Small-scale Convolutional Neural Networks with Learnable Gabor Filter for Image Classifications

2021 ◽  
Author(s):  
Zhengzhi Lu ◽  
Xiao Liang ◽  
Guoan Yang ◽  
Deyang Liu
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Gabor Filter ◽  
Small Scale

scholarly journals Going Deeper with Densely Connected Convolutional Neural Networks for Multispectral Pansharpening

2019 ◽  
Vol 11 (22) ◽  
pp. 2608 ◽  
Author(s):  
Dong Wang ◽  
Ying Li ◽  
Li Ma ◽  
Zongwen Bai ◽  
Jonathan Chan
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Gradient Flow ◽  
Superior Performance ◽  
Small Scale ◽  
Feature Maps ◽  
Spectral Angle Mapper ◽  
Residual Learning ◽  
Input Source ◽  
Scale Data

In recent years, convolutional neural networks (CNNs) have shown promising performance in the field of multispectral (MS) and panchromatic (PAN) image fusion (MS pansharpening). However, the small-scale data and the gradient vanishing problem have been preventing the existing CNN-based fusion approaches from leveraging deeper networks that potentially have better representation ability to characterize the complex nonlinear mapping relationship between the input (source) and the targeting (fused) images. In this paper, we introduce a very deep network with dense blocks and residual learning to tackle these problems. The proposed network takes advantage of dense connections in dense blocks that have connections for arbitrarily two convolution layers to facilitate gradient flow and implicit deep supervision during training. In addition, reusing feature maps can reduce the number of parameters, which is helpful for reducing overfitting that resulted from small-scale data. Residual learning is explored to reduce the difficulty for the model to generate the MS image with high spatial resolution. The proposed network is evaluated via experiments on three datasets, achieving competitive or superior performance, e.g. the spectral angle mapper (SAM) is decreased over 10% on GaoFen-2, when compared with other state-of-the-art methods.

scholarly journals Automatic Facial Recognition of Williams-Beuren Syndrome Based on Deep Convolutional Neural Networks

2021 ◽  
Vol 9 ◽  
Author(s):  
Hui Liu ◽  
Zi-Hua Mo ◽  
Hang Yang ◽  
Zheng-Fu Zhang ◽  
Dian Hong ◽  
...  
Keyword(s):  
Neural Networks ◽  
Face Recognition ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Facial Recognition ◽  
Small Scale ◽  
Healthy Children ◽  
Genetic Syndromes ◽  
Deep Convolutional Neural Networks ◽  
Genetic Syndrome

Background: Williams-Beuren syndrome (WBS) is a rare genetic syndrome with a characteristic “elfin” facial gestalt. The “elfin” facial characteristics include a broad forehead, periorbital puffiness, flat nasal bridge, short upturned nose, wide mouth, thick lips, and pointed chin. Recently, deep convolutional neural networks (CNNs) have been successfully applied to facial recognition for diagnosing genetic syndromes. However, there is little research on WBS facial recognition using deep CNNs.Objective: The purpose of this study was to construct an automatic facial recognition model for WBS diagnosis based on deep CNNs.Methods: The study enrolled 104 WBS children, 91 cases with other genetic syndromes, and 145 healthy children. The photo dataset used only one frontal facial photo from each participant. Five face recognition frameworks for WBS were constructed by adopting the VGG-16, VGG-19, ResNet-18, ResNet-34, and MobileNet-V2 architectures, respectively. ImageNet transfer learning was used to avoid over-fitting. The classification performance of the facial recognition models was assessed by five-fold cross validation, and comparison with human experts was performed.Results: The five face recognition frameworks for WBS were constructed. The VGG-19 model achieved the best performance. The accuracy, precision, recall, F1 score, and area under curve (AUC) of the VGG-19 model were 92.7 ± 1.3%, 94.0 ± 5.6%, 81.7 ± 3.6%, 87.2 ± 2.0%, and 89.6 ± 1.3%, respectively. The highest accuracy, precision, recall, F1 score, and AUC of human experts were 82.1, 65.9, 85.6, 74.5, and 83.0%, respectively. The AUCs of each human expert were inferior to the AUCs of the VGG-16 (88.6 ± 3.5%), VGG-19 (89.6 ± 1.3%), ResNet-18 (83.6 ± 8.2%), and ResNet-34 (86.3 ± 4.9%) models.Conclusions: This study highlighted the possibility of using deep CNNs for diagnosing WBS in clinical practice. The facial recognition framework based on VGG-19 could play a prominent role in WBS diagnosis. Transfer learning technology can help to construct facial recognition models of genetic syndromes with small-scale datasets.

scholarly journals Small-Scale Depthwise Separable Convolutional Neural Networks for Bacteria Classification

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 3005
Author(s):  
Duc-Tho Mai ◽  
Koichiro Ishibashi
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Recognition Accuracy ◽  
Computational Cost ◽  
Limited Resource ◽  
Vital Role ◽  
Small Scale ◽  
Automated Classification ◽  
Improving Accuracy ◽  
Low Computational Cost

Bacterial recognition and classification play a vital role in diagnosing disease by determining the presence of large bacteria in the specimens and the symptoms. Artificial intelligence and computer vision widely applied in the medical domain enable improving accuracy and reducing the bacterial recognition and classification time, which aids in making clinical decisions and choosing the proper treatment. This paper aims to provide an approach of 33 bacteria strains’ automated classification from the Digital Images of Bacteria Species (DIBaS) dataset based on small-scale depthwise separable convolutional neural networks. Our five-layer architecture has significant advantages due to the compact model, low computational cost, and reliable recognition accuracy. The experimental results proved that the proposed design reached the highest accuracy of 96.28% with a total of 6600 images and can be executed on limited-resource devices of 3.23 million parameters and 40.02 million multiply–accumulate operations (MACs). The number of parameters in this architecture is seven times less than the smallest model listed in the literature.

