COVID-19 ResNet: Residual neural network for COVID-19 classification with bayesian data augmentation

COVID-19 is an infectious disease caused by a novel coronavirus called SARS-CoV-2. The first case appeared in December 2019, and until now it still represents a significant challenge to many countries in the world. Accurately detecting positive COVID-19 patients is a crucial step to reduce the spread of the disease, which is characterize by a strong transmission capacity. In this work we implement a Residual Convolutional Neural Network (ResNet) for an automated COVID-19 diagnosis. The implemented ResNet can classify a patient´s Chest-Xray image into COVID-19 positive, pneumonia caused from another virus or bacteria, and healthy. Moreover, to increase the accuracy of the model and overcome the data scarcity of COVID-19 images, a personalized data augmentation strategy using a three-step Bayesian hyperparameter optimization approach is applied to enrich the dataset during the training process. The proposed COVID-19 ResNet achieves a 94% accuracy, 95% recall, and 95% F1-score in test set. Furthermore, we also provide an insight into which data augmentation operations are successful in increasing a CNNs performance when doing medical image classification with COVID-19 CXR.

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Hyperparameter Optimization of Deep Neural Network in Multimodality Fused Medical Image Classification for Medical and Industrial IoT

Internet of Things - Smart Sensors for Industrial Internet of Things ◽

10.1007/978-3-030-52624-5_9 ◽

2021 ◽

pp. 127-146

Author(s):

Velmurugan Subbiah Parvathy ◽

Sivakumar Pothiraj ◽

Jenyfal Sampson

Keyword(s):

Neural Network ◽

Image Classification ◽

Medical Image ◽

Deep Neural Network ◽

Hyperparameter Optimization ◽

Industrial Iot ◽

Medical Image Classification

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Deep convolutional neural network based medical image classification for disease diagnosis

Journal Of Big Data ◽

10.1186/s40537-019-0276-2 ◽

2019 ◽

Vol 6 (1) ◽

Cited By ~ 38

Author(s):

Samir S. Yadav ◽

Shivajirao M. Jadhav

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Image Classification ◽

Transfer Learning ◽

Medical Image ◽

Data Augmentation ◽

Support Vector ◽

Improve Performance ◽

Classification Tasks ◽

Medical Image Classification

AbstractMedical image classification plays an essential role in clinical treatment and teaching tasks. However, the traditional method has reached its ceiling on performance. Moreover, by using them, much time and effort need to be spent on extracting and selecting classification features. The deep neural network is an emerging machine learning method that has proven its potential for different classification tasks. Notably, the convolutional neural network dominates with the best results on varying image classification tasks. However, medical image datasets are hard to collect because it needs a lot of professional expertise to label them. Therefore, this paper researches how to apply the convolutional neural network (CNN) based algorithm on a chest X-ray dataset to classify pneumonia. Three techniques are evaluated through experiments. These are linear support vector machine classifier with local rotation and orientation free features, transfer learning on two convolutional neural network models: Visual Geometry Group i.e., VGG16 and InceptionV3, and a capsule network training from scratch. Data augmentation is a data preprocessing method applied to all three methods. The results of the experiments show that data augmentation generally is an effective way for all three algorithms to improve performance. Also, Transfer learning is a more useful classification method on a small dataset compared to a support vector machine with oriented fast and rotated binary (ORB) robust independent elementary features and capsule network. In transfer learning, retraining specific features on a new target dataset is essential to improve performance. And, the second important factor is a proper network complexity that matches the scale of the dataset.

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Deep Convolution Neural Network with 2-Stage Transfer Learning for Medical Image Classification

The Brain & Neural Networks ◽

10.3902/jnns.24.3 ◽

2017 ◽

Vol 24 (1) ◽

pp. 3-12 ◽

Cited By ~ 1

Author(s):

Hayaru Shouno ◽

Aiga Suzuki ◽

Satoshi Suzuki ◽

Shoji Kido

Keyword(s):

Neural Network ◽

Image Classification ◽

Transfer Learning ◽

Medical Image ◽

Convolution Neural Network ◽

Deep Convolution Neural Network ◽

Medical Image Classification

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Web Application for Statistical Tracking and Predicting the Evolution of Active Cases with the Novel Coronavirus (SARS-CoV-2)

International Journal of Modeling and Optimization ◽

10.7763/ijmo.2021.v11.780 ◽

2021 ◽

pp. 70-74

Author(s):

Iulia Clitan ◽

◽

Adela Puscasiu ◽

Vlad Muresan ◽

Mihaela Ligia Unguresan ◽

...

Keyword(s):

Neural Network ◽

Mathematical Model ◽

Web Application ◽

Point Of View ◽

The Novel ◽

End User ◽

First Case ◽

Novel Coronavirus ◽

Second Wave ◽

Over Time

Since February 2020, when the first case of infection with SARS COV-2 virus appeared in Romania, the evolution of COVID-19 pandemic continues to have an ascending allure, reaching in September 2020 a second wave of infections as expected. In order to understand the evolution and spread of this disease over time and space, more and more research is focused on obtaining mathematical models that are able to predict the evolution of active cases based on different scenarios and taking into account the numerous inputs that influence the spread of this infection. This paper presents a web responsive application that allows the end user to analyze the evolution of the pandemic in Romania, graphically, and that incorporates, unlike other COVID-19 statistical applications, a prediction of active cases evolution. The prediction is based on a neural network mathematical model, described from the architectural point of view.

