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 Brain Tumor identification by Convolution Neural Network with Fuzzy C-mean Model Using MR Brain Images

2021 ◽  
Vol 14 ◽  
pp. 1096-1102
Author(s):  
Abd El Kader Isselmou ◽  
Guizhi Xu ◽  
Zhang Shuai ◽  
Sani Saminu ◽  
Imran Javaid ◽  
...  
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Convolution Neural Network ◽  
Brain Images ◽  
Proposed Model ◽  
Tumor Identification ◽  
Mr Brain Images ◽  
Sensitivity Specificity ◽  
The Brain ◽  
Mr Brain

Medical image computing techniques are essential in helping the doctors to support their decision in the diagnosis of the patients. Due to the complexity of the brain structure, we choose to use MR brain images because of their quality and the highest resolution. The objective of this article is to detect brain tumor using convolution neural network with fuzzy c-means model, the advantage of the proposed model is the ability to achieve excellent performance using accuracy, sensitivity, specificity, overall dice and recall values better than the previous models that are already published. In addition, the novel model can identify the brain tumor, using different types of MR images. The proposed model obtained accuracy with 98%.

scholarly journals Ventricle Segmentation with Itk's Watershed filter

2007 ◽  
Author(s):  
Matthew Stickney
Keyword(s):  
Healthy Volunteers ◽  
Image Data ◽  
Mr Image ◽  
Brain Images ◽  
Mr Brain Images ◽  
Mr Brain ◽  
Ventricle Segmentation

This paper describes the use of the Insight Toolkit (www.itk.org) to segment the ventricles in MR image data obtained from the Designed Database of MR Brain Images of Healthy Volunteers hosted at the MIDAS project.

scholarly journals Image Segmentation Using K- Means Clustering Method for Brain Tumour Detection

2018 ◽  
Vol 7 (2.19) ◽  
pp. 97
Author(s):  
S Thylashri ◽  
Udutha Mahesh Yadav ◽  
T Danush Chowdary
Keyword(s):  
Brain Tumour ◽  
Clustering Algorithm ◽  
Main Idea ◽  
Medical Field ◽  
Mr Image ◽  
Brain Images ◽  
Fundamental Part ◽  
Gray Level Image ◽  
Mr Brain Images ◽  
Mr Brain

Brain tumour is an irregular development by cells imitating among them in an unstoppable way. Specific identification of size and area of Brain tumour assumes a fundamental part in the analysis of tumour. Image processing is a dynamic research territory in which processing of image in medical field is an exceedingly difficult field. Segmentation of image assumes a critical part in handling of image as it helps in the finding of suspicious districts from the restorative image. In this paper a proficient algorithm is proposed for detection of tumour based on segmentation of brain by means of clustering technique. The main idea in this clustering algorithm is to transfer  a given gray-level image and then separate  tumour objects position  from other items of an MR image by using K-means clustering. Experiments say that segmentation for MR brain images can be done to help medical professionals to identify exactly size and region of the tumour located area in brain.  

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

MSENet: Multi-Modal Squeeze-and-Excitation Network for Brain Tumor Severity Prediction

2021 ◽  
pp. 2157005
Author(s):  
Jyostna Devi bodapati ◽  
Shaik Nagur Shareef ◽  
Veeranjaneyulu Naralasetti ◽  
Nirupama Bhat Mundukur
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Medical Image Analysis ◽  
Experimental Studies ◽  
Model Parameters ◽  
Competitive Performance ◽  
Proposed Model ◽  
Severity Prediction ◽  
Augmentation Techniques

Classification of brain tumors is one of the daunting tasks in medical imaging and incorrect decisions during the diagnosis process may lead to increased human fatality. Latest advances in artificial intelligence and deep learning have opened the path for the success of numerous medical image analysis tasks, including the recognition of brain tumors. In this paper, we propose a simple architecture based on deep learning that results in strong generalization without needing much preprocessing. The proposed Multi-modal Squeeze and Excitation model (MSENet) receives multiple representations of a given tumor image, learns end-to-end and effectively predicts the severity level of tumor. Convolution feature descriptors from multiple deep pre-trained models are used to effectively describe the tumor images and are supplied as input to the proposed MSENet. The squeeze and excitation blocks of the MSENet allow the model to prioritize tumor regions while giving less emphasis to the rest of the image, serving as an attention mechanism in the model. The proposed model is evaluated on the benchmark brain tumor dataset that is publicly accessible from the Figshare repository. Experimental studies reveal that in terms of model parameters, the proposed approach is simple and leads to competitive performance compared to those of existing complex models. By increasing complexity, the proposed model leads to generalize better and achieves state-of-the-art accuracy of 96.05% on Figshare dataset. Compared to the existing models, the proposed model neither uses segmentation nor uses augmentation techniques and without much pre-processing achieves competitive performance.

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.

Recent advancements in Fuzzy C-means based techniques for brain MRI Segmentation

2021 ◽  
Vol 16 ◽  
Author(s):  
Ghazanfar Latif ◽  
Jaafar Alghazo ◽  
Fadi N. Sibai ◽  
D.N.F. Awang Iskandar ◽  
Adil H. Khan
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Brain Mri ◽  
Tumor Segmentation ◽  
Brain Anatomy ◽  
Mri Segmentation ◽  
Brain Tumor Segmentation ◽  
Brain Images ◽  
Low Contrast ◽  
Fuzzy C Means

Background: Variations of image segmentation techniques, particularly those used for Brain MRI segmentation, vary in complexity from basic standard Fuzzy C-means (FCM) to more complex and enhanced FCM techniques. Objective: In this paper, a comprehensive review is presented on all thirteen variations of FCM segmentation techniques. In the review process, the concentration is on the use of FCM segmentation techniques for brain tumors. Brain tumor segmentation is a vital step in the process of automatically diagnosing brain tumors. Unlike segmentation of other types of images, brain tumor segmentation is a very challenging task due to the variations in brain anatomy. The low contrast of brain images further complicates this process. Early diagnosis of brain tumors is indeed beneficial to patients, doctors, and medical providers. Results: FCM segmentation works on images obtained from magnetic resonance imaging (MRI) scanners, requiring minor modifications to hospital operations to early diagnose tumors as most, if not all, hospitals rely on MRI machines for brain imaging. In this paper, we critically review and summarize FCM based techniques for brain MRI segmentation.

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