Brain Tumor Detection and Classification by Hybrid CNN-DWA Model Using MR Images.

2021 ◽  
Vol 17 ◽  
Author(s):  
Isselmou Abd El Kader ◽  
Guizhi Xu ◽  
Zhang Shuai ◽  
Sani Saminu
Keyword(s):  
Brain Tumor ◽  
Early Detection ◽  
Hybrid Model ◽  
High Performance ◽  
Tumor Detection ◽  
Classification Model ◽  
Mr Images ◽  
Brain Images ◽  
Mr Brain Images ◽  
Mr Brain

Objective: Medical image processing is an exciting research area. In this paper, we proposed new brain tumor detection and classification model using MR brain images to help the doctors in early detection and classification of the brain tumor with high performance and best Accuracy. Materials: we trained and validated our model using five databases, including BRATS2012, BRATS2013, BRATS2014, BRATS2015, and ISLES-SISS 2015. Methods: The advantage of the hybrid model proposed is its novelty that is used for the first time; our new method is based on a hybrid deep convolution neural network and deep watershed auto-encoder (CNN-DWA) model. The method consists of six phases, first phase is input MR images, second phase is preprocessing using filter and morphology operation, third phase is matrix that represents MR brain images, fourth is applying the hybrid CNN-DWA, fifth is brain tumor classification, and detection, while sixth phase is the performance of the model using five values. Results and Conclusions: The novelty of our hybrid CNN-DWA model showed the best results and high performance with Accuracy around 98% and loss validation 0, 1. Hybrid model can classify and detect the Tumor clearly using MR images; comparing with other models like CNN, DNN, and DWA, we discover that the proposed model performs better than the above-mentioned models. Depending on the better performance of the proposed hybrid model, this helps in developing computer-aided system for early detection of brain tumors and helps the doctors to diagnose the patients better.

scholarly journals Interpolation of the Histogramed MR Brain Images for Resolution Enhancement

2019 ◽  
Vol 8 (11) ◽  
pp. 1253-1256 ◽  
Keyword(s):  
Performance Metrics ◽  
Interpolation Method ◽  
Signal To Noise Ratio ◽  
Resolution Enhancement ◽  
The Body ◽  
Mr Images ◽  
Brain Images ◽  
Method Performance ◽  
Mr Brain Images ◽  
Mr Brain

Magnetic resonance imaging (MRI) is an incredible testing method which provides appropriate anatomical images of the body. For the diagnosis, high resolution MR images are essential to extract the detailed information about the diseases. However, with the measured MR images it’s a challenging issue in extracting the detailed information associated to disease for the posterior analysis or treatment. Usually to improve the resolution of the MR image, histogram equalization process has to be applied. In this paper, interpolation method is applied to improve the resolution of MR brain images for the histogram-ed images. And for the assessment of the skillfulness of introduced method, performance metrics such as peak signal to noise ratio (PSNR) and absolute mean brightness error (AMBE) are measured. The peak of signal for the enhanced images through interpolation will be much better and may have the good variation to the mean brightness error. With this there can be potential to the artificial intelligence for better diagnosis in complex decisive instances

scholarly journals Classification of Alzheimer Condition using MR Brain Images and Inception-Residual Network Model

2021 ◽  
Vol 7 (2) ◽  
pp. 763-766
Author(s):  
Sreelakshmi Shaji ◽  
Nagarajan Ganapathy ◽  
Ramakrishnan Swaminathan
Keyword(s):  
Network Performance ◽  
Neurodegenerative Disorder ◽  
Automated Analysis ◽  
Brain Regions ◽  
Mr Images ◽  
Residual Network ◽  
Brain Images ◽  
Precise Diagnosis ◽  
Mr Brain Images ◽  
Mr Brain

