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

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

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

International Journal of Circuits, Systems and Signal Processing ◽

10.46300/9106.2020.14.137 ◽

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%.

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Efficiency of Fuzzy C Means algorithm for Brain Tumor segmentation in MR Brain Images

International Journal of Engineering and Technology ◽

10.21817/ijet/2016/v8i6/160806261 ◽

2016 ◽

Vol 8 (6) ◽

pp. 2979-2989 ◽

Cited By ~ 1

Author(s):

Velmurugan T. ◽

Mahalakshmi S.

Keyword(s):

Brain Tumor ◽

Tumor Segmentation ◽

Brain Tumor Segmentation ◽

Brain Images ◽

Fuzzy C Means ◽

Mr Brain Images ◽

Mr Brain ◽

Fuzzy C Means Algorithm

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

Diagnostics ◽

10.3390/diagnostics11091589 ◽

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.

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Deep Learning Algorithm for Brain Tumor Detection and Analysis Using MR Brain Images

Proceedings of the 2019 International Conference on Intelligent Medicine and Health - ICIMH 2019 ◽

10.1145/3348416.3348421 ◽

2019 ◽

Author(s):

Abd El Kader Isselmou ◽

Guizhi Xu ◽

Shuai Zhang ◽

Sani Saminu ◽

Imran Javaid

Keyword(s):

Deep Learning ◽

Brain Tumor ◽

Learning Algorithm ◽

Tumor Detection ◽

Brain Images ◽

Deep Learning Algorithm ◽

Mr Brain Images ◽

Mr Brain

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

Fractals ◽

10.1142/s0218348x17400011 ◽

2017 ◽

Vol 25 (04) ◽

pp. 1740001 ◽

Cited By ~ 10

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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Brain tumor segmentation from MR brain images using improved fuzzy c-means clustering and watershed algorithm

2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI) ◽

10.1109/icacci.2016.7732045 ◽

2016 ◽

Cited By ~ 14

Author(s):

C. C. Benson ◽

V. Deepa ◽

V. L. Lajish ◽

Kumar Rajamani

Keyword(s):

Brain Tumor ◽

Tumor Segmentation ◽

Watershed Algorithm ◽

Brain Tumor Segmentation ◽

Brain Images ◽

Fuzzy C Means ◽

Fuzzy C Means Clustering ◽

Mr Brain Images ◽

Mr Brain

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

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405617666210224113315 ◽

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.

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