Deep learning architectures for the analysis and classification of brain tumors in MR images

2020 ◽  
Author(s):  
A. Osorio-Barone ◽  
S. H. Contreras-Ortiz
Keyword(s):  
Deep Learning ◽  
Brain Tumors ◽  
Mr Images ◽  
Learning Architectures

scholarly journals An Effective Classification of Brain Tumor using Deep Learning Technique

2020 ◽  
Vol 8 (6) ◽  
pp. 100-104
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Learning Networks ◽  
Mr Images ◽  
Via Classification ◽  
Deep Learning Model ◽  
Modern Technologies ◽  
Health Experts

Health experts have increased taking advantage of the benefits of most modern technologies, thus generating a scalable improvement in the health care area. Because of this, there is a paradigm shift from manual monitoring towards more accurate virtual monitoring with minimum percentage of error. Advances in artificial intelligence (AI) led to exciting solutions with high accuracy for medical imaging technology and is a key method for enhancing future applications. Detection task of brain tumor is difficult in the medical field. Detection of brain tumors manually is time consuming and requires large number of MRI images for cancer diagnosis. So, there is a need for automatic brain tumors detection from Brain MR Images. Deep learning methods can achieve this task. Different deep learning networks can be used for the detection of brain tumors. The proposed method comprises a classification network which classifies the input MR images into 2 classes: one with tumor and the second without tumor. In this work, detection of brain tumor is done via classification by retraining the classifier using the technique known as transfer learning. The obtained result shows that our method works better than the existing methods. The most purpose of this project was to create a deep learning model that will classify if a subject features a growth or not based on MRI scan. I used the VGG-16, Inception v3, and Resnet.

Deep Learning-Based Binary Classification of ADHD Using Resting State MR Images

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Vikas Khullar ◽  
Karuna Salgotra ◽  
Harjit Pal Singh ◽  
Davinder Pal Sharma
Keyword(s):  
Deep Learning ◽  
Resting State ◽  
Binary Classification ◽  
Mr Images

scholarly journals A decision support system for multimodal brain tumor classification using deep learning

2021 ◽  
Author(s):  
Muhammad Imran Sharif ◽  
Muhammad Attique Khan ◽  
Musaed Alhussein ◽  
Khursheed Aurangzeb ◽  
Mudassar Raza
Keyword(s):  
Deep Learning ◽  
Brain Tumors ◽  
Imbalanced Data ◽  
Neural Nets ◽  
Threshold Function ◽  
Experimental Process ◽  
Feature Values ◽  
Multiclass Svm ◽  
Imbalanced Data Learning

AbstractMulticlass classification of brain tumors is an important area of research in the field of medical imaging. Since accuracy is crucial in the classification, a number of techniques are introduced by computer vision researchers; however, they still face the issue of low accuracy. In this article, a new automated deep learning method is proposed for the classification of multiclass brain tumors. To realize the proposed method, the Densenet201 Pre-Trained Deep Learning Model is fine-tuned and later trained using a deep transfer of imbalanced data learning. The features of the trained model are extracted from the average pool layer, which represents the very deep information of each type of tumor. However, the characteristics of this layer are not sufficient for a precise classification; therefore, two techniques for the selection of features are proposed. The first technique is Entropy–Kurtosis-based High Feature Values (EKbHFV) and the second technique is a modified genetic algorithm (MGA) based on metaheuristics. The selected features of the GA are further refined by the proposed new threshold function. Finally, both EKbHFV and MGA-based features are fused using a non-redundant serial-based approach and classified using a multiclass SVM cubic classifier. For the experimental process, two datasets, including BRATS2018 and BRATS2019, are used without increase and have achieved an accuracy of more than 95%. The precise comparison of the proposed method with other neural nets shows the significance of this work.

scholarly journals Deep learning model for metagenome fragment classification using spaced k-mers feature extraction

2020 ◽  
Vol 8 (3) ◽  
pp. 234-238
Author(s):  
Nur Choiriyati ◽  
Yandra Arkeman ◽  
Wisnu Ananta Kusuma
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Learning ◽  
Language Processing ◽  
Computational Time ◽  
Genus Level ◽  
Computational Resources ◽  
Learning Architectures ◽  
Deep Learning Model

An open challenge in bioinformatics is the analysis of the sequenced metagenomes from the various environments. Several studies demonstrated bacteria classification at the genus level using k-mers as feature extraction where the highest value of k gives better accuracy but it is costly in terms of computational resources and computational time. Spaced k-mers method was used to extract the feature of the sequence using 111 1111 10001 where 1 was a match and 0 was the condition that could be a match or did not match. Currently, deep learning provides the best solutions to many problems in image recognition, speech recognition, and natural language processing. In this research, two different deep learning architectures, namely Deep Neural Network (DNN) and Convolutional Neural Network (CNN), trained to approach the taxonomic classification of metagenome data and spaced k-mers method for feature extraction. The result showed the DNN classifier reached 90.89 % and the CNN classifier reached 88.89 % accuracy at the genus level taxonomy.

scholarly journals Recognization and Systematization of MR Images using K Means Clustering and DNN

2020 ◽  
Vol 9 (6) ◽  
pp. 924-927
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Digital Health ◽  
High Accuracy ◽  
Tumor Segmentation ◽  
Mr Images ◽  
Brain Tumor Segmentation ◽  
Brain Tumor Diagnosis ◽  
The Brain

Brain tumors are the result of unusual growth and unrestrained cell disunity in the brain. Most of the medical image application lack in segmentation and labeling. Brain tumors can lead to loss of lives if they are not detected early and correctly. Recently, deep learning has been an important role in the field of digital health. One of its action is the reduction of manual decision in the diagnosis of diseases specifically brain tumor diagnosis needs high accuracy, where minute errors in judgment may lead to loss therefore, brain tumor segmentation is an necessary challenge in medical side. In recent time numerous ,methods exist for tumor segmentation with lack of accuracy. Deep learning is used to achieve the goal of brain tumor segmentation. In this work, three network of brain MR images segmentation is employed .A single network is compared to achieve segmentation of MR images using separate network .In this paper segmentation has improved and result is obtained with high accuracy and efficiency.

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