Abstract Alzheimer’s Disease (AD) is an irreversible progressive neurodegenerative disorder. Magnetic Resonance (MR) imaging based deep learning models with visualization capabilities are essential for the precise diagnosis of AD. In this study, an attempt has been made to categorize AD and Healthy Controls (HC) using structural MR images and an Inception-Residual Network (ResNet) model. For this, T1- weighted MR brain images are acquired from a public database. These images are pre-processed and are applied to a two-layer Inception-ResNet-A model. Additionally, Gradient weighted Class Activation Mapping (Grad-CAM) is employed to visualize the significant regions in MR images identified by the model for AD classification. The network performance is validated using standard evaluation metrics. Results demonstrate that the proposed Inception-ResNet model differentiates AD from HC using MR brain images. The model achieves an average recall and precision of 69%. The Grad- CAM visualization identified lateral ventricles in the mid-axial slice as the most discriminative brain regions for AD classification. Thus, the computer aided diagnosis study could be useful in the visualization and automated analysis of AD diagnosis with minimal medical expertise.

scholarly journals Brain Tumor Detection and Classification on MR Images by a Deep Wavelet Auto-Encoder Model

Diagnostics ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1589
Author(s):  
Isselmou Abd El Kader ◽  
Guizhi Xu ◽  
Zhang Shuai ◽  
Sani Saminu ◽  
Imran Javaid ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Medical Image Analysis ◽  
Mr Image ◽  
Brain Images ◽  
Pass Filter ◽  
Learning Techniques ◽  
Proposed Model ◽  
Mr Brain Images ◽  
Mr Brain

The process of diagnosing brain tumors is very complicated for many reasons, including the brain’s synaptic structure, size, and shape. Machine learning techniques are employed to help doctors to detect brain tumor and support their decisions. In recent years, deep learning techniques have made a great achievement in medical image analysis. This paper proposed a deep wavelet autoencoder model named “DWAE model”, employed to divide input data slice as a tumor (abnormal) or no tumor (normal). This article used a high pass filter to show the heterogeneity of the MRI images and their integration with the input images. A high median filter was utilized to merge slices. We improved the output slices’ quality through highlight edges and smoothened input MR brain images. Then, we applied the seed growing method based on 4-connected since the thresholding cluster equal pixels with input MR data. The segmented MR image slices provide two two-layer using the proposed deep wavelet auto-encoder model. We then used 200 hidden units in the first layer and 400 hidden units in the second layer. The softmax layer testing and training are performed for the identification of the MR image normal and abnormal. The contribution of the deep wavelet auto-encoder model is in the analysis of pixel pattern of MR brain image and the ability to detect and classify the tumor with high accuracy, short time, and low loss validation. To train and test the overall performance of the proposed model, we utilized 2500 MR brain images from BRATS2012, BRATS2013, BRATS2014, BRATS2015, 2015 challenge, and ISLES, which consists of normal and abnormal images. The experiments results show that the proposed model achieved an accuracy of 99.3%, loss validation of 0.1, low FPR and FNR values. This result demonstrates that the proposed DWAE model can facilitate the automatic detection of brain tumors.

scholarly journals Modified Kernel Based Fuzzy Clustering for MR Brain Image Segmentation using Deep Learning

2019 ◽  
Vol 8 (6) ◽  
pp. 2881-2887
Keyword(s):  
Fuzzy Clustering ◽  
Bias Field ◽  
Mr Images ◽  
Brain Images ◽  
Normal Tissues ◽  
Segmentation Algorithms ◽  
Segmentation Approach ◽  
Mr Brain Images ◽  
Mr Brain ◽  
Abnormal Tissue

The segmentation procedure might cause error in diagnosing MR images due to the artifacts and noises that exist in it. This may lead to misclassifying normal tissue as abnormal tissue. In addition, it is also required to model the ontogenesis of a tumour, as they propagate at distinctive rates in contrast to their surroundings. Hence, it is still a challenging task to segment MR brain images due to possible noise presence, bias field and impact of partial volume. This article presents an efficient approach for segmenting MR brain images using a modified kernel based fuzzy clustering (MKFC) algorithm. In addition, this approach computes the weight of each picture element based on the local mutation coefficient (LMC). The proposed system would reflexively group normal tissues like white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) respectively, from abnormal tissue, such as a tumour region, in MR brain images. Simulation outcomes have shown that the performance of the proposed segmentation approach is superior to the existing segmentation algorithms in terms of both ocular and quantitative analysis

scholarly journals Differentiation of Alzheimer Conditions in MR Brain Images Using a Single Inception Module Network

