scholarly journals Detection of Fuchs’ Uveitis Syndrome From Slit-Lamp Images Using Deep Convolutional Neural Networks in a Chinese Population

2021 ◽  
Vol 9 ◽  
Author(s):  
Wanyun Zhang ◽  
Zhijun Chen ◽  
Han Zhang ◽  
Guannan Su ◽  
Rui Chang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Independent Set ◽  
Network Models ◽  
Test Accuracy ◽  
Slit Lamp ◽  
Deep Convolutional Neural Networks ◽  
Receiver Operating Characteristic Curves ◽  
Optimized Model ◽  
Fuchs Uveitis Syndrome

Fuchs’ uveitis syndrome (FUS) is one of the most under- or misdiagnosed uveitis entities. Many undiagnosed FUS patients are unnecessarily overtreated with anti-inflammatory drugs, which may lead to serious complications. To offer assistance for ophthalmologists in the screening and diagnosis of FUS, we developed seven deep convolutional neural networks (DCNNs) to detect FUS using slit-lamp images. We also proposed a new optimized model with a mixed “attention” module to improve test accuracy. In the same independent set, we compared the performance between these DCNNs and ophthalmologists in detecting FUS. Seven different network models, including Xception, Resnet50, SE-Resnet50, ResNext50, SE-ResNext50, ST-ResNext50, and SET-ResNext50, were used to predict FUS automatically with the area under the receiver operating characteristic curves (AUCs) that ranged from 0.951 to 0.977. Our proposed SET-ResNext50 model (accuracy = 0.930; Precision = 0.918; Recall = 0.923; F1 measure = 0.920) with an AUC of 0.977 consistently outperformed the other networks and outperformed general ophthalmologists by a large margin. Heat-map visualizations of the SET-ResNext50 were provided to identify the target areas in the slit-lamp images. In conclusion, we confirmed that a trained classification method based on DCNNs achieved high effectiveness in distinguishing FUS from other forms of anterior uveitis. The performance of the DCNNs was better than that of general ophthalmologists and could be of value in the diagnosis of FUS.

scholarly journals Identifying Habitat Elements from Bird Images Using Deep Convolutional Neural Networks

Animals ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 1263
Author(s):  
Zhaojun Wang ◽  
Jiangning Wang ◽  
Congtian Lin ◽  
Yan Han ◽  
Zhaosheng Wang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Digital Technology ◽  
Rapid Development ◽  
Image Data ◽  
Image Database ◽  
Test Accuracy ◽  
Practical Application ◽  
Accuracy Rate ◽  
Deep Convolutional Neural Networks

With the rapid development of digital technology, bird images have become an important part of ornithology research data. However, due to the rapid growth of bird image data, it has become a major challenge to effectively process such a large amount of data. In recent years, deep convolutional neural networks (DCNNs) have shown great potential and effectiveness in a variety of tasks regarding the automatic processing of bird images. However, no research has been conducted on the recognition of habitat elements in bird images, which is of great help when extracting habitat information from bird images. Here, we demonstrate the recognition of habitat elements using four DCNN models trained end-to-end directly based on images. To carry out this research, an image database called Habitat Elements of Bird Images (HEOBs-10) and composed of 10 categories of habitat elements was built, making future benchmarks and evaluations possible. Experiments showed that good results can be obtained by all the tested models. ResNet-152-based models yielded the best test accuracy rate (95.52%); the AlexNet-based model yielded the lowest test accuracy rate (89.48%). We conclude that DCNNs could be efficient and useful for automatically identifying habitat elements from bird images, and we believe that the practical application of this technology will be helpful for studying the relationships between birds and habitat elements.

scholarly journals Transfer learning with pre-trained deep convolutional neural networks for the automatic assessment of liver steatosis in ultrasound images

2020 ◽  
Author(s):  
Elena Codruta Constantinescu ◽  
Anca-Loredana Udriștoiu ◽  
Ștefan Cristinel Udriștoiu ◽  
Andreea Valentina Iacob ◽  
Lucian Gheorghe Gruionu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Fatty Liver ◽  
Receiver Operating Characteristic ◽  
Convolutional Neural Networks ◽  
Operating Characteristic ◽  
Liver Steatosis ◽  
Normal Liver ◽  
Test Accuracy ◽  
Receiver Operating Characteristic Curves ◽  
Proposed Model

Aim: In this paper we proposed different architectures of convolutional neural network (CNN) to classify fatty liver disease in images using only pixels and diagnosis labels as input. We trained and validated our models using a dataset of 629 images consisting of 2 types of liver images, normal and liver steatosis. Material and methods: We assessed two pre-trained models of convolutional neural networks, Inception-v3 and VGG-16 using fine-tuning. Both models were pre-trained on ImageNet dataset to extract features from B-mode ultrasound liver images. The results obtained through these methods were compared for selecting the predictive model with the best performance metrics. We trained the two models using a dataset of 262 images of liver steatosis and 234 images of normal liver. We assessed the models using a dataset of 70 liver steatosis im-ages and 63 normal liver images. Results. The proposed model that used Inception v3 obtained a 93.23% test accuracy with a sensitivity of 89.9%% and a precision of 96.6%, and areas under each receiver operating characteristic curves (ROC AUC) of 0.93. The other proposed model that used VGG-16, obtained a 90.77% test accuracy with a sensitivity of 88.9% and a precision of 92.85%, and areas under each receiver operating characteristic curves (ROC AUC) of 0.91. Conclusion. The deep learning algorithms that we proposed to detect steatosis and classify the images in normal and fatty liver images, yields an excellent test performance of over 90%. However, future larger studies are required in order to establish how these algorithms can be implemented in a clinical setting.

scholarly journals A Deep Convolutional Neural Network Approach to Sign Alphabet Recognition

