scholarly journals Classification on Magnetic Resonance Imaging (MRI) Brain Tumour using BPNN, SVM and CNN

2019 ◽  
Vol 8 (3) ◽  
pp. 8601-8607
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Image Segmentation ◽  
Magnetic Resonance ◽  
Brain Tumour ◽  
Support Vector ◽  
Low Grade ◽  
Resonance Imaging ◽  
Mri Brain ◽  
Magnetic Resonance Imaging Mri

In this works, the main objective is to detect the high grade gliomas (HGG) and low grade gliomas (LGG) from Magnetic Resonance Imaging (MRI) Brain Tumour images by applying the efficient image segmentation and classify among them. So hybrid image segmentation techniques applied in this work, first one is canny edge detection which is used to locate the boundary of the image and second is fuzzy c-mean clustering which is used to clubbed together of the similarity intensity value into clusters. Also further eight feature extracted using Intensity based Histogram and GrayLevel Co-occurrence Matrix (GLCM). Now three classifiers learning algorithm applied in this system, first one is backpropogation neural network (BPNN) which consists of multi-layer perceptrons to solve the complex problem for the given inputs. Second one is convolution neural network (CNN) are the part of neural networks which have very effective in areas such as image recognition and image classification. Third is Support vector machine (SVM) which can be used for both classification and regression challenges. Each of one is evaluated performance based on different techniques. It found that SVM and CNN gives 88% accuracy for this work.

scholarly journals Magnetic Resonance Imaging Image Segmentation and Brain Tumour Detection Using Pulse-Coupled Neural Networks

2021 ◽  
Vol 17 (1) ◽  
pp. 1-16
Author(s):  
Louiza Dehyadegari ◽  
Somayeh Khajehasani
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Image Processing ◽  
Image Segmentation ◽  
Magnetic Resonance ◽  
Brain Tumour ◽  
Detection System ◽  
Brain Mri ◽  
Resonance Imaging ◽  
Tumour Detection

Image processing can be defined as a functional structure to correct and change the images viewed and their interpretation. One of the applications of digital image processing is using image processing techniques in the component and image segmentation. One of these techniques is magnetic resonance imaging (MRI) in the medical world. In this article, a brain tumour detection system and various anomalies and abnormalities are presented where image pre-processing and preparation include image enhancement, filtering and noise reduction. Then image segmentation is done by a pulse neural network. Next, the image features are extracted and finally, the tumour and abnormal area are separated from the normal area by the algorithms. In this research, the feature selection and integration method are used and the most important statistical features of brain MRI images are used to improve brain tumour detection. Along with the studies done and the implementation of tumour detection systems, the following suggestions can be provided for future researches and the tumour detection system will work more efficiently. The pulse-coupled neural network (PCNN) can be used for image segmentation in the pre-processing stage, especially in the image filtering.

scholarly journals Diagnosa Tumor Otak Berdasarkan Citra MRI (Magnetic Resonance Imaging)

2019 ◽  
Vol 18 (2) ◽  
Author(s):  
Ida Bagus Leo Mahadya Suta ◽  
Rukmi Sari Hartati ◽  
Yoga Divayana
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Convolutional Neural Network ◽  
Resonance Imaging ◽  
Magnetic Resonance Imaging Mri

Tumor otak menjadi salah satu penyakit yang paling mematikan, salah satu jenis yang paling banyak ditemukan adalah glioma sekitar 6 dari 100.000 pasien adalah penderita glioma. Citra digital melalui Magnetic Resonance Imaging (MRI) merupakan salah satu metode untuk membantu dokter dalam menganalisa dan mengklasifikasikan jenis tumor otak. Namun, klasifikasi secara manual membutuhkan waktu yang lama dan memiliki resiko kesalahan yang tinggi, untuk itu dibutuhkan suatu cara otomatis dan akurat dalam melakukan klasifikasi citra MRI. Convolutional Neural Network (CNN) menjadi salah satu solusi dalam melakukan klasifikasi otomatis dalam citra MRI. CNN merupakan algoritma deep learning yang memiliki kemampuan untuk belajar sendiri dari kasus kasus sebelumnya. Dan dari penelitian yang telah dilakukan, diperoleh hasil bahwa CNN mampu dalam menyelesaikan klasifikasi tumor otak dengan akurasi yang tinggi. Peningkatan akurasi diperoleh dengan mengembangkan algoritma CNN baik melalui menentukan nilai kernel dan/atau fungsi aktivasi.

scholarly journals Role of Magnetic Resonance Imaging and Spinal Tap in Early Diagnosis of Tuberculous Meningitis

2019 ◽  
Vol 8 (3) ◽  
pp. 127-130
Author(s):  
Salma Haji
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Early Diagnosis ◽  
Tuberculous Meningitis ◽  
Stage I ◽  
Resonance Imaging ◽  
Mri Brain ◽  
Csf Analysis ◽  
Magnetic Resonance Imaging Mri