2021 ◽  
Author(s):  
Sreelakshmi Shaji ◽  
Nagarajan Ganapathy ◽  
Ramakrishnan Swaminathan
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Input Image ◽  
Mr Images ◽  
Brain Images ◽  
Medical Expertise ◽  
Module Network ◽  
Mr Brain Images ◽  
Mr Brain ◽  
Normal Controls

In this study, an attempt has been made to differentiate Alzheimer’s Disease (AD) stages in structural Magnetic Resonance (MR) images using single inception module network. For this, T1-weighted MR brain images of AD, mild cognitive impairment and Normal Controls (NC) are obtained from a public database. From the images, significant features are extracted and classified using an inception module network. The performance of the model is computed and analyzed for different input image sizes. Results show that the single inception module is able to classify AD stages using MR images. The end-to-end network differentiates AD from NC with 85% precision. The model is found to be effective for varied sizes of input images. Since the proposed approach is able to categorize AD stages, single inception module networks could be used for the automated AD diagnosis with minimum medical expertise.

scholarly journals Improved Methods for Brain Tumor Detection and Analysis Using MR Brain Images

2019 ◽  
Vol 12 (04) ◽  
pp. 1621-1631
Author(s):  
Abd El Kader Isselmou ◽  
Guizhi Xu ◽  
Shuai Zhang
Keyword(s):  
Brain Tumor ◽  
Self Organizing Map ◽  
Feed Forward Neural Network ◽  
Brain Images ◽  
Short Period ◽  
Mr Brain Images ◽  
Sensitivity Specificity ◽  
Processing Techniques ◽  
Improved Methods ◽  
Mr Brain

Medical image processing techniques play an important role in helping doctors and facilities for patient diagnosis, the aim of this paper is comparison between three improved methods to identify the brain tumor using magnetic resonance brain images and analysis of the performance of each method according to different values, accuracy, nJaccard coeff, ndice, sensitivity, specificity, recall and precision values,We used three improved methods the first method improved fuzzy c-means algorithm (IFCM), the second method is improved feed-forward neural network (IFFNN), and the third method is a hybrid self-organizing map with a fuzzy k-means algorithm,the significance of these methods is complementary among them where each one has an advantage in a certain value as shown in the paper results, the three methods gave a very good performance, generally they can identify the tumor area clearly in MR brain image with different performance of the values, each method gave better values than others according to a comparison between the performance value of three methods,Finally, the improved methods allow the development of algorithms to diagnose a tumor more accurately and for a short period of time and each method is distinguished from each other in the performance and value, this gives integrity and strength to this work, these methods can be used in pre and post radio surgical applications

scholarly journals CLUSTERING MULTIPLE SCLEROSIS SUBGROUPS WITH MULTIFRACTAL METHODS AND SELF-ORGANIZING MAP ALGORITHM

Fractals ◽  
2017 ◽  
Vol 25 (04) ◽  
pp. 1740001 ◽  
Author(s):  
YELIZ KARACA ◽  
CARLO CATTANI
Keyword(s):  
Multiple Sclerosis ◽  
Self Organizing Map ◽  
Mr Images ◽  
Brain Images ◽  
Som Algorithm ◽  
Mr Brain Images ◽  
Magnetic Resonance Imaging Mri ◽  
Mr Brain ◽  
Multifractal Method ◽  
Self Organizing

Magnetic resonance imaging (MRI) is the most sensitive method to detect chronic nervous system diseases such as multiple sclerosis (MS). In this paper, Brownian motion Hölder regularity functions (polynomial, periodic (sine), exponential) for 2D image, such as multifractal methods were applied to MR brain images, aiming to easily identify distressed regions, in MS patients. With these regions, we have proposed an MS classification based on the multifractal method by using the Self-Organizing Map (SOM) algorithm. Thus, we obtained a cluster analysis by identifying pixels from distressed regions in MR images through multifractal methods and by diagnosing subgroups of MS patients through artificial neural networks.

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