scholarly journals Convolutional neural networks for mild diabetic retinopathy detection: an experimental study

2019 ◽  
Author(s):  
Rubina Sarki ◽  
Sandra Michalska ◽  
Khandakar Ahmed ◽  
Hua Wang ◽  
Yanchun Zhang
Keyword(s):  
Neural Networks ◽  
Diabetic Retinopathy ◽  
Performance Improvement ◽  
Convolutional Neural Networks ◽  
Large Scale ◽  
Data Augmentation ◽  
Comprehensive Evaluation ◽  
Optimal Solution ◽  
Fine Tuning ◽  
Fundus Photography

AbstractCurrently, Diabetes and the associated Diabetic Retinopathy (DR) instances are increasing at an alarming rate. Numerous previous research has focused on automated DR detection from fundus photography. The classification of severe cases of pathological indications in the eye has achieved over 90% accuracy. Still, the mild cases are challenging to detect due to CNN inability to identify the subtle features, discrimnative of disease. The data used (i.e. annotated fundus photographies) was obtained from 2 publicly available sources – Messidor and Kaggle. The experiments were conducted with 13 Convolutional Neural Networks architectures, pre-trained on large-scale ImageNet database using the concept of Transfer Learning. Several performance improvement techniques were applied, such as: (i) fine-tuning, (ii) data augmentation, and (iii) volume increase. The results were measured against the standard Accuracy metric on the testing dataset. After the extensive experimentation, the maximum Accuracy of 86% on No DR/Mild DR classification task was obtained for ResNet50 model with fine-tuning (un-freeze and re-train the layers from 100 onwards), and RMSProp Optimiser trained on the combined Messidor + Kaggle (aug) datasets. Despite promising results, Deep learning continues to be an empirical approach that requires extensive experimentation in order to arrive at the most optimal solution. The comprehensive evaluation of numerous CNN architectures was conducted in order to facilitate an early DR detection. Furthermore, several performance improvement techniques were assessed to address the CNN limitation in subtle eye lesions identification. The model also included various levels of image quality (low/high resolution, under/over-exposure, out-of-focus etc.), in order to prove its robustness and ability to adapt to real-world conditions.

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 Baryon acoustic oscillations reconstruction using convolutional neural networks

2020 ◽  
Vol 501 (1) ◽  
pp. 1499-1510
Author(s):  
Tian-Xiang Mao ◽  
Jie Wang ◽  
Baojiu Li ◽  
Yan-Chuan Cai ◽  
Bridget Falck ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Large Scale ◽  
Initial Conditions ◽  
Signal To Noise Ratio ◽  
Fine Tuning ◽  
Reconstruction Method ◽  
Acoustic Oscillations ◽  
Deep Convolutional Neural Networks ◽  
Baryon Acoustic Oscillations

ABSTRACT We propose a new scheme to reconstruct the baryon acoustic oscillations (BAO) signal, which contains key cosmological information, based on deep convolutional neural networks (CNN). Trained with almost no fine tuning, the network can recover large-scale modes accurately in the test set: the correlation coefficient between the true and reconstructed initial conditions reaches $90{{\ \rm per\ cent}}$ at $k\le 0.2 \, h\mathrm{Mpc}^{-1}$, which can lead to significant improvements of the BAO signal-to-noise ratio down to $k\simeq 0.4\, h\mathrm{Mpc}^{-1}$. Since this new scheme is based on the configuration-space density field in sub-boxes, it is local and less affected by survey boundaries than the standard reconstruction method, as our tests confirm. We find that the network trained in one cosmology is able to reconstruct BAO peaks in the others, i.e. recovering information lost to non-linearity independent of cosmology. The accuracy of recovered BAO peak positions is far less than that caused by the difference in the cosmology models for training and testing, suggesting that different models can be distinguished efficiently in our scheme. It is very promising that our scheme provides a different new way to extract the cosmological information from the ongoing and future large galaxy surveys.

