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 Development of brain tumor segmentation of magnetic resonance imaging (MRI) using U-Net deep learning

2021 ◽  
Vol 4 (9(112)) ◽  
pp. 23-31
Author(s):  
Wasan M. Jwaid ◽  
Zainab Shaker Matar Al-Husseini ◽  
Ahmad H. Sabry
Keyword(s):  
Magnetic Resonance Imaging ◽  
Deep Learning ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Tumor Segmentation ◽  
Resonance Imaging ◽  
Brain Tumor Segmentation ◽  
Brain Images ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain

Brain tumors are the growth of abnormal cells or a mass in a brain. Numerous kinds of brain tumors were discovered, which need accurate and early detection techniques. Currently, most diagnosis and detection methods rely on the decision of neuro-specialists and radiologists to evaluate brain images, which may be time-consuming and cause human errors. This paper proposes a robust U-Net deep learning Convolutional Neural Network (CNN) model that can classify if the subject has a tumor or not based on Brain Magnetic resonance imaging (MRI) with acceptable accuracy for medical-grade application. The study built and trained the 3D U-Net CNN including encoding/decoding relationship architecture to perform the brain tumor segmentation because it requires fewer training images and provides more precise segmentation. The algorithm consists of three parts; the first part, the downsampling part, the bottleneck part, and the optimum part. The resultant semantic maps are inserted into the decoder fraction to obtain the full-resolution probability maps. The developed U-Net architecture has been applied on the MRI scan brain tumor segmentation dataset in MICCAI BraTS 2017. The results using Matlab-based toolbox indicate that the proposed architecture has been successfully evaluated and experienced for MRI datasets of brain tumor segmentation including 336 images as training data and 125 images for validation. This work demonstrated comparative performance and successful feasibility of implementing U-Net CNN architecture in an automated framework of brain tumor segmentations in Fluid-attenuated inversion recovery (FLAIR) MR Slices. The developed U-Net CNN model succeeded in performing the brain tumor segmentation task to classify the input brain images into a tumor or not based on the MRI dataset.

scholarly journals Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ramin Ranjbarzadeh ◽  
Abbas Bagherian Kasgari ◽  
Saeid Jafarzadeh Ghoushchi ◽  
Shokofeh Anari ◽  
Maryam Naseri ◽  
...  
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Imaging Modality ◽  
Complex Structure ◽  
Computing Time ◽  
Tumor Segmentation ◽  
Global Features ◽  
Brain Tumor Segmentation ◽  
Brain Images ◽  
The Brain

AbstractBrain tumor localization and segmentation from magnetic resonance imaging (MRI) are hard and important tasks for several applications in the field of medical analysis. As each brain imaging modality gives unique and key details related to each part of the tumor, many recent approaches used four modalities T1, T1c, T2, and FLAIR. Although many of them obtained a promising segmentation result on the BRATS 2018 dataset, they suffer from a complex structure that needs more time to train and test. So, in this paper, to obtain a flexible and effective brain tumor segmentation system, first, we propose a preprocessing approach to work only on a small part of the image rather than the whole part of the image. This method leads to a decrease in computing time and overcomes the overfitting problems in a Cascade Deep Learning model. In the second step, as we are dealing with a smaller part of brain images in each slice, a simple and efficient Cascade Convolutional Neural Network (C-ConvNet/C-CNN) is proposed. This C-CNN model mines both local and global features in two different routes. Also, to improve the brain tumor segmentation accuracy compared with the state-of-the-art models, a novel Distance-Wise Attention (DWA) mechanism is introduced. The DWA mechanism considers the effect of the center location of the tumor and the brain inside the model. Comprehensive experiments are conducted on the BRATS 2018 dataset and show that the proposed model obtains competitive results: the proposed method achieves a mean whole tumor, enhancing tumor, and tumor core dice scores of 0.9203, 0.9113 and 0.8726 respectively. Other quantitative and qualitative assessments are presented and discussed.

scholarly journals Application of Deep Learning and Brain Images in Diagnosis of Alzheimer’s Patients

2021 ◽  
Vol 38 (5) ◽  
pp. 1431-1438
Author(s):  
Yu Jiang
Keyword(s):  
Cerebrospinal Fluid ◽  
Deep Learning ◽  
Generation Process ◽  
Image Block ◽  
Brain Images ◽  
Image Preprocessing ◽  
Brain Image ◽  
Registration Process ◽  
Feature Information ◽  
The Brain

