USE OF CONVOLUTIONAL NEURAL NETWORKS FOR X-RAY IMAGE ORIENTATION DETERMINATION

2021 ◽  
Author(s):  
Sandi Baressi Šegota ◽  
◽  
Simon Lysdahlgaard ◽  
Søren Hess ◽  
Ronald Antulov
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
High Performance ◽  
Data Augmentation ◽  
Classification Model ◽  
Orientation Sensitivity ◽  
X Ray ◽  
Artificial Neural Network Ann ◽  
Image Orientation ◽  
Single Orientation

The fact that Artificial Intelligence (AI) based algorithms exhibit a high performance on image classification tasks has been shown many times. Still, certain issues exist with the application of machine learning (ML) artificial neural network (ANN) algorithms. The best known is the need for a large amount of statistically varied data, which can be addressed with expanded collection or data augmentation. Other issues are also present. Convolutional neural networks (CNNs) show extremely high performance on image-shaped data. Despite their performance, CNNs exhibit a large issue which is the sensitivity to image orientation. Previous research shows that varying the orientation of images may greatly lower the performance of the trained CNN. This is especially problematic in certain applications, such as X-ray radiography, an example of which is presented here. Previous research shows that the performance of CNNs is higher when used on images in a single orientation (left or right), as opposed to the combination of both. This means that the data needs to be differentiated before it enters the classification model. In this paper, the CNN-based model for differentiation between left and right-oriented images is presented. Multiple CNNs are trained and tested, with the highest performing being the VGG16 architecture which achieved an Accuracy of 0.99 (+/- 0.01), and an AUC of 0.98 (+/- 0.01). These results show that CNNs can be used to address the issue of orientation sensitivity by splitting the data in advance of being used in classification models.

scholarly journals Automatic Evaluation of the Lung Condition of COVID-19 Patients Using X-ray Images and Convolutional Neural Networks

2021 ◽  
Vol 11 (1) ◽  
pp. 28
Author(s):  
Ivan Lorencin ◽  
Sandi Baressi Šegota ◽  
Nikola Anđelić ◽  
Anđela Blagojević ◽  
Tijana Šušteršić ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Clinical Picture ◽  
Data Augmentation ◽  
Lower Amount ◽  
Training Procedure ◽  
X Ray ◽  
Severe Clinical Picture ◽  
Lung Condition

COVID-19 represents one of the greatest challenges in modern history. Its impact is most noticeable in the health care system, mostly due to the accelerated and increased influx of patients with a more severe clinical picture. These facts are increasing the pressure on health systems. For this reason, the aim is to automate the process of diagnosis and treatment. The research presented in this article conducted an examination of the possibility of classifying the clinical picture of a patient using X-ray images and convolutional neural networks. The research was conducted on the dataset of 185 images that consists of four classes. Due to a lower amount of images, a data augmentation procedure was performed. In order to define the CNN architecture with highest classification performances, multiple CNNs were designed. Results show that the best classification performances can be achieved if ResNet152 is used. This CNN has achieved AUCmacro¯ and AUCmicro¯ up to 0.94, suggesting the possibility of applying CNN to the classification of the clinical picture of COVID-19 patients using an X-ray image of the lungs. When higher layers are frozen during the training procedure, higher AUCmacro¯ and AUCmicro¯ values are achieved. If ResNet152 is utilized, AUCmacro¯ and AUCmicro¯ values up to 0.96 are achieved if all layers except the last 12 are frozen during the training procedure.

scholarly journals Image Classification for the Automatic Feature Extraction in Human Worn Fashion Data

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 624
Author(s):  
Stefan Rohrmanstorfer ◽  
Mikhail Komarov ◽  
Felix Mödritscher
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Image Data ◽  
Classification Model ◽  
Upper Body ◽  
Automatic Feature Extraction

With the always increasing amount of image data, it has become a necessity to automatically look for and process information in these images. As fashion is captured in images, the fashion sector provides the perfect foundation to be supported by the integration of a service or application that is built on an image classification model. In this article, the state of the art for image classification is analyzed and discussed. Based on the elaborated knowledge, four different approaches will be implemented to successfully extract features out of fashion data. For this purpose, a human-worn fashion dataset with 2567 images was created, but it was significantly enlarged by the performed image operations. The results show that convolutional neural networks are the undisputed standard for classifying images, and that TensorFlow is the best library to build them. Moreover, through the introduction of dropout layers, data augmentation and transfer learning, model overfitting was successfully prevented, and it was possible to incrementally improve the validation accuracy of the created dataset from an initial 69% to a final validation accuracy of 84%. More distinct apparel like trousers, shoes and hats were better classified than other upper body clothes.

