Inference Performance Comparison of Convolutional Neural Networks on Edge Devices

2021 ◽  
pp. 323-335
Author(s):  
Sheikh Rufsan Reza ◽  
Yuzhong Yan ◽  
Xishuang Dong ◽  
Lijun Qian
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Performance Comparison

scholarly journals Performance of Fine-Tuning Convolutional Neural Networks for HEp-2 Image Classification

2020 ◽  
Vol 10 (19) ◽  
pp. 6940 ◽  
Author(s):  
Vincenzo Taormina ◽  
Donato Cascio ◽  
Leonardo Abbene ◽  
Giuseppe Raso
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Fluorescence Intensity ◽  
Characteristic Curve ◽  
Performance Comparison ◽  
Fine Tuning ◽  
Tuning Method ◽  
Effective Performance ◽  
Training Modalities

The search for anti-nucleus antibodies (ANA) represents a fundamental step in the diagnosis of autoimmune diseases. The test considered the gold standard for ANA research is indirect immunofluorescence (IIF). The best substrate for ANA detection is provided by Human Epithelial type 2 (HEp-2) cells. The first phase of HEp-2 type image analysis involves the classification of fluorescence intensity in the positive/negative classes. However, the analysis of IIF images is difficult to perform and particularly dependent on the experience of the immunologist. For this reason, the interest of the scientific community in finding relevant technological solutions to the problem has been high. Deep learning, and in particular the Convolutional Neural Networks (CNNs), have demonstrated their effectiveness in the classification of biomedical images. In this work the efficacy of the CNN fine-tuning method applied to the problem of classification of fluorescence intensity in HEp-2 images was investigated. For this purpose, four of the best known pre-trained networks were analyzed (AlexNet, SqueezeNet, ResNet18, GoogLeNet). The classifying power of CNN was investigated with different training modalities; three levels of freezing weights and scratch. Performance analysis was conducted, in terms of area under the ROC (Receiver Operating Characteristic) curve (AUC) and accuracy, using a public database. The best result achieved an AUC equal to 98.6% and an accuracy of 93.9%, demonstrating an excellent ability to discriminate between the positive/negative fluorescence classes. For an effective performance comparison, the fine-tuning mode was compared to those in which CNNs are used as feature extractors, and the best configuration found was compared with other state-of-the-art works.

scholarly journals Differentiation Between Anteroposterior and Posteroanterior Chest X-Ray View Position With Convolutional Neural Networks

2020 ◽  
Author(s):  
René Hosch ◽  
Lennard Kroll ◽  
Felix Nensa ◽  
Sven Koitka
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Performance Comparison ◽  
University Hospital ◽  
Hospital Data ◽  
X Ray ◽  
Ensemble Models ◽  
Class Distribution ◽  
Meta Information ◽  
Chest X Ray

Purpose Detection and validation of the chest X-ray view position with use of convolutional neural networks to improve meta-information for data cleaning within a hospital data infrastructure. Material and Methods Within this paper we developed a convolutional neural network which automatically detects the anteroposterior and posteroanterior view position of a chest radiograph. We trained two different network architectures (VGG variant and ResNet-34) with data published by the RSNA (26 684 radiographs, class distribution 46 % AP, 54 % PA) and validated these on a self-compiled dataset with data from the University Hospital Essen (4507, radiographs, class distribution 55 % PA, 45 % AP) labeled by a human reader. For visualization and better understanding of the network predictions, a Grad-CAM was generated for each network decision. The network results were evaluated based on the accuracy, the area under the curve (AUC), and the F1-score against the human reader labels. Also a final performance comparison between model predictions and DICOM labels was performed. Results The ensemble models reached accuracy and F1-scores greater than 95 %. The AUC reaches more than 0.99 for the ensemble models. The Grad-CAMs provide insight as to which anatomical structures contributed to a decision by the networks which are comparable with the ones a radiologist would use. Furthermore, the trained models were able to generalize over mislabeled examples, which was found by comparing the human reader labels to the predicted labels as well as the DICOM labels. Conclusion The results show that certain incorrectly entered meta-information of radiological images can be effectively corrected by deep learning in order to increase data quality in clinical application as well as in research. Key Points:  Citation Format

scholarly journals Convolutional neural networks performance comparison for handwritten Bengali numerals recognition

2019 ◽  
Vol 1 (12) ◽  
Author(s):  
Md. Moklesur Rahman ◽  
Md. Shafiqul Islam ◽  
Roberto Sassi ◽  
Md. Aktaruzzaman
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Performance Comparison

Performance Comparison of Deep Convolutional Neural Networks for Vespa Image Recognition

2019 ◽  
Vol 34 (3) ◽  
pp. 207-215 ◽  
Author(s):  
Cheol-Hee Lee ◽  
Yoon-Ju Jeong ◽  
Taeho Kim ◽  
Jae-Hyeon Park ◽  
Seongbin Bak ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Image Recognition ◽  
Performance Comparison ◽  
Deep Convolutional Neural Networks

scholarly journals Performance Comparison and Analysis for Large-Scale Crowd Counting Based on Convolutional Neural Networks

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 204425-204432
Author(s):  
Reem Alotaibi ◽  
Bander Alzahrani ◽  
Rui Wang ◽  
Tarik Alafif ◽  
Ahmed Barnawi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Large Scale ◽  
Performance Comparison ◽  
Crowd Counting ◽  
Comparison And Analysis

scholarly journals Classifying Image Stacks of Specular Silicon Wafer Back Surface Regions: Performance Comparison of CNNs and SVMs

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2056 ◽  
Author(s):  
Corinna Kofler ◽  
Robert Muhr ◽  
Gunter Spöck
Keyword(s):  
Neural Networks ◽  
Support Vector Machines ◽  
Silicon Wafer ◽  
Convolutional Neural Networks ◽  
Performance Comparison ◽  
Back Surface ◽  
Support Vector ◽  
Neural Net ◽  
Convolutional Network ◽  
Vector Machines

In this work, we compare the performance of convolutional neural networks and support vector machines for classifying image stacks of specular silicon wafer back surfaces. In these image stacks, we can identify structures typically originating from replicas of chip structures or from grinding artifacts such as comets or grinding grooves. However, defects like star cracks are also visible in those images. To classify these image stacks, we test and compare three different approaches. In the first approach, we train a convolutional neural net performing feature extraction and classification. In the second approach, we manually extract features of the images and use these features to train support vector machines. In the third approach, we skip the classification layers of the convolutional neural networks and use features extracted from different network layers to train support vector machines. Comparing these three approaches shows that all yield an accuracy value above 90%. With a quadratic support vector machine trained on features extracted from a convolutional network layer we achieve the best compromise between precision and recall rate of the class star crack with 99.3% and 98.6%, respectively.

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