scholarly journals REVIEW ON DEEP LEARNING APPROACH FOR BRAIN TUMOR GLIOMA ANALYSIS

2021 ◽  
Vol 9 (1) ◽  
pp. 395-408
Author(s):  
Mrs. Disha Sushant Wankhede, Dr. Selvarani Rangasamy
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Early Diagnosis ◽  
Image Coding ◽  
Learning Approach ◽  
Sample Processing ◽  
Processing Efficiency ◽  
Learning Capability ◽  
Tumor Patient ◽  
Mri Diagnosis

Brain tumor diagnosis has evolved as a very critical need in current medical diagnosis. Early diagnosis of tumor detection is an important need for the primitive treatment of brain tumor patient increasing the survival rate of patient. MRI diagnosis of brain tumor for cancer treatment is a large processing due to volumetric content of scan sample. The processing of clinical data is large and consumes a high processing time. Hence, the need of early diagnosis and proper segmentation of brain tumor region is in need. This paper outlines a review on the developments of MRI sample processing for early diagnosis for brain tumor glioma diagnosis using deep learning approach. The advantage of learning capability and finer processing efficiency has gained an advantage in MRI image processing, which enable a better processing efficiency and accuracy in early diagnosis. Deep learning approach has shown a benefit of image coding based on selective features and state of art processing in diagnosis. The evaluation objective of the MRI sample processing has shown a better accuracy than the comparative existing approaches.  The recent trends, the advantages and limitation of the existing approach for MRI diagnosis is outlined.

scholarly journals A Deep Learning approach for Diagnosis of Mild Cognitive Impairment Based on MRI Images

2019 ◽  
Vol 9 (9) ◽  
pp. 217 ◽  
Author(s):  
Gorji ◽  
Kaabouch
Keyword(s):  
Deep Learning ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Early Diagnosis ◽  
Brain Structure ◽  
Neurodegenerative Disorder ◽  
Healthy People ◽  
Learning Approach ◽  
Brain Structure And Function ◽  
Percent Accuracy

Mild cognitive impairment (MCI) is an intermediary stage condition between healthy people and Alzheimer’s disease (AD) patients and other dementias. AD is a progressive and irreversible neurodegenerative disorder, which is a significant threat to people, age 65 and older. Although MCI does not always lead to AD, an early diagnosis at the stage of MCI can be very helpful in identifying people who are at risk of AD. Moreover, the early diagnosis of MCI can lead to more effective treatment, or at least, significantly delay the disease’s progress, and can lead to social and financial benefits. Magnetic resonance imaging (MRI), which has become a significant tool for the diagnosis of MCI and AD, can provide neuropsychological data for analyzing the variance in brain structure and function. MCI is divided into early and late MCI (EMCI and LMCI) and sadly, there is no clear differentiation between the brain structure of healthy people and MCI patients, especially in the EMCI stage. This paper aims to use a deep learning approach, which is one of the most powerful branches of machine learning, to discriminate between healthy people and the two types of MCI groups based on MRI results. The convolutional neural network (CNN) with an efficient architecture was used to extract high-quality features from MRIs to classify people into healthy, EMCI, or LMCI groups. The MRIs of 600 individuals used in this study included 200 control normal (CN) people, 200 EMCI patients, and 200 LMCI patients. This study randomly selected 70 percent of the data to train our model and 30 percent for the test set. The results showed the best overall classification between CN and LMCI groups in the sagittal view with an accuracy of 94.54 percent. In addition, 93.96 percent and 93.00 percent accuracy were reached for the pairs of EMCI/LMCI and CN/EMCI, respectively.

scholarly journals Dual-energy CT for automatic organs-at-risk segmentation in brain-tumor patients using a multi-atlas and deep-learning approach

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Brent van der Heyden ◽  
Patrick Wohlfahrt ◽  
Daniëlle B. P. Eekers ◽  
Christian Richter ◽  
Karin Terhaag ◽  
...  
Keyword(s):  
At Risk ◽  
Deep Learning ◽  
Brain Tumor ◽  
Organs At Risk ◽  
Dual Energy ◽  
Dual Energy Ct ◽  
Learning Approach ◽  
Tumor Patients

scholarly journals Applying a deep learning approach focusing on spatiotemporal features in early diagnosis of Alzheimer's disease

2021 ◽  
Vol 17 (S12) ◽  
Author(s):  
Eyitomilayo Yemisi Babatope ◽  
Jesus Alejandro Acosta‐Franco ◽  
Mireya Saraí García‐Vázquez ◽  
Alejandro Álvaro Ramírez‐Acosta ◽  
APIM Laboratory Citedi‐IPN
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Early Diagnosis ◽  
Learning Approach ◽  
Spatiotemporal Features

scholarly journals Deep Learning-Based Question Answering System for Intelligent Humanoid Robot

2020 ◽  
Author(s):  
Widodo Budiharto ◽  
Vincent Andreas ◽  
Alexander Agung Santoso Gunawan
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Humanoid Robot ◽  
Question Answering ◽  
Training Dataset ◽  
Learning Approach ◽  
Exact Match ◽  
Learning Capability ◽  
Question Answering Systems ◽  
Self Learning

Abstract The development of intelligent Humanoid Robot focuses on question answering systems to be able to interact with people is very rare. In this research, we would like to propose a Humanoid Robot with the self-learning capability for accepting and giving a response from people based on Deep Learning and big data from the internet. This kind of robot can be used widely in hotels, universities and public services. The Humanoid Robot should consider the style of questions and conclude the answer through conversation between robot and user. In our scenario, the robot will detect the user’s face and accept commands from the user to do an action, where the question from the user will be processed using deep learning, and the result will be compared with knowledge on the system. We proposed our deep learning approach, based on use GRU/LSTM, CNN and BiDAF with big data SQuAD as training dataset. Our experiment indicates that using GRU/LSTM encoder with BiDAF gives higher Exact Match and F1 Score, than CNN with the BiDAF model.

scholarly journals Deep Learning Approach for Medical Image Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Adekanmi Adeyinka Adegun ◽  
Serestina Viriri ◽  
Roseline Oluwaseun Ogundokun
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Skin Lesion ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Brain Magnetic Resonance Imaging ◽  
Region Of Interest ◽  
Learning Approach ◽  
Convolutional Network ◽  
Diagnosis Of Diseases

Localization of region of interest (ROI) is paramount to the analysis of medical images to assist in the identification and detection of diseases. In this research, we explore the application of a deep learning approach in the analysis of some medical images. Traditional methods have been restricted due to the coarse and granulated appearance of most of these images. Recently, deep learning techniques have produced promising results in the segmentation of medical images for the diagnosis of diseases. This research experiments on medical images using a robust deep learning architecture based on the Fully Convolutional Network- (FCN-) UNET method for the segmentation of three samples of medical images such as skin lesion, retinal images, and brain Magnetic Resonance Imaging (MRI) images. The proposed method can efficiently identify the ROI on these images to assist in the diagnosis of diseases such as skin cancer, eye defects and diabetes, and brain tumor. This system was evaluated on publicly available databases such as the International Symposium on Biomedical Imaging (ISBI) skin lesion images, retina images, and brain tumor datasets with over 90% accuracy and dice coefficient.

Applying deep learning approach for brain tumor detection

2020 ◽  
Author(s):  
Ankur Gupta ◽  
Apurv Verma ◽  
Dushyant Kaushik ◽  
Muskan Garg
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Tumor Detection ◽  
Learning Approach
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