In the identification of which stages Alzheimer’s patients are in, the application of the medical imaging technology helps doctors give more accurate qualitative diagnoses. However, the existing research results are not effective enough in the acquisition of valuable information from medical images, nor can they make full use of other modal images that highlight different feature information. To this end, this paper studies the application of deep learning and brain images in the diagnosis of Alzheimer’s patients. First, the image preprocessing operations and the brain image registration process were explained in detail. Then, the image block generation process was given, and the degrees of membership to white matter, gray matter and cerebrospinal fluid were calculated, and the brain images were also preliminarily classified. Finally, a complete auxiliary diagnosis process for Alzheimer’s disease based on deep learning was provided, an improved sparse noise reduction auto-encoder network was constructed, and the brain image recognition and classification based on deep learning were completed. The experimental results verified the effectiveness of the constructed model.

scholarly journals Brain Tumor Detection and Classification in Magnetic Resonance Imaging (MRI) using Region Growing, Fuzzy Symmetric Measure, and Artificial Neural Network Backpropagation

2016 ◽  
Vol 1 (1) ◽  
Author(s):  
Lugina Muhammad ◽  
Retno Novi Dayawanti ◽  
Rita Rismala
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Region Growing ◽  
Seed Point ◽  
Cad System ◽  
Average Accuracy ◽  
Symmetric Measure ◽  
The Brain

<p>Brain tumor is one type of malignant tumors that occurs because there is an abnormal and uncontrolled cell division activity. There are several ways to diagnose brain tumors, for example use MRI images. Through the MRI images, the radiologist can see the brain anatomy without performing surgery. However, this process is still done manually and could lead to misdiagnose. In addition, the different characteristics of brain tumor makes the diagnose more difficult. Therefore, we need a system of Computer-Aided Diagnostic (CAD) that will help radiologist in identifying brain tumors. </p><p>In general, the CAD system consists of two major processes, namely image segmentation and feature extraction and classification. One example of segmentation is Region Growing that will classify the pixels based on certain criteria. However, the manual selection of seed point is a drawback of this method. The examples of feature extraction methods are Fuzzy Symmetric Measure (FSM), and First and Second Order Statistics. FSM values can be used to calculate the symmetry of the image brain, while the first and second order to represent feature in the image. As for the classification process, Artificial Neural Network Backpropagation method is widely used for its ability to resolve nonlinear dan complex problems.</p><p>This research implements CAD system that uses Region Growing, Symmetric Fuzzy Measure, and Backpropagation Neural Network for detecting and classifying the brain tumors. In addition, the modification of converging square is conducted to select a seed point automatically. After testing, the system generates a 100% accuracy and BER is 0 in the case of distinguishing between normal and tumor brain. Besides, the average accuracy in classifying the types of brain tumors achieved 89.72% , the BER 0.1 for training data, and the average accuracy of 84.44%, BER 0.16 for the testing data.</p>

scholarly journals Localization and Classification of Brain Tumor using Machine Learning & Deep Learning Techniques

2019 ◽  
Vol 8 (9S) ◽  
pp. 59-66
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Brain Tumor ◽  
State Of The Art ◽  
Body Part ◽  
X Rays ◽  
Meaningful Information ◽  
Learning Techniques ◽  
The Brain

Digital image processing is a rising field for the investigation of complicated diseases such as brain tumor, breast cancer, kidney stones, lung cancer, ovarian cancer, and cervix cancer and so on. The recognition of the brain tumor is considered to be a very critical task. A number of approaches are used for the scanning of a particular body part like CT scan, X-rays, and Magnetic Resonance Image (MRI). These pictures are then examined by the surgeons for the removal of the problem. The main objective of examining these MRI images (mainly) is to extract the meaningful information with high accuracy. Machine Learning and Deep Learning algorithms are mainly used for analysing the medical images which can identify, localize and classify the brain tumor into sub categories, according to which the diagnosis would be done by the professionals. In this paper, we have discussed the different techniques that are used for tumor pre-processing, segmentation, localization, extraction of features and classification and summarize more than 30 contributions to this field. Also, we discussed the existing state-of-the-art, literature gaps, open challenges and future scope in this area.

scholarly journals Deep Learning Approach for Brain Tumor Classification

2021 ◽  
Vol 9 (VI) ◽  
pp. 3094-3098
Author(s):  
Saudagar Punam
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Tumor Detection ◽  
Detection Methods ◽  
Brain Images ◽  
Abnormal Growth ◽  
Images Processing ◽  
Learning Architectures ◽  
The Brain ◽  
Complicated Task