Background: Tuberculous meningitis (TBM) is difficult to diagnose in early stages due to nonspecific symptoms. There should be high index of suspicion to diagnose TBM at an early stage. The objective of the study was to find out the role of magnetic resonance imaging (MRI) and spinal tap in early diagnosis of tuberculous meningitis. Material and Methods: A cross sectional study was conducted from July 2015 till July 2018 at Neuromedicine ward, Jinnah Postgraduate Medical Centre (JPMC), Karachi. All patients above 12 year of age, both male and female with nonspecific symptoms like headache, malaise and drowsiness or suspicion of TBM (stage I, II, and III according to British Medical Research Council TBM staging criteria) were included in the study. Patients diagnosed with other CNS disease like encephalitis, malaria and acute bacterial meningitis were excluded. Magnetic Resonance Imaging (MRI) of the brain and early spinal tap for cerebrospinal fluid (CSF) analysis were used to diagnose TBM and findings were noted. Results of MRI and CSF analysis were analyzed by SPSS version 24. Results: A total of 110 patients of TBM, with 60 (54.5%) male and 50 (45.5%) female patients were included in the study. Most of the patients belonged to a younger age group of 12-40 years (81.8%), while 18.2% were above 40 years of age. About 90% patients were diagnosed in stage I TBM and 10% in stage II and III. MRI brain findings included meningeal enhancement (60%), hydrocephalus (41.81%) cerebral edema (82.73%), tuberculoma (19%) and infarct (14.5%), respectively. CSF analysis showed low protein in 80%, low glucose in 91.8% and lymphocytic pleocytosis in 97.2%, respectively. Conclusion: Both MRI brain and spinal tap with CSF analysis played a role in the early diagnosis of TBM, which is important to prevent the lethal complications associated with late diagnosis of this disease.

scholarly journals Classification of Abdominal Visceral and Subcutaneous Fat Distributions by Body Shape Descriptors

2020 ◽  
Author(s):  
BUGAO XU
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Body Shape ◽  
Cross Validation ◽  
Abdominal Fat ◽  
Support Vector ◽  
Shape Descriptors ◽  
Resonance Imaging ◽  
Selection Scheme ◽  
Magnetic Resonance Imaging Mri

<p>This study aims to explore new categorization that characterizes the distribution clusters of visceral and subcutaneous adipose tissues (VAT and SAT) measured by magnetic resonance imaging (MRI); to analyze the relationship between the VAT-SAT distribution patterns and the novel body shape descriptors (BSDs); and to develop a classifier to predict the fat distribution clusters using the BSDs. 66 male and 54 female participants were scanned by magnetic resonance imaging (MRI) and a stereovision body imaging (SBI) to measure participants’ abdominal VAT and SAT volumes and the BSDs. A fuzzy <i>c-</i>means algorithm was used to form the inherent grouping clusters of abdominal fat distributions. A support-vector-machine (SVM) classifier, with an embedded feature selection scheme, was employed to determine an optimal subset of the BSDs for predicting internal fat distributions. A five-fold cross-validation procedure was used to prevent over-fitting in the classification. The classification results of the BSDs were compared with those of the traditional anthropometric measurements and the Dual Energy X-Ray Absorptiometry (DXA) measurements.<b> </b>Four clusters were identified for abdominal fat distributions: low VAT and SAT, elevated VAT and SAT, higher SAT, and higher VAT. The cross-validation accuracies of the traditional anthropometric, DXA and BSD measurements are 85.0%, 87.5% and 90%, respectively. </p>

Utilizing magnetic resonance imaging (MRI) to assess the effects of angling-induced barotrauma on rockfish (Sebastes)

2008 ◽  
Vol 65 (7) ◽  
pp. 1245-1249 ◽  
Author(s):  
Bonnie L. Rogers ◽  
Christopher G. Lowe ◽  
Esteban Fernández-Juricic ◽  
Lawrence R. Frank
Keyword(s):  
Magnetic Resonance Imaging ◽  
Image Segmentation ◽  
Magnetic Resonance ◽  
Three Dimensional ◽  
The Body ◽  
The Other ◽  
Bladder Rupture ◽  
Swim Bladder ◽  
Resonance Imaging ◽  
Magnetic Resonance Imaging Mri

The physical consequences of barotrauma on the economically important rockfish ( Sebastes ) were evaluated with a novel method using T2-weighted magnetic resonance imaging (MRI) in combination with image segmentation and analysis. For this pilot study, two fishes were captured on hook-and-line from 100 m, euthanized, and scanned in a 3 Tesla human MRI scanner. Analyses were made on each fish, one exhibiting swim bladder overinflation and exophthalmia and the other showing low to moderate swim bladder overinflation. Air space volumes in the body were quantified using image segmentation techniques that allow definition of individual anatomical regions in the three-dimensional MRIs. The individual exhibiting the most severe signs of barotrauma revealed the first observation of a gas-filled orbital space behind the eyes, which was not observable by gross dissection. Severe exophthalmia resulted in extreme stretching of the optic nerves, which was clearly validated with dissections and not seen in the other individual. Expanding gas from swim bladder overinflation must leak from the swim bladder, rupture the peritoneum, and enter the cranium. This MRI method of evaluating rockfish following rapid decompression is useful for quantifying the magnitude of internal barotrauma associated with decompression and complementing studies on the effects of capture and discard mortality of rockfishes.