scholarly journals X-ray image based pneumonia classification using convolutional neural networks

2020 ◽  
Vol 6 (20) ◽  
pp. 54-67
Author(s):  
Sarah Badr AlSumairi ◽  
Mohamed Maher Ben Ismail
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Network Architecture ◽  
Data Augmentation ◽  
Critical Factor ◽  
Classification Problem ◽  
X Rays ◽  
X Ray ◽  
Erroneous Decision ◽  
Short Period

Pneumonia is an infectious disease of the lungs. About one third to one half of pneumonia cases are caused by bacteria. Early diagnosis is a critical factor for a successful treatment process. Typically, the disease can be diagnosed by a radiologist using chest X-ray images. In fact, chest X-rays are currently the best available method for diagnosing pneumonia. However, the recognition of pneumonia symptoms is a challenging task that relies on the availability of expert radiologists. Such “human” diagnosis can be inaccurate and subjective due to lack of clarity and erroneous decision. Moreover, the error can increase more if the physician is requested to analyze tens of X-rays within a short period of time. Therefore, Computer-Aided Diagnosis (CAD) systems were introduced to support and assist physicians and make their efforts more productive. In this paper, we investigate, design, implement and assess customized Convolutional Neural Networks to overcome the image-based Pneumonia classification problem. Namely, ResNet-50 and DenseNet-161 models were inherited to design customized deep network architecture and improve the overall pneumonia classification accuracy. Moreover, data augmentation was deployed and associated with standard datasets to assess the proposed models. Besides, standard performance measures were used to validate and evaluate the proposed system.

scholarly journals An Improved VGG16 Model for Pneumonia Image Classification

2021 ◽  
Vol 11 (23) ◽  
pp. 11185
Author(s):  
Zhi-Peng Jiang ◽  
Yi-Yang Liu ◽  
Zhen-En Shao ◽  
Ko-Wei Huang
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Image Recognition ◽  
Medical Image ◽  
Best Practice ◽  
Data Augmentation ◽  
X Rays ◽  
Neural Network Models ◽  
X Ray ◽  
Medical Image Recognition

Image recognition has been applied to many fields, but it is relatively rarely applied to medical images. Recent significant deep learning progress for image recognition has raised strong research interest in medical image recognition. First of all, we found the prediction result using the VGG16 model on failed pneumonia X-ray images. Thus, this paper proposes IVGG13 (Improved Visual Geometry Group-13), a modified VGG16 model for classification pneumonia X-rays images. Open-source thoracic X-ray images acquired from the Kaggle platform were employed for pneumonia recognition, but only a few data were obtained, and datasets were unbalanced after classification, either of which can result in extremely poor recognition from trained neural network models. Therefore, we applied augmentation pre-processing to compensate for low data volume and poorly balanced datasets. The original datasets without data augmentation were trained using the proposed and some well-known convolutional neural networks, such as LeNet AlexNet, GoogLeNet and VGG16. In the experimental results, the recognition rates and other evaluation criteria, such as precision, recall and f-measure, were evaluated for each model. This process was repeated for augmented and balanced datasets, with greatly improved metrics such as precision, recall and F1-measure. The proposed IVGG13 model produced superior outcomes with the F1-measure compared with the current best practice convolutional neural networks for medical image recognition, confirming data augmentation effectively improved model accuracy.

scholarly journals A comprehensive study on classification of COVID-19 on computed tomography with pretrained convolutional neural networks

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Tuan D. Pham
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
High Performance ◽  
Data Augmentation ◽  
Ct Scans ◽  
Automated Classification ◽  
Imaging Data ◽  
Use Of Data

