Brain Tumor

2019 ◽  
Vol 9 (1) ◽  
pp. 2030-2032
Keyword(s):  
Mathematical Modeling ◽  
Game Theory ◽  
Deep Learning ◽  
Brain Tumor ◽  
Applied Mathematics ◽  
Learning Technology ◽  
Strategic Development ◽  
The Game Theory ◽  
Strategic Solution ◽  
The Brain

The goal of applied mathematics is to develop and implement the automated systems, services and support to execute engineering goals. Game theory is always a domain of choice for development of strategy to enhance the usability of mathematical applications. Today, there is rarely any system which works without keeping mathematics behind the curtain. A recent trend in medical imaging leads via mathematical algorithmic development for brain tumor analysis. The deep learning technology is a mathematical strategic solution to identify the brain tumor from medical resonance imaging. The game theory strategic development is necessary for mathematical modeling of image technologies as pixel level calculations are required. Hence, this paper shows the newly developed mathematical modeling for efficient brain tumor detection using game theory and set theory. This paper also suggests the new algorithm which is ported from mathematical modeling for deep learning brain tumor image processing.

Sign in / Sign up

Export Citation Format

Share Document