scholarly journals FRD-CNN: Object detection based on small-scale convolutional neural networks and feature reuse

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Wei Li ◽  
Kai Liu ◽  
Lin Yan ◽  
Fei Cheng ◽  
YunQiu Lv ◽  
...  
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Convolutional Neural Networks ◽  
Network Performance ◽  
Thin Layers ◽  
Detection Methods ◽  
Small Scale ◽  
Pascal Voc ◽  
Model Size

Abstract Most of the recent successful object detection methods have been based on convolutional neural networks (CNNs). From previous studies, we learned that many feature reuse methods improve the network performance, but they increase the number of parameters. DenseNet uses thin layers that have fewer channels to alleviate the increase in parameters. This motivated us to find other methods for solving the increase in model size problems introduced by feature reuse methods. In this work, we employ different feature reuse methods on fire units and mobile units. We solved the problem and constructed two novel neural networks, fire-FRD-CNN and mobile-FRD-CNN. We conducted experiments with the proposed neural networks on KITTI and PASCAL VOC datasets.

scholarly journals Biological sequence modeling with convolutional kernel networks

2019 ◽  
Vol 35 (18) ◽  
pp. 3294-3302 ◽  
Author(s):  
Dexiong Chen ◽  
Laurent Jacob ◽  
Julien Mairal
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Hybrid Approach ◽  
Training Data ◽  
Supplementary Information ◽  
Small Scale ◽  
Biological Sequences ◽  
Homology Detection ◽  
Binding Prediction ◽  
Sequence Modeling

Abstract Motivation The growing number of annotated biological sequences available makes it possible to learn genotype-phenotype relationships from data with increasingly high accuracy. When large quantities of labeled samples are available for training a model, convolutional neural networks can be used to predict the phenotype of unannotated sequences with good accuracy. Unfortunately, their performance with medium- or small-scale datasets is mitigated, which requires inventing new data-efficient approaches. Results We introduce a hybrid approach between convolutional neural networks and kernel methods to model biological sequences. Our method enjoys the ability of convolutional neural networks to learn data representations that are adapted to a specific task, while the kernel point of view yields algorithms that perform significantly better when the amount of training data is small. We illustrate these advantages for transcription factor binding prediction and protein homology detection, and we demonstrate that our model is also simple to interpret, which is crucial for discovering predictive motifs in sequences. Availability and implementation Source code is freely available at https://gitlab.inria.fr/dchen/CKN-seq. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Klasifikasi COVID-19 menggunakan Filter Gabor dan CNN dengan Hyperparameter Tuning

2021 ◽  
Vol 9 (3) ◽  
pp. 493
Author(s):  
AGUS EKO MINARNO ◽  
MOCHAMMAD HAZMI COKRO MANDIRI ◽  
MUHAMMAD RIFAL ALFARIZY
Keyword(s):  
Neural Networks ◽  
Ct Scan ◽  
Convolutional Neural Networks ◽  
Gabor Filter ◽  
Filter Method ◽  
Filter Model ◽  
Multiple Test ◽  
Model Filter ◽  
Lung Condition ◽  
Severity Of The Disease

ABSTRAKPenyakit COVID-19 dapat timbul karena berbagai faktor sebab dan akibat, sehingga penyakit ini memiliki efek buruk bagi penderita. Pencitraan CT-Scan memiliki keunggulan dalam memproyeksikan kondisi paru-paru pasien penderita, sehingga dapat membantu dalam mendeteksi tingkat keparahan penyakit. Dalam studi ini, penelitian dilakukan untuk mendeteksi penyakit COVID-19 melalui citra CT-Scan menggunakan metode Filter Gabor dan Convolutional Neural Networks (CNN) dengan Hyperparameter Tuning. Data yang digunakan yaitu citra CT-Scan SARSCoV-2 berjumlah 2481 gambar. Sebelum melatih model, dilakukan preprocessing data, seperti pelabelan, pengubahan ukuran, dan augmentasi gambar. Pengujian Model dilakukan dengan beberapa skenario uji. Hasil terbaik diperoleh pada skenario untuk model Filter Gabor dan CNN dengan Hyperparameter Tuning mendapatkan akurasi sebesar 97,9% dan AUC sebesar 99% dibandingkan dengan model tanpa Hyperparameter Tuning dan Filter Gabor.Kata kunci: COVID-19, CNN, Filter Gabor, Hyperparameter Tuning, COVID-19 Classification ABSTRACTCOVID-19 disease can arise due to various causal and causal factors, so it has an adverse effect on patients. CT-Scan imaging has an advantage in projecting the lung condition of patients with the patient, so it can help in detecting the severity of the disease. In this study, research was conducted to detect COVID-19 disease through CT-Scan imagery using Gabor Filter method and Convolutional Neural Networks (CNN) with Hyperparameter Tuning. The data used is CT-Scan SARSCoV-2 imagery amounting to 2481 images. Before training the model, preprocessing data is performed, such as labeling, resizing, and augmentation of images. Model testing is performed with multiple test scenarios. The best results were obtained in scenarios for The Gabor Filter model and CNN with Hyperparameter Tuning getting 97.9% accuracy and AUC by 99% compared to models without Hyperparameter Tuning and Gabor Filter.Keywords: COVID-19, CNN, Filter Gabor, Hyperparameter Tuning, COVID-19 Classification

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