scholarly journals Brain Tumor Classification using Deep Learning Algorithms

2021 ◽  
Vol 9 (12) ◽  
pp. 417-426
Author(s):  
Ankita Kadam
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Neural Networks ◽  
Deep Learning ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Tumor Classification ◽  
Resonance Imaging ◽  
Magnetic Resonance Imaging Mri

Abstract: A Brain tumor is one aggressive disease. An estimated more than 84,000 people will receive a primary brain tumor diagnosis in 2021 and an estimated 18,600 people will die from a malignant brain tumor (brain cancer) in 2021.[8] The best technique to detect brain tumors is by using Magnetic Resonance Imaging (MRI). More than any other cancer, brain tumors can have lasting and life-altering physical, cognitive, and psychological impacts on a patient’s life and hence faster diagnosis and best treatment plan should be devised to improve the life expectancy and well-being of these patients. Neural networks have shown colossal accuracy in image classification and segmentation problems. In this paper, we propose comparative studies of various deep learning models based on different types of Neural Networks(ANN, CNN, TL) to firstly identify brain tumors and then classify them into Benign Tumor, Malignant Tumor or Pituitary Tumor. The data set used holds 3190 images on T1-weighted contrast-enhanced images which were cleaned and augmented. The best ANN model concluded with an accuracy of 78% and the best CNN model consisting of 3 convolution layers had an accuracy of 90%. The VGG16(retrained on the dataset) model surpasses other ANN, CNN, TL models for multi-class tumor classification. This proposed network achieves significantly better performance with a validation accuracy of 94% and an F1-Score of 91. Keywords: Artificial Neural Network(ANN), Convolution Neural Network (CNN), Transfer Learning(TL), Magnetic Resonance Imaging(MRI.)

scholarly journals A Comprehensive Machine-Learning Model Applied to Magnetic Resonance Imaging (MRI) to Predict Alzheimer’s Disease (AD) in Older Subjects

2020 ◽  
Vol 9 (7) ◽  
pp. 2146
Author(s):  
Gopi Battineni ◽  
Nalini Chintalapudi ◽  
Francesco Amenta ◽  
Enea Traini
Keyword(s):  
Magnetic Resonance Imaging ◽  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Selection ◽  
Magnetic Resonance ◽  
Neurodegenerative Disorder ◽  
Support Vector ◽  
Resonance Imaging ◽  
Magnetic Resonance Imaging Mri

Increasing evidence suggests the utility of magnetic resonance imaging (MRI) as an important technique for the diagnosis of Alzheimer’s disease (AD) and for predicting the onset of this neurodegenerative disorder. In this study, we present a sophisticated machine learning (ML) model of great accuracy to diagnose the early stages of AD. A total of 373 MRI tests belonging to 150 subjects (age ≥ 60) were examined and analyzed in parallel with fourteen distinct features related to standard AD diagnosis. Four ML models, such as naive Bayes (NB), artificial neural networks (ANN), K-nearest neighbor (KNN), and support-vector machines (SVM), and the receiver operating characteristic (ROC) curve metric were used to validate the model performance. Each model evaluation was done in three independent experiments. In the first experiment, a manual feature selection was used for model training, and ANN generated the highest accuracy in terms of ROC (0.812). In the second experiment, automatic feature selection was conducted by wrapping methods, and the NB achieved the highest ROC of 0.942. The last experiment consisted of an ensemble or hybrid modeling developed to combine the four models. This approach resulted in an improved accuracy ROC of 0.991. We conclude that the involvement of ensemble modeling, coupled with selective features, can predict with better accuracy the development of AD at an early stage.

scholarly journals A discrepancy between clinical course and magnetic resonance imaging in a case of non-herpetic acute limbic encephalitis

2013 ◽  
Vol 5 (2) ◽  
pp. 8
Author(s):  
Naoto Kohno ◽  
Yuko Kawakami ◽  
Chizuko Hamada ◽  
Genya Toyoda ◽  
Hirokazu Bokura ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Limbic Encephalitis ◽  
Time Lag ◽  
Pulse Therapy ◽  
Symptomatic Improvement ◽  
Low Grade ◽  
Resonance Imaging ◽  
Steroid Pulse ◽  
Magnetic Resonance Imaging Mri

We report the case of a 64-year old man who presented memory disturbance, low-grade fever, weight loss, and bilateral hand tremors for three months. He was diagnosed with non-herpetic acute limbic encephalitis (NHALE). Follow-up magnetic resonance imaging (MRI) revealed new lesions after symptomatic improvement following steroid pulse therapy. This may indicate that there is a time lag between the disturbance or recovery of neurons and astrocytes. Thus, other lesions might occasionally appear during convalescence in patients with NHALE, even if only minimal lesions were found on the initial MRI.

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