scholarly journals A Reliable and an Efficient Approach for Diagnosis of Brain Tumor Using Transfer Learning

2021 ◽  
Vol 14 (1) ◽  
pp. 283-293
Author(s):  
Sarika A Panwar ◽  
Mousami V Munot ◽  
Suraj Gawande ◽  
Pallavi S Deshpande
Keyword(s):  
Brain Tumor ◽  
Transfer Learning ◽  
Brain Mri ◽  
Discriminative Power ◽  
Resonance Imaging ◽  
Medical Field ◽  
World Brain ◽  
Magnetic Resonance Imaging Mri ◽  
Fully Connected ◽  
The Brain

Introduction: The World Brain Tumor Day is seen on eighth June, in a year. Despite exhaustive research in the medical field, the prevalence of this deadly disease is increasing globally with over new 28,000 braintumor cases being reported annually, in India alone. Recent advancements in the field of machine learning facilitate minimally invasive, efficient and reliable procedures for the diagnosis of Brain tumor. Objective: This research intends to design and devlop a reliable framework for accurate diagnosis of brain tumor mainly meningioma type, gliomatype and pituitary cerebrum tumor utilizing Magnetic Resonance Imaging (MRI), one of the most mainstream non-obtrusive procedure Methods: In the proposed system, pre-trained AlexNet is used to classify meningioma, glioma and pituitary brain tumor. The concept of transfer learning is applied using AlexNet for extracting the features from brain MRI images.The AlexNet contains eight layers in which the first five are convolution layer and the remaining three are fully connected layers. The last layer is a softmax layer which gets the output from fully connected layers. The ReLU non-linearity is applied to the output of every convolution and fully connected layer. The idea of transfer learning is applied utilizing AlexNet for computing thefeatures from brain MRI pictures. The AlexNet contains eight layers in which the initial five are convolution layers and the staying three layers are fully connected layers. a softmax is the last layer , which is feeded by the fully connected layers. The ReLUactivation function is applied to the output of convolution layer and fully connectedlayer Result: The proposed system framework recorded the best order precision of 100 % to classify the brain tumor when validated using a practical dataset. Conclusion: The proposed work presents accurate and automatic brain tumor classification using transfer learning. The features extracted using AlexNet has proven to be efficient in obtaining good discriminative power in diagnosis of brain tumor

scholarly journals A Review on Some Classification Methods of Brain Tumor MRI Datasets

2020 ◽  
pp. 421-424
Author(s):  
Nirmal Mungale ◽  
Snehal Kene ◽  
Amol Chaudhary
Keyword(s):  
Brain Tumor ◽  
Brain Mri ◽  
Mr Images ◽  
Classification Techniques ◽  
Abnormal Growth ◽  
Life Threatening ◽  
Magnetic Resonance Imaging Mri ◽  
Recent Classification ◽  
The Brain

Brain tumor is a life-threatening disease. Brain tumor is formed by the abnormal growth of cells inside and around the brain. Identification of the size and type of tumor is necessary for deciding the course of treatment of the patient. Magnetic Resonance Imaging (MRI) is one of the methods for detection of tumor in the brain. The classification of MR Images is a difficult task due to variety and complexity of brain tumors. Various classification techniques have been identified for brain MRI tumor images. This paper reviews some of these recent classification techniques.

scholarly journals Concurrence of Juvenile Idiopathic Arthritis and Multiple Sclerosis

2011 ◽  
Vol 2011 ◽  
pp. 1-2
Author(s):  
Ben Abdelghani Kaouther ◽  
Souabni Leila ◽  
Belhadj Salwa ◽  
Zakraoui Leith
Keyword(s):  
Multiple Sclerosis ◽  
Juvenile Idiopathic Arthritis ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Brain Mri ◽  
Resonance Imaging ◽  
Cerebral Magnetic Resonance Imaging ◽  
Need To Evaluate ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain

We report a 21-year-old female patient known to have Juvenile idiopathic arthritis (JIA) who later developed multiple sclerosis (MS). The disease was documented on the brain and cerebral magnetic resonance imaging (MRI) and the visual evoked potential. Our case emphasizes the need to evaluate the symptoms and brain MRI carefully. The concurrence of MS and JIA is uncommon. The possible relationship between the 2 diseases was discussed.

scholarly journals Clustering of Brain Tumors in Brain MRI Images based on Extraction of Textural and Statistical Features

2020 ◽  
Vol 16 (12) ◽  
pp. 116
Author(s):  
Hamed Samadi Ghoushchi ◽  
Yaghoub Pourasad
Keyword(s):  
Magnetic Resonance Imaging ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Imaging Techniques ◽  
Brain Mri ◽  
Support Vector ◽  
Statistical Features ◽  
Resonance Imaging ◽  
The Brain