Tumors are complex. There are a lot of variations in sizes and location of tumor. This makes it really hard for complete understanding of tumor. Brain tumour is the abnormal growth of cells inside the brain cranium which limits the functioning of brain. Now a days, medical images processing is a most challenging and developing field. Automated detection of tumor in MRI is extremely crucial because it provides information about abnormal tissues which is important for planning treatment. The conventional method for defect detection in resonance brain images is time consuming. So, automated tumor detection methods are developed because it would save radiologist time and acquire a tested accuracy. The MRI brain tumor detection is complicated task due to complexity and variance of tumors.There are many previously implemented approaches on detecting these kinds of brain tumors. In this paper, we used and implement Convolutional Neural Network (CNN) which is one among the foremost widely used deep learning architectures for classifying a brain tumor into four types. i.e Glioma , Meningioma, Pituitary and No tumour. CNN may be used to effectively locate most cancers cells in brain via MRI. classification.

scholarly journals Image Analysis for MRI-Based Brain Tumor Classification Using Deep Learning

2021 ◽  
Vol 5 (1) ◽  
pp. 21
Author(s):  
Krisna Nuresa Qodri ◽  
Indah Soesanti ◽  
Hanung Adi Nugroho
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Transfer Learning ◽  
Medical Personnel ◽  
The United States ◽  
Brain Images ◽  
Training And Research ◽  
The Brain

Tumors are cells that grow abnormally and uncontrollably, whereas brain tumors are abnormally growing cells growing in or near the brain. It is estimated that 23,890 adults (13,590 males and 10,300 females) in the United States and 3,540 children under the age of 15 would be diagnosed with a brain tumor. Meanwhile, there are over 250 cases in Indonesia of patients afflicted with brain tumors, both adults and infants. The doctor or medical personnel usually conducted a radiological test that commonly performed using magnetic resonance image (MRI) to identify the brain tumor. From several studies, each researcher claims that the results of their proposed method can detect brain tumors with high accuracy; however, there are still flaws in their methods. This paper will discuss the classification of MRI-based brain tumors using deep learning and transfer learning. Transfer learning allows for various domains, functions, and distributions used in training and research. This research used a public dataset. The dataset comprises 253 images, divided into 98 tumor-free brain images and 155 tumor images. Residual Network (ResNet), Neural Architecture Search Network (NASNet), Xception, DenseNet, and Visual Geometry Group (VGG) are the techniques that will use in this paper. The results got to show that the ResNet50 model gets 96% for the accuracy, and VGG16 gets 96% for the accuracy. The results obtained indicate that transfer learning can handle medical images.

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.

scholarly journals Deep Learning for Brain Tumor Segmentation: A Survey of State-of-the-Art

2021 ◽  
Vol 7 (2) ◽  
pp. 19
Author(s):  
Tirivangani Magadza ◽  
Serestina Viriri
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Brain Tumor ◽  
Quantitative Analysis ◽  
Medical Image ◽  
State Of The Art ◽  
Medical Image Analysis ◽  
Building Blocks ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation

Quantitative analysis of the brain tumors provides valuable information for understanding the tumor characteristics and treatment planning better. The accurate segmentation of lesions requires more than one image modalities with varying contrasts. As a result, manual segmentation, which is arguably the most accurate segmentation method, would be impractical for more extensive studies. Deep learning has recently emerged as a solution for quantitative analysis due to its record-shattering performance. However, medical image analysis has its unique challenges. This paper presents a review of state-of-the-art deep learning methods for brain tumor segmentation, clearly highlighting their building blocks and various strategies. We end with a critical discussion of open challenges in medical image analysis.

scholarly journals Particle Size Estimation in Mixed Commercial Waste Images Using Deep Learning

2021 ◽  
Author(s):  
Phongsathorn Kittiworapanya ◽  
Kitsuchart Pasupa ◽  
Peter Auer
Keyword(s):  
Computer Vision ◽  
Particle Size ◽  
Deep Learning ◽  
Waste Management ◽  
State Of The Art ◽  
Learning Algorithms ◽  
Input Image ◽  
Size Estimation ◽  
Waste Particles ◽  
Set Up

<div>We assessed several state-of-the-art deep learning algorithms and computer vision techniques for estimating the particle size of mixed commercial waste from images. In waste management, the first step is often coarse shredding, using the particle size to set up the shredder machine. The difficulty is separating the waste particles in an image, which can not be performed well. This work focused on estimating size by using the texture from the input image, captured at a fixed height from the camera lens to the ground. We found that EfficientNet achieved the best performance of 0.72 on F1-Score and 75.89% on accuracy.<br></div>

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