Breast Infrared Thermography Segmentation Based on Adaptive Tuning of a Fully Convolutional Network

2020 ◽  
Vol 16 (5) ◽  
pp. 611-621
Author(s):  
Mazhar Basyouni Tayel ◽  
Azza Mahmoud Elbagoury
Keyword(s):  
Neural Networks ◽  
Infrared Thermography ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
Semantic Segmentation ◽  
Image Features ◽  
Fine Tuning ◽  
Fully Convolutional Neural Networks ◽  
End To End

Background: Accurate segmentation of Breast Infrared Thermography is an important step for early detection of breast pathological changes. Automatic segmentation of Breast Infrared Thermography is a very challenging task, as it is difficult to find an accurate breast contour and extract regions of interest from it. Although several semi-automatic methods have been proposed for segmentation, their performance often depends on hand-crafted image features, as well as preprocessing operations. Objective: In this work, an approach to automatic semantic segmentation of the Breast Infrared Thermography is proposed based on end-to-end fully convolutional neural networks and without any pre or post-processing. Methods: The lack of labeled Breast Infrared Thermography data limits the complete utilization of fully convolutional neural networks. The proposed model overcomes this challenge by applying data augmentation and two-tier transfer learning from bigger datasets combined with adaptive multi-tier fine-tuning before training the fully convolutional neural networks model. Results: Experimental results show that the proposed approach achieves better segmentation results: 97.986% accuracy; 98.36% sensitivity and 97.61% specificity compared to hand-crafted segmentation methods. Conclusion: This work provided an end-to-end automatic semantic segmentation of Breast Infrared Thermography combined with fully convolutional networks, adaptive multi-tier fine-tuning and transfer learning. Also, this work was able to deal with challenges in applying convolutional neural networks on such data and achieving the state-of-the-art accuracy.

scholarly journals Large-Scale Printed Chinese Character Recognition for ID Cards Using Deep Learning and Few Samples Transfer Learning

2021 ◽  
Author(s):  
Yi-Quan Li ◽  
Hao-Sen Chang ◽  
Daw-Tung Lin
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Transfer Learning ◽  
Character Recognition ◽  
Large Scale ◽  
Data Augmentation ◽  
Training Data ◽  
Fine Tuning ◽  
Training Dataset ◽  
Model Parameters

In the field of computer vision, large-scale image classification tasks are both important and highly challenging. With the ongoing advances in deep learning and optical character recognition (OCR) technologies, neural networks designed to perform large-scale classification play an essential role in facilitating OCR systems. In this study, we developed an automatic OCR system designed to identify up to 13,070 large-scale printed Chinese characters by using deep learning neural networks and fine-tuning techniques. The proposed framework comprises four components, including training dataset synthesis and background simulation, image preprocessing and data augmentation, the process of training the model, and transfer learning. The training data synthesis procedure is composed of a character font generation step and a background simulation process. Three background models are proposed to simulate the factors of the background noise and anti-counterfeiting patterns on ID cards. To expand the diversity of the synthesized training dataset, rotation and zooming data augmentation are applied. A massive dataset comprising more than 19.6 million images was thus created to accommodate the variations in the input images and improve the learning capacity of the CNN model. Subsequently, we modified the GoogLeNet neural architecture by replacing the FC layer with a global average pooling layer to avoid overfitting caused by a massive amount of training data. Consequently, the number of model parameters was reduced. Finally, we employed the transfer learning technique to further refine the CNN model using a small number of real data samples. Experimental results show that the overall recognition performance of the proposed approach is significantly better than that of prior methods and thus demonstrate the effectiveness of proposed framework, which exhibited a recognition accuracy as high as 99.39% on the constructed real ID card dataset.

scholarly journals Data Augmentation Methods Applying Grayscale Images for Convolutional Neural Networks in Machine Vision

2021 ◽  
Vol 11 (15) ◽  
pp. 6721
Author(s):  
Jinyeong Wang ◽  
Sanghwan Lee
Keyword(s):  
Neural Networks ◽  
Machine Vision ◽  
Object Detection ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
Image Data ◽  
Manufacturing Productivity ◽  
Smart Factories ◽  
Grayscale Images

In increasing manufacturing productivity with automated surface inspection in smart factories, the demand for machine vision is rising. Recently, convolutional neural networks (CNNs) have demonstrated outstanding performance and solved many problems in the field of computer vision. With that, many machine vision systems adopt CNNs to surface defect inspection. In this study, we developed an effective data augmentation method for grayscale images in CNN-based machine vision with mono cameras. Our method can apply to grayscale industrial images, and we demonstrated outstanding performance in the image classification and the object detection tasks. The main contributions of this study are as follows: (1) We propose a data augmentation method that can be performed when training CNNs with industrial images taken with mono cameras. (2) We demonstrate that image classification or object detection performance is better when training with the industrial image data augmented by the proposed method. Through the proposed method, many machine-vision-related problems using mono cameras can be effectively solved by using CNNs.

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