<p>The purpose of this article is to investigate techniques for classifying tumor grade from magnetic resonance imaging (MRI). This requires early diagnosis of the brain tumor and its grade. Magnetic resonance imaging may show a clear tumor in the brain, but doctors need to measure the tumor in order to treat more or to advance treatment. For this purpose, digital imaging techniques along with machine learning can help to quickly identify tumors and also treatments and types of surgery. These combined techniques in understanding medical images for researchers are an important tool to increase the accuracy of diagnosis. In this paper, classification methods for MRI images of tumors of the human brain are performed to review the astrocytoma-containing glands. Methods used to classify brain tumors, including preprocessing, screening, tissue extraction, and statistical features of the tumor using two types of T<sub>1</sub>W and Flair brain MRI images and also the method of dimensionality reduction of extracted features and how to train them in classification are also explained. Determine the tumor area using three classification of Fuzzy Logic <em>C</em><em>-</em><em>Means</em><em> </em>Clustering (FCM), Probabilistic Neural Networks (PNN) and Support Vector Machines (SVM). In this paper, simulated and real MRI images are used. The results obtained from the proposed methods in this paper are compared with the reference results and the results show that the proposed approach can increase the reliability of brain tumor diagnosis.</p>

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.

Outpatient Procedural Sedation of Patients With Autism Spectrum Disorders for Magnetic Resonance Imaging of the Brain Using Propofol

2018 ◽  
Vol 33 (5) ◽  
pp. 313-319 ◽  
Author(s):  
Pradip P. Kamat ◽  
Marie K. Karaga ◽  
Benjamin L. Wisniewski ◽  
Courtney E. McCracken ◽  
Harold K. Simon ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Brain Mri ◽  
Autism Spectrum ◽  
Resonance Imaging ◽  
Serious Adverse Events ◽  
Children With Asd ◽  
Spectrum Disorders ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain

Objective: To quantify the number of personnel, time to induce and complete sedation using propofol for outpatient magnetic resonance imaging (MRI) of the brain, and the frequency of serious adverse events (SAEs) in children with autism spectrum disorder (ASD) compared with children without ASD. Results: Baseline characteristics were the same between both groups. Overall sedation success was 99%. Although most children were sedated with ≤3 providers, 10% with ASD needed ≥4 providers (P = .005). The duration of sedation was less for the ASD group compared with the non-ASD group (49 minutes vs 56 minutes, P = .005). There was no difference in SAE frequency between groups (ASD 14% vs non-ASD 16%, P = .57). Conclusion: Children with ASD can be sedated for brain MRI using propofol with no increased frequency of SAEs compared with children without ASD. Sedation teams should anticipate that 10% of children with ASD may need additional personnel before propofol induction.

scholarly journals Systemic inflammatory response in infants with craniostenosis sedated with propofol

2021 ◽  
Vol 11 (3) ◽  
pp. 297-306
Author(s):  
Viktoriia I. Gurskaya ◽  
Vadim P. Ivanov ◽  
Vitalii Yu. Novikov ◽  
Natalia V. Draygina ◽  
Irina A. Savvina
Keyword(s):  
Blood Plasma ◽  
Brain Mri ◽  
Short Exposure ◽  
Intravenous Sedation ◽  
Resonance Imaging ◽  
Intravenous Anesthesia ◽  
Tumor Necrosis Factors ◽  
Before And After ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain

AIM: This study aimed to investigate the possible effect of intravenous anesthesia (sedation) with propofol on the levels of several cytokines (interleukin [IL]-6, IL-8, IL-10, and tumor necrosis factors-) and S100B protein in the blood plasma of children aged 1 year with craniostenosis. MATERIALS AND METHODS: Twenty patients aged 112 months diagnosed with non-syndromic forms of craniosynostosis, who underwent magnetic resonance imaging (MRI) of the brain under propofol sedation, were classified according to ASA I-II class. Peripheral blood sampling was performed before and after the drug administration, followed by laboratory analysis. RESULTS: A significant increase was found in the serum level of IL-6 (p = 0.004) when intravenous sedation with propofol was used for 29 4.93 min. CONCLUSION: Short exposure of children aged 1 year with craniostenosis to hypnotic propofol during brain MRI significantly increased the level of the pro-inflammatory cytokine IL-6 in the blood plasma.

