scholarly journals Automated classification of brain tumors by deep learning-based models on magnetic resonance images using a developed web-based interface

2021 ◽  
Author(s):  
Bora TETİK ◽  
Hasan UCUZAL ◽  
Şeyma YAŞAR ◽  
Cemil ÇOLAK
Keyword(s):  
Deep Learning ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Magnetic Resonance Images ◽  
Automated Classification ◽  
Web Based

Classification of type of brain magnetic resonance images with deep learning technique

2021 ◽  
Vol 77 ◽  
pp. 180-185
Author(s):  
Hiroyuki Sugimori ◽  
Hiroyuki Hamaguchi ◽  
Taro Fujiwara ◽  
Kinya Ishizaka
Keyword(s):  
Deep Learning ◽  
Magnetic Resonance ◽  
Magnetic Resonance Images ◽  
Learning Technique

Automated classification of atherosclerotic plaque from magnetic resonance images using predictive models

Biosystems ◽  
2007 ◽  
Vol 90 (2) ◽  
pp. 456-466 ◽  
Author(s):  
Russell W. Anderson ◽  
Christopher Stomberg ◽  
Charles W. Hahm ◽  
Venkatesh Mani ◽  
Daniel D. Samber ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Atherosclerotic Plaque ◽  
Predictive Models ◽  
Magnetic Resonance Images ◽  
Automated Classification

scholarly journals Classification of Brain MRI Tumor Images Based on Deep Learning PGGAN Augmentation

Diagnostics ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2343
Author(s):  
Ahmed M. Gab Allah ◽  
Amany M. Sarhan ◽  
Nada M. Elshennawy
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Performance Metrics ◽  
Brain Mri ◽  
Magnetic Resonance Images ◽  
Tumor Type ◽  
Generative Adversarial Network ◽  
Learning Stage

The wide prevalence of brain tumors in all age groups necessitates having the ability to make an early and accurate identification of the tumor type and thus select the most appropriate treatment plans. The application of convolutional neural networks (CNNs) has helped radiologists to more accurately classify the type of brain tumor from magnetic resonance images (MRIs). The learning of CNN suffers from overfitting if a suboptimal number of MRIs are introduced to the system. Recognized as the current best solution to this problem, the augmentation method allows for the optimization of the learning stage and thus maximizes the overall efficiency. The main objective of this study is to examine the efficacy of a new approach to the classification of brain tumor MRIs through the use of a VGG19 features extractor coupled with one of three types of classifiers. A progressive growing generative adversarial network (PGGAN) augmentation model is used to produce ‘realistic’ MRIs of brain tumors and help overcome the shortage of images needed for deep learning. Results indicated the ability of our framework to classify gliomas, meningiomas, and pituitary tumors more accurately than in previous studies with an accuracy of 98.54%. Other performance metrics were also examined.

scholarly journals Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Manan Binth Taj Noor ◽  
Nusrat Zerin Zenia ◽  
M Shamim Kaiser ◽  
Shamim Al Mamun ◽  
Mufti Mahmud
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Neurological Disorders ◽  
Magnetic Resonance Images ◽  
Future Research ◽  
Comparative Performance

Abstract Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer’s disease, Parkinson’s disease and schizophrenia—from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided.

scholarly journals Implementation of a deep learning model for automated classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) in real time

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Song-Quan Ong ◽  
Hamdan Ahmad ◽  
Gomesh Nair ◽  
Pradeep Isawasan ◽  
Abdul Hafiz Ab Majid
Keyword(s):  
Deep Learning ◽  
Aedes Aegypti ◽  
Real Time ◽  
Aedes Albopictus ◽  
Automated Classification ◽  
Expert Performance ◽  
Deep Convolutional Neural Networks ◽  
Significant Difference ◽  
Set Up

AbstractClassification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) by humans remains challenging. We proposed a highly accessible method to develop a deep learning (DL) model and implement the model for mosquito image classification by using hardware that could regulate the development process. In particular, we constructed a dataset with 4120 images of Aedes mosquitoes that were older than 12 days old and had common morphological features that disappeared, and we illustrated how to set up supervised deep convolutional neural networks (DCNNs) with hyperparameter adjustment. The model application was first conducted by deploying the model externally in real time on three different generations of mosquitoes, and the accuracy was compared with human expert performance. Our results showed that both the learning rate and epochs significantly affected the accuracy, and the best-performing hyperparameters achieved an accuracy of more than 98% at classifying mosquitoes, which showed no significant difference from human-level performance. We demonstrated the feasibility of the method to construct a model with the DCNN when deployed externally on mosquitoes in real time.

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