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

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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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Development of Deep Learning Algorithm for Brain Tumor Segmentation

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.a9784.109119 ◽

2019 ◽

Vol 9 (1) ◽

pp. 2800-2803

Keyword(s):

Deep Learning ◽

Brain Tumor ◽

Brain Tumors ◽

Learning Algorithm ◽

Tumor Segmentation ◽

Brain Tumor Segmentation ◽

Human Errors ◽

Deep Learning Algorithm ◽

Proposed Model ◽

A New Technique

Medical imaging is an emerging field in engineering. As traditional way of brain tumor analysis, MRI scanning is the way to identify brain tumor. The core drawback of manual MRI studies conducted by surgeons is getting manual visual errorswhich can lead toofa false identification of tumor boundaries. To avoid such human errors, ultra age engineering adopted deep learning as a new technique for brain tumor segmentation. Deep learning convolution network can be further developed by means of various deep learning models for better performance. Hence, we proposed a new deep learning algorithm development which can more efficiently identifies the types of brain tumors in terms of level of tumor like T1, T2, and T1ce etc. The proposed system can identify tumors using convolution neural network(CNN) which works with the proposed algorithm “Sculptor DeepCNet”. The proposed model can be used by surgeons to identify post-surgical remains (if any) of brain tumors and thus proposed research can be useful for ultra-age neural surgical image assessments. This paper discusses newly developed algorithm and its testing results.

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Image Features of Magnetic Resonance Imaging under the Deep Learning Algorithm in the Diagnosis and Nursing of Malignant Tumors

Contrast Media & Molecular Imaging ◽

10.1155/2021/1104611 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Lifang Sun ◽

Xi Hu ◽

Yutao Liu ◽

Hengyu Cai

Keyword(s):

Deep Learning ◽

Brain Tumor ◽

Learning Algorithm ◽

Malignant Tumors ◽

Image Features ◽

Control Group ◽

Deep Learning Algorithm ◽

Image Characteristics ◽

Traditional Algorithm ◽

Mri Image

In order to explore the effect of convolutional neural network (CNN) algorithm based on deep learning on magnetic resonance imaging (MRI) images of brain tumor patients and evaluate the practical value of MRI image features based on deep learning algorithm in the clinical diagnosis and nursing of malignant tumors, in this study, a brain tumor MRI image model based on the CNN algorithm was constructed, and 80 patients with brain tumors were selected as the research objects. They were divided into an experimental group (CNN algorithm) and a control group (traditional algorithm). The patients were nursed in the whole process. The macroscopic characteristics and imaging index of the MRI image and anxiety of patients in two groups were compared and analyzed. In addition, the image quality after nursing was checked. The results of the study revealed that the MRI characteristics of brain tumors based on CNN algorithm were clearer and more accurate in the fluid-attenuated inversion recovery (FLAIR), MRI T1, T1c, and T2; in terms of accuracy, sensitivity, and specificity, the mean value was 0.83, 0.84, and 0.83, which had obvious advantages compared with the traditional algorithm ( P < 0.05 ). The patients in the nursing group showed lower depression scores and better MRI images in contrast to the control group ( P < 0.05 ). Therefore, the deep learning algorithm can further accurately analyze the MRI image characteristics of brain tumor patients on the basis of conventional algorithms, showing high sensitivity and specificity, which improved the application value of MRI image characteristics in the diagnosis of malignant tumors. In addition, effective nursing for patients undergoing analysis and diagnosis on brain tumor MRI image characteristics can alleviate the patient’s anxiety and ensure that high-quality MRI images were obtained after the examination.

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A Deep Learning Algorithm for Fully Automatic Brain Tumor Segmentation

2019 International Joint Conference on Neural Networks (IJCNN) ◽

10.1109/ijcnn.2019.8852210 ◽

2019 ◽

Cited By ~ 1

Author(s):

Yu Wang ◽

Changsheng Li ◽

Ting Zhu ◽

Chongchong Yu

Keyword(s):

Deep Learning ◽

Brain Tumor ◽

Learning Algorithm ◽

Tumor Segmentation ◽

Brain Tumor Segmentation ◽

Deep Learning Algorithm ◽

Fully Automatic

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Deep learning based enhanced tumor segmentation approach for MR brain images

Applied Soft Computing ◽

10.1016/j.asoc.2019.02.036 ◽

2019 ◽

Vol 78 ◽

pp. 346-354 ◽

Cited By ~ 42

Author(s):

Mamta Mittal ◽

Lalit Mohan Goyal ◽

Sumit Kaur ◽

Iqbaldeep Kaur ◽

Amit Verma ◽

...

Keyword(s):

Deep Learning ◽

Tumor Segmentation ◽

Brain Images ◽

Segmentation Approach ◽

Mr Brain Images ◽

Mr Brain

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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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