Abstract The use of imaging data has been reported to be useful for rapid diagnosis of COVID-19. Although computed tomography (CT) scans show a variety of signs caused by the viral infection, given a large amount of images, these visual features are difficult and can take a long time to be recognized by radiologists. Artificial intelligence methods for automated classification of COVID-19 on CT scans have been found to be very promising. However, current investigation of pretrained convolutional neural networks (CNNs) for COVID-19 diagnosis using CT data is limited. This study presents an investigation on 16 pretrained CNNs for classification of COVID-19 using a large public database of CT scans collected from COVID-19 patients and non-COVID-19 subjects. The results show that, using only 6 epochs for training, the CNNs achieved very high performance on the classification task. Among the 16 CNNs, DenseNet-201, which is the deepest net, is the best in terms of accuracy, balance between sensitivity and specificity, $$F_1$$ F 1 score, and area under curve. Furthermore, the implementation of transfer learning with the direct input of whole image slices and without the use of data augmentation provided better classification rates than the use of data augmentation. Such a finding alleviates the task of data augmentation and manual extraction of regions of interest on CT images, which are adopted by current implementation of deep-learning models for COVID-19 classification.

scholarly journals Evaluating Convolutional Neural Networks for COVID-19 classification in chest X-ray images

2020 ◽  
Author(s):  
Leonardo Rodrigues ◽  
Larissa Rodrigues ◽  
Danilo Da Silva ◽  
João Fernando Mari
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
Relevant Information ◽  
Fine Tuning ◽  
Machine Learning Techniques ◽  
Disease Spread ◽  
X Ray ◽  
Chest X Ray ◽  
Scalable Methods

Coronavirus Disease 2019 (COVID-19) pandemic rapidly spread globally, impacting the lives of billions of people. The effective screening of infected patients is a critical step to struggle with COVID-19, and treating the patients avoiding this quickly disease spread. The need for automated and scalable methods has increased due to the unavailability of accurate automated toolkits. Recent researches using chest X-ray images suggest they include relevant information about the COVID-19 virus. Hence, applying machine learning techniques combined with radiological imaging promises to identify this disease accurately. It is straightforward to collect these images once it is spreadly shared and analyzed in the world. This paper presents a method for automatic COVID-19 detection using chest Xray images through four convolutional neural networks, namely: AlexNet, VGG-11, SqueezeNet, and DenseNet-121. This method had been providing accurate diagnostics for positive or negative COVID-19 classification. We validate our experiments using a ten-fold cross-validation procedure over the training and test sets. Our findings include the shallow fine-tuning and data augmentation strategies that can assist in dealing with the low number of positive COVID-19 images publicly available. The accuracy for all CNNs is higher than 97.00%, and the SqueezeNet model achieved the best result with 99.20%.

scholarly journals Deep Transfer Learning for COVID-19 Prediction: Case Study for Limited Data Problems

2020 ◽  
Vol 16 ◽  
Author(s):  
Saleh Albahli ◽  
Waleed Albattah
Keyword(s):  
Neural Networks ◽  
Transfer Learning ◽  
High Performance ◽  
Data Augmentation ◽  
Medical Image Analysis ◽  
Medical Community ◽  
X Ray ◽  
Normal Chest ◽  
Chest X Ray ◽  
Performance Results

Objective: Automatic prediction of COVID-19 using deep convolution neural networks based pre-trained transfer models and Chest X-ray images. Method: This research employs the advantages of computer vision and medical image analysis to develop an automated model that has the clinical potential for early detection of the disease. Using Deep Learning models, the research aims at evaluating the effectiveness and accuracy of different convolutional neural networks models in the automatic diagnosis of COVID-19 from X-ray images as compared to diagnosis performed by experts in the medical community. Result: Due to the fact that the dataset available for COVID-19 is still limited, the best model to use is the InceptionNetV3. Performance results show that the InceptionNetV3 model yielded the highest accuracy of 98.63% (with data augmentation) and 98.90% (without data augmentation) among the three models designed. However, as the dataset gets bigger, the Inception ResNetV2 and NASNetlarge will do a better job of classification. All the performed networks tend to over-fit when data augmentation is not used, this is due to the small amount of data used for training and validation. Conclusion: A deep transfer learning is proposed to detecting the COVID-19 automatically from chest X-ray by training it with X-ray images gotten from both COVID-19 patients and people with normal chest Xrays. The study is aimed at helping doctors in making decisions in their clinical practice due its high performance and effectiveness, the study also gives an insight to how transfer learning was used to automatically detect the COVID-19.