2021 ◽  
pp. 172-182
Author(s):  
Uday Kumar Adusumilli ◽  
Sanjana M S ◽  
Teja S ◽  
Yashawanth K M ◽  
Raghavendra R ◽  
...  
Keyword(s):  
Neural Networks ◽  
Sign Language ◽  
Convolutional Neural Networks ◽  
Skin Color ◽  
Color Space ◽  
Test Accuracy ◽  
Hand Detection ◽  
Deep Convolutional Neural Networks ◽  
Neural Network Approach ◽  
Real Time Processing

In this paper, we present an application that has been developed to be used as a tool for the purposes of learning sign language for beginners that utilizes hand detection as part of the process. It uses a skin-color modelling technique, such as explicit thresholding in the skin-color space, which is based on modeling skin-color spaces. This predetermined range of skin-colors is used to determine how pixels (hand) will be extracted from non-pixels (background). To classify the images, convolutional neural networks (CNN) were fed the images for the creation of the classifier. The training of the images was done using Keras. A uniform background and proper lighting conditions enabled the system to achieve a test accuracy of 93.67%, of which 90.04% was attributed to ASL alphabet recognition, 93.44% for number recognition and 97.52% recognition of static words, surpassing other studies of the type. An approach which is based on this technique is used for fast computation as well as real-time processing. Deaf-dumb people face a number of social challenges as the communication barrier prevents them from accessing basic and essential services of the life that they are entitled to as members of the hearing community. In spite of the fact that a number of factors have been incorporated into the innovations in the automatic recognition of sign language, an adequate solution has yet to be reached because of a number of challenges. As far as I know, the vast majority of existing works focus on developing vision based recognizers by deriving complex feature descriptors from captured images of the gestures and applying a classical pattern analysis technique. Although utilizing these methods can be effective when dealing with small sign vocabulary captures with a complex and uncontrolled background, they are very limited when dealing with large sign vocabulary. This paper proposes a method for analyzing and representing hand gestures, which acts as the core component of the vocabulary for signing languages, using a deep convolutional neural networks (CNN) architecture. On two publicly accessible datasets (the NUS hand posture dataset and the American fingerspelling A dataset), the method was demonstrated to be more accurate in recognizing hand postures.

scholarly journals Using Deep Convolutional Neural Networks for Image-Based Diagnosis of Nutrient Deficiencies in Rice

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Zhe Xu ◽  
Xi Guo ◽  
Anfan Zhu ◽  
Xiaolin He ◽  
Xiaomin Zhao ◽  
...  
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Fine Tuning ◽  
Support Vector ◽  
Test Accuracy ◽  
Nutrient Deficiencies ◽  
Deep Convolutional Neural Networks ◽  
Histogram Of Oriented Gradient

Symptoms of nutrient deficiencies in rice plants often appear on the leaves. The leaf color and shape, therefore, can be used to diagnose nutrient deficiencies in rice. Image classification is an efficient and fast approach for this diagnosis task. Deep convolutional neural networks (DCNNs) have been proven to be effective in image classification, but their use to identify nutrient deficiencies in rice has received little attention. In the present study, we explore the accuracy of different DCNNs for diagnosis of nutrient deficiencies in rice. A total of 1818 photographs of plant leaves were obtained via hydroponic experiments to cover full nutrition and 10 classes of nutrient deficiencies. The photographs were divided into training, validation, and test sets in a 3 : 1 : 1 ratio. Fine-tuning was performed to evaluate four state-of-the-art DCNNs: Inception-v3, ResNet with 50 layers, NasNet-Large, and DenseNet with 121 layers. All the DCNNs obtained validation and test accuracies of over 90%, with DenseNet121 performing best (validation accuracy = 98.62 ± 0.57%; test accuracy = 97.44 ± 0.57%). The performance of the DCNNs was validated by comparison to color feature with support vector machine and histogram of oriented gradient with support vector machine. This study demonstrates that DCNNs provide an effective approach to diagnose nutrient deficiencies in rice.

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

scholarly journals Deep convolutional neural networks for cardiovascular vulnerable plaque detection

2019 ◽  
Vol 277 ◽  
pp. 02024 ◽  
Author(s):  
Lincan Li ◽  
Tong Jia ◽  
Tianqi Meng ◽  
Yizhe Liu
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Vulnerable Plaque ◽  
Recall Rate ◽  
Superior Performance ◽  
Learning Approaches ◽  
Deep Convolutional Neural Networks ◽  
Vulnerable Plaques ◽  
Plaque Detection

In this paper, an accurate two-stage deep learning method is proposed to detect vulnerable plaques in ultrasonic images of cardiovascular. Firstly, a Fully Convonutional Neural Network (FCN) named U-Net is used to segment the original Intravascular Optical Coherence Tomography (IVOCT) cardiovascular images. We experiment on different threshold values to find the best threshold for removing noise and background in the original images. Secondly, a modified Faster RCNN is adopted to do precise detection. The modified Faster R-CNN utilize six-scale anchors (122,162,322,642,1282,2562) instead of the conventional one scale or three scale approaches. First, we present three problems in cardiovascular vulnerable plaque diagnosis, then we demonstrate how our method solve these problems. The proposed method in this paper apply deep convolutional neural networks to the whole diagnostic procedure. Test results show the Recall rate, Precision rate, IoU (Intersection-over-Union) rate and Total score are 0.94, 0.885, 0.913 and 0.913 respectively, higher than the 1st team of CCCV2017 Cardiovascular OCT Vulnerable Plaque Detection Challenge. AP of the designed Faster RCNN is 83.4%, higher than conventional approaches which use one-scale or three-scale anchors. These results demonstrate the superior performance of our proposed method and the power of deep learning approaches in diagnose cardiovascular vulnerable plaques.

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