Deep learning methods for predicting brain abnormalities and compute human cognitive power using fMRI

2021 ◽  
pp. 1-19
Author(s):  
K. Palraj ◽  
V. Kalaivani
Keyword(s):  
Medical Images ◽  
Brain Diseases ◽  
Resonance Imaging ◽  
Residual Network ◽  
Learning Methods ◽  
Modern Times ◽  
Machine Learning Methods ◽  
Computer Aided ◽  
Cognitive Power ◽  
Magnetic Resonance Imaging Mri

In modern times, digital medical images play a significant progression in clinical diagnosis to treat the populace earlier to hoard their lives. Magnetic Resonance Imaging (MRI) is one of the most advanced medical imaging modalities that facilitate scanning various parts of the human body like the head, chest, abdomen, and pelvis and identify the diseases. Numerous studies on the same discipline have proposed different algorithms, techniques, and methods for analyzing medical digital images, especially MRI. Most of them have mainly focused on identifying and classifying the images as either normal or abnormal. Computing brainpower is essential to understand and handle various brain diseases efficiently in critical situations. This paper knuckles down to design and implement a computer-aided framework, enhancing the identification of humans’ cognitive power from their MRI. Images. The proposed framework converts the 3D DICOM images into 2D medical images, preprocessing, enhancement, learning, and extracting various image information to classify it as normal or abnormal and provide the brain’s cognitive power. This study widens the efficient use of machine learning methods, Voxel Residual Network (VRN), with multimodality fusion architecture to learn and analyze the image to classify and predict cognitive power. The experimental results denote that the proposed framework demonstrates better performance than the existing approaches.

Using Tomographic Medical Images as Objects of Portrait Examination

2019 ◽  
pp. 142-145
Author(s):  
G. V. Cherepenko
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Medical Images ◽  
Resonance Imaging ◽  
Examination Technique ◽  
Magnetic Resonance Imaging Mri ◽  
Practical Recommendations ◽  
Mri Image ◽  
Expert Practice

The paper provides an example from expert practice, during which a head image obtained using magnetic resonance imaging (MRI) was used as a sample. It is proposed to include an MRI image in a number of objects and samples considered by the current portrait examination technique. The nature of the suitability of such an object for the production of portrait examination is determined. Practical recommendations are given for working with the appropriate software to get the most visual picture.

scholarly journals Overview of Multi-Modal Brain Tumor MR Image Segmentation

Healthcare ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 1051
Author(s):  
Wenyin Zhang ◽  
Yong Wu ◽  
Bo Yang ◽  
Shunbo Hu ◽  
Liang Wu ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Brain Diseases ◽  
Learning Methods ◽  
Brain Images ◽  
Advantages And Disadvantages ◽  
Segmentation Methods ◽  
Magnetic Resonance Imaging Mri ◽  
Tumor Mri

The precise segmentation of brain tumor images is a vital step towards accurate diagnosis and effective treatment of brain tumors. Magnetic Resonance Imaging (MRI) can generate brain images without tissue damage or skull artifacts, providing important discriminant information for clinicians in the study of brain tumors and other brain diseases. In this paper, we survey the field of brain tumor MRI images segmentation. Firstly, we present the commonly used databases. Then, we summarize multi-modal brain tumor MRI image segmentation methods, which are divided into three categories: conventional segmentation methods, segmentation methods based on classical machine learning methods, and segmentation methods based on deep learning methods. The principles, structures, advantages and disadvantages of typical algorithms in each method are summarized. Finally, we analyze the challenges, and suggest a prospect for future development trends.

scholarly journals COMPUTER AIDED ARTICULATORY TUTOR: A SCIENTIFIC STUDY

2014 ◽  
pp. 100-105
Author(s):  
Arumugam Rathinavelu ◽  
Hemalatha Thiagarajan
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Visual Cues ◽  
Hearing Impaired ◽  
Visual Simulation ◽  
Simulation Tool ◽  
Resonance Imaging ◽  
Computer Aided ◽  
Magnetic Resonance Imaging Mri ◽  
Speech Information

This paper describes the use of Computer Aided Articulation Tutor (CAAT) to conduct phonetic training to the hearing-impaired (HI) children with inner articulators as visual cues. This Articulatory tutor was developed by using Magnetic Resonance Imaging (MRI) and computer graphics techniques. The articulators include the movement of jaw, lips, tongue and velum. Ten hearing impaired (HI) children between the ages 4 and 7 were selected and trained for 10 hours across 4 weeks on 24 words. Intelligibility of HI children was investigated to find out their performance in speech perception and articulation. The post-training results indicated that HI children improved in articulation of speech sounds placed at different contexts. This 3D visual simulation tool helped HI children in perceiving the speech information significantly.

scholarly journals Multimodal Data Integration for Computer-Aided Ablation of Atrial Fibrillation

2008 ◽  
Vol 2008 ◽  
pp. 1-8 ◽  
Author(s):  
Jonghye Woo ◽  
Byung-Woo Hong ◽  
Sunil Kumar ◽  
Indranill Basu Ray ◽  
C.-C. Jay Kuo
Keyword(s):  
Atrial Fibrillation ◽  
Information Integration ◽  
Resonance Imaging ◽  
Multimodal Data ◽  
Simultaneous Reconstruction ◽  
Surgical Methods ◽  
Multimodal Information ◽  
Computer Aided ◽  
Magnetic Resonance Imaging Mri ◽  
Prior Shape

Image-guided percutaneous interventions have successfully replaced invasive surgical methods in some cardiologic practice, where the use of 3D-reconstructed cardiac images, generated by magnetic resonance imaging (MRI) and computed tomography (CT), plays an important role. To conduct computer-aided catheter ablation of atrial fibrillation accurately, multimodal information integration with electroanatomic mapping (EAM) data and MRI/CT images is considered in this work. Specifically, we propose a variational formulation for surface reconstruction and incorporate the prior shape knowledge, which results in a level set method. The proposed method enables simultaneous reconstruction and registration under nonrigid deformation. Promising experimental results show the potential of the proposed approach.

scholarly journals Machine Learning and Deep Learning Methods for Skin Lesion Classification and Diagnosis: A Systematic Review

Diagnostics ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1390
Author(s):  
Mohamed A. Kassem ◽  
Khalid M. Hosny ◽  
Robertas Damaševičius ◽  
Mohamed Meselhy Eltoukhy
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Skin Lesion ◽  
Ad Hoc ◽  
Racial Bias ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Computer Aided ◽  
Aided Diagnosis

Computer-aided systems for skin lesion diagnosis is a growing area of research. Recently, researchers have shown an increasing interest in developing computer-aided diagnosis systems. This paper aims to review, synthesize and evaluate the quality of evidence for the diagnostic accuracy of computer-aided systems. This study discusses the papers published in the last five years in ScienceDirect, IEEE, and SpringerLink databases. It includes 53 articles using traditional machine learning methods and 49 articles using deep learning methods. The studies are compared based on their contributions, the methods used and the achieved results. The work identified the main challenges of evaluating skin lesion segmentation and classification methods such as small datasets, ad hoc image selection and racial bias.

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