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DMCNN: A Deep Multiscale Convolutional Neural Network Model for Medical Image Segmentation

Journal of Healthcare Engineering ◽

10.1155/2019/8597606 ◽

2019 ◽

Vol 2019 ◽

pp. 1-10 ◽

Cited By ~ 2

Author(s):

Lin Teng ◽

Hang Li ◽

Shahid Karim

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Image Segmentation ◽

Convolutional Neural Network ◽

Medical Image ◽

Data Augmentation ◽

Medical Images ◽

Medical Image Segmentation ◽

Related Area ◽

Segmentation Accuracy

Medical image segmentation is one of the hot issues in the related area of image processing. Precise segmentation for medical images is a vital guarantee for follow-up treatment. At present, however, low gray contrast and blurred tissue boundaries are common in medical images, and the segmentation accuracy of medical images cannot be effectively improved. Especially, deep learning methods need more training samples, which lead to time-consuming process. Therefore, we propose a novelty model for medical image segmentation based on deep multiscale convolutional neural network (CNN) in this article. First, we extract the region of interest from the raw medical images. Then, data augmentation is operated to acquire more training datasets. Our proposed method contains three models: encoder, U-net, and decoder. Encoder is mainly responsible for feature extraction of 2D image slice. The U-net cascades the features of each block of the encoder with those obtained by deconvolution in the decoder under different scales. The decoding is mainly responsible for the upsampling of the feature graph after feature extraction of each group. Simulation results show that the new method can boost the segmentation accuracy. And, it has strong robustness compared with other segmentation methods.

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Deep Convolution Neural Network for Big Data Medical Image Classification

IEEE Access ◽

10.1109/access.2020.2998808 ◽

2020 ◽

Vol 8 ◽

pp. 105659-105670 ◽

Cited By ~ 1

Author(s):

Rehan Ashraf ◽

Muhammad Asif Habib ◽

Muhammad Akram ◽

Muhammad Ahsan Latif ◽

Muhammad Sheraz Arshad Malik ◽

...

Keyword(s):

Neural Network ◽

Big Data ◽

Image Classification ◽

Medical Image ◽

Convolution Neural Network ◽

Deep Convolution Neural Network ◽

Medical Image Classification

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A Data Augmentation Strategy Based on Simulated Samples for Ship Detection in RGB Remote Sensing Images

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8060276 ◽

2019 ◽

Vol 8 (6) ◽

pp. 276 ◽

Cited By ~ 8

Author(s):

Yiming Yan ◽

Zhichao Tan ◽

Nan Su

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Data Augmentation ◽

Three Dimensional ◽

Small Sample ◽

Optical Remote Sensing ◽

Ship Detection ◽

Training Samples ◽

Augmentation Strategy ◽

Three Dimensional Models

In this paper, we propose a data augmentation method for ship detection. Inshore ship detection using optical remote sensing imaging is a challenging task owing to an insufficient number of training samples. Although the multilayered neural network method has achieved excellent results in recent research, a large number of training samples is indispensable to guarantee the accuracy and robustness of ship detection. The majority of researchers adopt such strategies as clipping, scaling, color transformation, and flipping to enhance the samples. Nevertheless, these methods do not essentially increase the quality of the dataset. A novel data augmentation strategy was thus proposed in this study by using simulated remote sensing ship images to augment the positive training samples. The simulated images are generated by true background images and three-dimensional models on the same scale as real ships. A faster region-based convolutional neural network (Faster R-CNN) based on Res101netwok was trained by the dataset, which is composed of both simulated and true images. A series of experiments is designed under small sample conditions; the experimental results show that better detection is obtained with our data augmentation strategy.

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Optimizing data augmentation policies for convolutional neural networks based on classification of sickle cells

10.5753/wvc.2020.13479 ◽

2020 ◽

Author(s):

Matheus Da Silva ◽

Larissa Rodrigues ◽

João Fernando Mari

Keyword(s):

Sickle Cell ◽

Network Architecture ◽

Data Augmentation ◽

Bayesian Optimization ◽

Optimization Approach ◽

Hyperparameter Optimization ◽

Bayesian Optimization Algorithm ◽

Sickle Cells ◽

Validation Procedure ◽

Image Transformations

Data augmentation is a key procedure in many image classification tasks, mainly to overcome the problem of small datasets. In this work, we exploit the data augmentation as a hyperparameter optimization approach. We tested our methods to classify erythrocytes to assist the diagnosis of sickle cell anemia. In this study, we proposed a data augmentation approach based on hyperparameter optimization to find the best augmentation policies through the Bayesian optimization algorithm. We also developed a convolutional neural network architecture from scratch and compared it with two classic architectures to classify sickle cell images. Our approach defines the best data augmentation solutions and sends those solutions to be carried out by CNN in the final training. All experiments were validated using a stratified five-fold cross-validation procedure, and our best result achieves 92.54% of accuracy. The results suggest the best augmentation policies defined with optimization allow us to obtain superior results than other strategies such as without data augmentation, several randomly defined image transformations, and only random rotations. As far as we know, our paper is the first to propose optimizing data augmentation policies in biomedical images leading to a better exploration of these strategies in several fields.

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