scholarly journals TRAFFIC SIGN RECOGNITION USING CONVOLUTIONAL NEURAL NETWORKS / KELIO ŽENKLŲ ATPAŽINIMAS NAUDOJANT NEURONINĮ TINKLĄ

2018 ◽  
Vol 10 (0) ◽  
pp. 1-5
Author(s):  
Ervin Miloš ◽  
Aliaksei Kolesau ◽  
Dmitrij Šešok
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
High Performance ◽  
Data Augmentation ◽  
Autonomous Driving ◽  
Histogram Equalization ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Sign Recognition ◽  
Computer Vision Applications

Traffic sign recognition is an important method that improves the safety in the roads, and this system is an additional step to autonomous driving. Nowadays, to solve traffic sign recognition problem, convolutional neural networks (CNN) can be adopted for its high performance well proved for computer vision applications. This paper proposes histogram equalization preprocessing (HOG) and CNN with additional operations – batch normalization, dropout and data augmentation. Several CNN architectures are compared to differentiate how each operation affects the accuracy of CNN model. Experimental results describe the effectiveness of using CNN with proposed operations. Santrauka Kelio ženklų atpažinimas – vienas iš svarbių būdų pagerinti saugumą keliuose. Ši sistema laikoma papildomu autonominio vairavimo žingsniu. Šiandien kelio ženklų atpažinimo problemai spręsti taikomi konvoliuciniai neuroniniai tinklai (KNN) dėl jų našumo, įrodyto vaizdų atpažinimo programose. Šiame straipsnyje siūlomas vaizdų histogramos išlyginimo apdorojimo metodas ir KNN su papildomomis operacijomis – paketo normalizavimas ir neuronų išjungimas / įjungimas. Yra palyginamos kelios KNN architektūros siekiant ištirti, kokią įtaką kiekviena operacija daro KNN modelio tikslumui. Eksperimentiniai rezultatai apibūdina KNN naudojimo efektyvumą su pasiūlytomis operacijomis.

Casting defect detection in X-ray images using convolutional neural networks and attention-guided data augmentation

Measurement ◽  
2021 ◽  
Vol 170 ◽  
pp. 108736
Author(s):  
Lili Jiang ◽  
Yongxiong Wang ◽  
Zhenhui Tang ◽  
Yinlong Miao ◽  
Shuyi Chen
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Defect Detection ◽  
Data Augmentation ◽  
Casting Defect ◽  
X Ray

scholarly journals Neural Style Transfer as Data Augmentation for Improving COVID-19 Diagnosis Classification

2021 ◽  
Vol 2 (5) ◽  
Author(s):  
Netzahualcoyotl Hernandez-Cruz ◽  
David Cato ◽  
Jesus Favela
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
Alternative Hypothesis ◽  
X Rays ◽  
Generative Adversarial Network ◽  
Style Transfer ◽  
X Ray ◽  
Adversarial Network ◽  
Training Samples

AbstractCoronavirus disease 2019 (COVID-19) has accounted for millions of causalities. While it affects not only individuals but also our collective healthcare and economic systems, testing is insufficient and costly hampering efforts to deal with the pandemic. Chest X-rays are routine radiographic imaging tests that are used for the diagnosis of respiratory conditions such as pneumonia and COVID-19. Convolutional neural networks have shown promise to be effective at classifying X-rays for assisting diagnosis of conditions; however, achieving robust performance demanded in most modern medical applications typically requires a large number of samples. While there exist datasets containing thousands of X-ray images of patients with healthy and pneumonia diagnoses, because COVID-19 is such a recent phenomenon, there are relatively few confirmed COVID-19 positive chest X-rays openly available to the research community. In this paper, we demonstrate the effectiveness of cycle-generative adversarial network, commonly used for neural style transfer, as a way to augment COVID-19 negative X-ray images to look like COVID-19 positive images for increasing the number of COVID-19 positive training samples. The statistical results show an increase in the mean macro f1-score over 21% on a one-tailed t score = 2.68 and p value = 0.01 to accept our alternative hypothesis for an $$\alpha = 0.05$$ α = 0.05 . We conclude that this approach, when used in conjunction with standard transfer learning techniques, is effective at improving the performance of COVID-19 classifiers for a variety of common convolutional neural networks.

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