scholarly journals A Fully Automated Pipeline for a Robust Conjunctival Hyperemia Estimation

2021 ◽  
Vol 11 (7) ◽  
pp. 2978
Author(s):  
Nico Curti ◽  
Enrico Giampieri ◽  
Fabio Guaraldi ◽  
Federico Bernabei ◽  
Laura Cercenelli ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Network Model ◽  
Neural Network Model ◽  
Automated Analysis ◽  
Conjunctival Hyperemia ◽  
Average Accuracy ◽  
Automated Pipeline ◽  
Spearman’S Correlation ◽  
Conjunctival Redness

Purpose: Many semi-automated and fully-automated approaches have been proposed in literature to improve the objectivity of the estimation of conjunctival hyperemia, based on image processing analysis of eyes’ photographs. The purpose is to improve its evaluation using faster fully-automated systems and independent by the human subjectivity. Methods: In this work, we introduce a fully-automated analysis of the redness grading scales able to completely automatize the clinical procedure, starting from the acquired image to the redness estimation. In particular, we introduce a neural network model for the conjunctival segmentation followed by an image processing pipeline for the vessels network segmentation. From these steps, we extract some features already known in literature and whose correlation with the conjunctival redness has already been proved. Lastly, we implemented a predictive model for the conjunctival hyperemia using these features. Results: In this work, we used a dataset of images acquired during clinical practice.We trained a neural network model for the conjunctival segmentation, obtaining an average accuracy of 0.94 and a corresponding IoU score of 0.88 on a test set of images. The set of features extracted on these ROIs is able to correctly predict the Efron scale values with a Spearman’s correlation coefficient of 0.701 on a set of not previously used samples. Conclusions: The robustness of our pipeline confirms its possible usage in a clinical practice as a viable decision support system for the ophthalmologists.

scholarly journals Nonlinear Hyperparameter Optimization of a Neural Network in Image Processing for Micromachines

Micromachines ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1504
Author(s):  
Mingming Shen ◽  
Jing Yang ◽  
Shaobo Li ◽  
Ansi Zhang ◽  
Qiang Bai
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Deep Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Dropout Rate ◽  
Adjustment Process ◽  
Correlation Model ◽  
Mathematical Correlation ◽  
Verification Methods

Deep neural networks are widely used in the field of image processing for micromachines, such as in 3D shape detection in microelectronic high-speed dispensing and object detection in microrobots. It is already known that hyperparameters and their interactions impact neural network model performance. Taking advantage of the mathematical correlations between hyperparameters and the corresponding deep learning model to adjust hyperparameters intelligently is the key to obtaining an optimal solution from a deep neural network model. Leveraging these correlations is also significant for unlocking the “black box” of deep learning by revealing the mechanism of its mathematical principle. However, there is no complete system for studying the combination of mathematical derivation and experimental verification methods to quantify the impacts of hyperparameters on the performances of deep learning models. Therefore, in this paper, the authors analyzed the mathematical relationships among four hyperparameters: the learning rate, batch size, dropout rate, and convolution kernel size. A generalized multiparameter mathematical correlation model was also established, which showed that the interaction between these hyperparameters played an important role in the neural network’s performance. Different experiments were verified by running convolutional neural network algorithms to validate the proposal on the MNIST dataset. Notably, this research can help establish a universal multiparameter mathematical correlation model to guide the deep learning parameter adjustment process.

scholarly journals Computer image processing and neural network technology for thermal energy diagnosis of boiler plants

2020 ◽  
Vol 24 (5 Part B) ◽  
pp. 3221-3228
Author(s):  
Junzhen Zhang
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Network Model ◽  
Thermal Energy ◽  
Neural Network Model ◽  
Computer Image ◽  
Network Technology ◽  
Flame Combustion ◽  
Computer Image Processing ◽  
Neural Network Technology

Objective: The computer image processing and neural network technology are applied to diagnose the thermal energy of boiler plants, i. e., the flame combustion diagnosis, to verify their effectiveness and superiority. Methods: First, the YD-NQ type endoscopic high temperature video acquisition system is used to collect the images of flame combustion. Second, the images are pre-processed by the gray-scale method and the median filtering method. Then the flame combustion parameter features are extracted. The neural network algorithm is improved, and the boiler combustion model based on the improved neural network algorithm is established. Therefore, the combustion decision base is obtained. Finally, the improved neural network model is compared with the traditional neural network model and the 5-4 model to verify its validity. Results: The experiments have found that the improved neural network model is superior to the traditional neural network model. Meanwhile, its accuracy rate and confidence are relatively higher than those of the traditional model. In addition, a single sample also consumes shorter running time, which is 0.0075 seconds. Comparing with the 5-4 model, the improved neural network model has certain advantages, i. e., its accuracy rate and confidence are relatively higher, which are, respectively 91.28% and 96.69%, however, a single sample consumes longer running time than the 5-4 model. Conclusion: The experimental research has found that the application of computer image processing and neural network technology to the thermal energy diagnosis of boiler plants can effectively determine the stability of flame combustion, timely understand the state of flame combustion, and thus diagnose the thermal energy. Therefore, they have values for applications.

scholarly journals Action Recognition and State Change Prediction in a Recipe Understanding Task Using a Lightweight Neural Network Model (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13945-13946
Author(s):  
Qing Wan ◽  
Yoonsuck Choe
Keyword(s):  
Neural Network ◽  
Action Recognition ◽  
Network Model ◽  
Loss Function ◽  
Neural Network Model ◽  
Prediction Performance ◽  
State Change ◽  
Average Accuracy ◽  
Specific Step ◽  
Change Prediction

Consider a natural language sentence describing a specific step in a food recipe. In such instructions, recognizing actions (such as press, bake, etc.) and the resulting changes in the state of the ingredients (shape molded, custard cooked, temperature hot, etc.) is a challenging task. One way to cope with this challenge is to explicitly model a simulator module that applies actions to entities and predicts the resulting outcome (Bosselut et al. 2018). However, such a model can be unnecessarily complex. In this paper, we propose a simplified neural network model that separates action recognition and state change prediction, while coupling the two through a novel loss function. This allows learning to indirectly influence each other. Our model, although simpler, achieves higher state change prediction performance (67% average accuracy for ours vs. 55% in (Bosselut et al. 2018)) and takes fewer samples to train (10K ours vs. 65K+ by (Bosselut et al. 2018)).

Wilson-Cowan neural-network model in image processing

1992 ◽  
Vol 2 (2-3) ◽  
pp. 251-259 ◽  
Author(s):  
Kari Mantere ◽  
Jussi Parkkinen ◽  
Timo Jaaskelainen ◽  
Madan M. Gupta
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Network Model ◽  
Neural Network Model

Wilson—Cowan Neural-Network Model in Image Processing

1993 ◽  
pp. 155-163
Author(s):  
Kari Mantere ◽  
Jussi Parkkinen ◽  
Timo Jaaskelainen ◽  
Madan M. Gupta
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Network Model ◽  
Neural Network Model
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