Classification of Brain Tumors via Deep Learning Models

2020 ◽  
Author(s):  
Kaya DAGLI ◽  
Osman EROGUL
Keyword(s):  
Deep Learning ◽  
Brain Tumors ◽  
Learning Models

A Study on the Auxiliary Diagnosis of Thyroid Disease Images Based on Multiple Dimensional Deep Learning Algorithms

2020 ◽  
Vol 16 (3) ◽  
pp. 199-205
Author(s):  
Yuejun Liu ◽  
Yifei Xu ◽  
Xiangzheng Meng ◽  
Xuguang Wang ◽  
Tianxu Bai
Keyword(s):  
Deep Learning ◽  
Learning Algorithms ◽  
Region Of Interest ◽  
Classification Performance ◽  
Thyroid Diseases ◽  
Great Success ◽  
Learning Models ◽  
Good Classification Performance ◽  
Spect Images

Background: Medical imaging plays an important role in the diagnosis of thyroid diseases. In the field of machine learning, multiple dimensional deep learning algorithms are widely used in image classification and recognition, and have achieved great success. Objective: The method based on multiple dimensional deep learning is employed for the auxiliary diagnosis of thyroid diseases based on SPECT images. The performances of different deep learning models are evaluated and compared. Methods: Thyroid SPECT images are collected with three types, they are hyperthyroidism, normal and hypothyroidism. In the pre-processing, the region of interest of thyroid is segmented and the amount of data sample is expanded. Four CNN models, including CNN, Inception, VGG16 and RNN, are used to evaluate deep learning methods. Results: Deep learning based methods have good classification performance, the accuracy is 92.9%-96.2%, AUC is 97.8%-99.6%. VGG16 model has the best performance, the accuracy is 96.2% and AUC is 99.6%. Especially, the VGG16 model with a changing learning rate works best. Conclusion: The standard CNN, Inception, VGG16, and RNN four deep learning models are efficient for the classification of thyroid diseases with SPECT images. The accuracy of the assisted diagnostic method based on deep learning is higher than that of other methods reported in the literature.

scholarly journals Deep Learning Model Comparison for Vision-Based Classification of Full/Empty-Load Trucks in Earthmoving Operations

2019 ◽  
Vol 9 (22) ◽  
pp. 4871 ◽  
Author(s):  
Quan Liu ◽  
Chen Feng ◽  
Zida Song ◽  
Joseph Louis ◽  
Jian Zhou
Keyword(s):  
Deep Learning ◽  
Model Comparison ◽  
Surveillance Systems ◽  
Comparison Study ◽  
Learning Models ◽  
The Core ◽  
Dump Trucks ◽  
Deep Learning Model ◽  
Contact Field

Earthmoving is an integral civil engineering operation of significance, and tracking its productivity requires the statistics of loads moved by dump trucks. Since current truck loads’ statistics methods are laborious, costly, and limited in application, this paper presents the framework of a novel, automated, non-contact field earthmoving quantity statistics (FEQS) for projects with large earthmoving demands that use uniform and uncovered trucks. The proposed FEQS framework utilizes field surveillance systems and adopts vision-based deep learning for full/empty-load truck classification as the core work. Since convolutional neural network (CNN) and its transfer learning (TL) forms are popular vision-based deep learning models and numerous in type, a comparison study is conducted to test the framework’s core work feasibility and evaluate the performance of different deep learning models in implementation. The comparison study involved 12 CNN or CNN-TL models in full/empty-load truck classification, and the results revealed that while several provided satisfactory performance, the VGG16-FineTune provided the optimal performance. This proved the core work feasibility of the proposed FEQS framework. Further discussion provides model choice suggestions that CNN-TL models are more feasible than CNN prototypes, and models that adopt different TL methods have advantages in either working accuracy or speed for different tasks.

Open-source deep learning models for acoustic detection and classification of orcas

2021 ◽  
Vol 150 (4) ◽  
pp. A286-A286
Author(s):  
Sadman Sakib ◽  
Steven Bergner ◽  
Dave Campbell ◽  
Mike Dowd ◽  
Fabio Frazao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Open Source ◽  
Acoustic Detection ◽  
Learning Models

A Similarity-Based Object Classification Using Deep Neural Networks

2019 ◽  
pp. 197-219
Author(s):  
Parvathi R. ◽  
Pattabiraman V.
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Search Algorithm ◽  
Random Projection ◽  
Learning Networks ◽  
Learning Models ◽  
External Conditions ◽  
And Training

This chapter proposes a hybrid method for classification of the objects based on deep neural network and a similarity-based search algorithm. The objects are pre-processed with external conditions. After pre-processing and training different deep learning networks with the object dataset, the authors compare the results to find the best model to improve the accuracy of the results based on the features of object images extracted from the feature vector layer of a neural network. RPFOREST (random projection forest) model is used to predict the approximate nearest images. ResNet50, InceptionV3, InceptionV4, and DenseNet169 models are trained with this dataset. A proposal for adaptive finetuning of the deep learning models by determining the number of layers required for finetuning with the help of the RPForest model is given, and this experiment is conducted using the Xception model.

scholarly journals Deep Learning Models for Classification of Red Blood Cells in Microscopy Images to Aid in Sickle Cell Anemia Diagnosis

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 427 ◽  
Author(s):  
Laith Alzubaidi ◽  
Mohammed A. Fadhel ◽  
Omran Al-Shamma ◽  
Jinglan Zhang ◽  
Ye Duan
Keyword(s):  
Deep Learning ◽  
Red Blood Cells ◽  
Transfer Learning ◽  
Sickle Cell ◽  
Sickle Cell Anemia ◽  
Blood Cells ◽  
Learning Models ◽  
Sickle Cells ◽  
Microscopy Images

Sickle cell anemia, which is also called sickle cell disease (SCD), is a hematological disorder that causes occlusion in blood vessels, leading to hurtful episodes and even death. The key function of red blood cells (erythrocytes) is to supply all the parts of the human body with oxygen. Red blood cells (RBCs) form a crescent or sickle shape when sickle cell anemia affects them. This abnormal shape makes it difficult for sickle cells to move through the bloodstream, hence decreasing the oxygen flow. The precise classification of RBCs is the first step toward accurate diagnosis, which aids in evaluating the danger level of sickle cell anemia. The manual classification methods of erythrocytes require immense time, and it is possible that errors may be made throughout the classification stage. Traditional computer-aided techniques, which have been employed for erythrocyte classification, are based on handcrafted features techniques, and their performance relies on the selected features. They also are very sensitive to different sizes, colors, and complex shapes. However, microscopy images of erythrocytes are very complex in shape with different sizes. To this end, this research proposes lightweight deep learning models that classify the erythrocytes into three classes: circular (normal), elongated (sickle cells), and other blood content. These models are different in the number of layers and learnable filters. The available datasets of red blood cells with sickle cell disease are very small for training deep learning models. Therefore, addressing the lack of training data is the main aim of this paper. To tackle this issue and optimize the performance, the transfer learning technique is utilized. Transfer learning does not significantly affect performance on medical image tasks when the source domain is completely different from the target domain. In some cases, it can degrade the performance. Hence, we have applied the same domain transfer learning, unlike other methods that used the ImageNet dataset for transfer learning. To minimize the overfitting effect, we have utilized several data augmentation techniques. Our model obtained state-of-the-art performance and outperformed the latest methods by achieving an accuracy of 99.54% with our model and 99.98% with our model plus a multiclass SVM classifier on the erythrocytesIDB dataset and 98.87% on the collected dataset.

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.

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