U-Net Model-Based Classification and Description of Brain Tumor in MRI Images

2021 ◽  
pp. 2140005
Author(s):  
P. Prakash Tunga ◽  
Vipula Singh ◽  
V. Sri Aditya ◽  
N. Subramanya
Keyword(s):  
Brain Tumor ◽  
Brain Mri ◽  
Ground Truth ◽  
Invasive Technique ◽  
Imaging Method ◽  
Non Invasive ◽  
Tissue Contrast ◽  
Tumor Region ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain

In this paper, we discuss the classification of the brain tumor in Magnetic Resonance Imaging (MRI) images using the U-Net model, then evaluate parameters that indicate the performance of the model. We also discuss the extraction of the tumor region from brain image and description of the tumor regarding its position and size. Here, we consider the case of Gliomas, one of the types of brain tumors, which occur in common and can be fatal depending on their position and growth. U-Net is a model of Convolutional Neural Network (CNN) which has U-shaped architecture. MRI employs a non-invasive technique and can very well provide soft-tissue contrast and hence, for the detection and description of the brain tumor, this imaging method can be beneficial. Manual delineation of tumors from brain MRI is laborious, time-consuming and can vary from expert to expert. Our work forms a computer aided technique which is relatively faster and reproducible, and the accuracy is very much on par with ground truth. The results of the work can be used for treatment planning and further processing related to storage or transmission of images.

scholarly journals Transient Periictal Brain Imaging Abnormality in a Saudi Patient with Probable Celiac Disease Epilepsy and Occipital Calcification Syndrome

2019 ◽  
Vol 2019 ◽  
pp. 1-5
Author(s):  
Soha Khan ◽  
Asma AlNajjar ◽  
Abdullah Alquaydheb ◽  
Shahpar Nahrir
Keyword(s):  
Celiac Disease ◽  
Energy Demand ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Resonance Imaging ◽  
Pump Failure ◽  
Mediterranean Countries ◽  
Apparent Diffusion ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain

Celiac disease epilepsy and occipital calcification (CEC) syndrome is a rare, emerging disease first described in 1992. To date, fewer than 200 cases have been reported worldwide. CEC syndrome is generally thought to be a genetic, noninherited, and ethnically and geographically restricted disease in Mediterranean countries. However, we report the first ever case of probable CEC in a Saudi patient. Furthermore, the patient manifested a magnitude of brain magnetic resonance imaging (MRI) signal abnormalities during the periictal period which, to the best of our knowledge, has never been described in CEC. The brain MRI revealed diffusion-weighted imaging (DWI) restriction with a concordant area of apparent diffusion coefficient (ADC) hypointensity around bilateral occipital area of calcification. An imbalance between the heightened energy demand during ictal phase of the seizure and unadjusted blood supply may have caused an electric pump failure and cytotoxic edema, which then led to DWI/ADC signal alteration.

scholarly journals Detection and Classification of Brain Tumor in MRI Images Using Wavelet Transform and Convolutional Neural Network

2020 ◽  
pp. 15-26
Author(s):  
Ahmad M. Sarhan
Keyword(s):  
Brain Tumor ◽  
Wavelet Transform ◽  
Brain Tumors ◽  
Brain Mri ◽  
Discrete Wavelet ◽  
Abnormal Cells ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain ◽  
Mri Image

A brain tumor is a mass of abnormal cells in the brain. Brain tumors can be benign or malignant. Conventional diagnosis of a brain tumor by the radiologist, is done by examining a set of images produced by magnetic resonance imaging (MRI). Many computer-aided detection (CAD) systems have been developed in order to help the radiologist reach his goal of correctly classifying the MRI image. Convolutional neural networks (CNNs) have been widely used in the classification of medical images. This paper presents a novel CAD technique for the classification of brain tumors in MRI images The proposed system extracts features from the brain MRI images by utilizing the strong energy compactness property exhibited by the Discrete Wavelet transform (DWT). The Wavelet features are then applied to a CNN to classify the input MRI image. Experimental results indicate that the proposed approach outperforms other commonly used methods and gives an overall accuracy of 98.5%.

Segmentation and clustering in brain MRI imaging

2018 ◽  
Vol 30 (1) ◽  
pp. 31-44 ◽  
Author(s):  
Golrokh Mirzaei ◽  
Hojjat Adeli
Keyword(s):  
Grey Matter ◽  
State Of The Art ◽  
Brain Mri ◽  
Brain Regions ◽  
Mri Imaging ◽  
Resonance Imaging ◽  
Brain Images ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain ◽  
Different Parts

AbstractClustering is a vital task in magnetic resonance imaging (MRI) brain imaging and plays an important role in the reliability of brain disease detection, diagnosis, and effectiveness of the treatment. Clustering is used in processing and analysis of brain images for different tasks, including segmentation of brain regions and tissues (grey matter, white matter, and cerebrospinal fluid) and clustering of the atrophy in different parts of the brain. This paper presents a state-of-the-art review of brain MRI studies that use clustering techniques for different tasks.

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