Classification and Recognition of Urban Tree Defects in a Small Dataset using Convolutional Neural Network, Resnet-50 Architecture, and Data Augmentation

AbstractFinding the correct category of wear particles is important to understand the tribological behavior. However, manual identification is tedious and time-consuming. We here propose an automatic morphological residual convolutional neural network (M-RCNN), exploiting the residual knowledge and morphological priors between various particle types. We also employ data augmentation to prevent performance deterioration caused by the extremely imbalanced problem of class distribution. Experimental results indicate that our morphological priors are distinguishable and beneficial to largely boosting overall performance. M-RCNN demonstrates a much higher accuracy (0.940) than the deep residual network (0.845) and support vector machine (0.821). This work provides an effective solution for automatically identifying wear particles and can be a powerful tool to further analyze the failure mechanisms of artificial joints.

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Performance Evaluation of Convolutional Neural Network Using Synthetic Medical Data Augmentation Generated by GAN

International Journal of Image and Graphics ◽

10.1142/s021946782350002x ◽

2021 ◽

Author(s):

Ramesh Adhikari ◽

Suresh Pokharel

Keyword(s):

Neural Network ◽

Neural Networks ◽

Convolutional Neural Network ◽

Data Augmentation ◽

Medical Diagnostics ◽

Generative Adversarial Networks ◽

Generalization Capability ◽

X Ray ◽

Original Dataset ◽

Unseen Data

Data augmentation is widely used in image processing and pattern recognition problems in order to increase the richness in diversity of available data. It is commonly used to improve the classification accuracy of images when the available datasets are limited. Deep learning approaches have demonstrated an immense breakthrough in medical diagnostics over the last decade. A significant amount of datasets are needed for the effective training of deep neural networks. The appropriate use of data augmentation techniques prevents the model from over-fitting and thus increases the generalization capability of the network while testing afterward on unseen data. However, it remains a huge challenge to obtain such a large dataset from rare diseases in the medical field. This study presents the synthetic data augmentation technique using Generative Adversarial Networks to evaluate the generalization capability of neural networks using existing data more effectively. In this research, the convolutional neural network (CNN) model is used to classify the X-ray images of the human chest in both normal and pneumonia conditions; then, the synthetic images of the X-ray from the available dataset are generated by using the deep convolutional generative adversarial network (DCGAN) model. Finally, the CNN model is trained again with the original dataset and augmented data generated using the DCGAN model. The classification performance of the CNN model is improved by 3.2% when the augmented data were used along with the originally available dataset.

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Data Augmentation with 3DCG Models for Nuisance Wildlife Detection using a Convolutional Neural Network

10.12792/icisip2021.032 ◽

2021 ◽

Author(s):

Ryoke Naoya ◽

Hironori Kitakaze ◽

Ryo Matsumura

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Data Augmentation ◽

Nuisance Wildlife ◽

Wildlife Detection

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"LeukNet" - A Model of Convolutional Neural Network for the Diagnosis of Leukemia

10.5753/sibgrapi.est.2020.12993 ◽

2020 ◽

Author(s):

Luis H. S. Vogado ◽

Rodrigo M. S. Veras ◽

Kelson R. T. Aires

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Cross Validation ◽

Data Augmentation ◽

Fine Tuning ◽

Current State ◽

Leukemia Diagnosis ◽

New Guidelines ◽

Texture Contrast ◽

Tuning Methods

Leukemia is a disorder that affects the bone marrow, causing uncontrolled production of leukocytes, impairing the transport of oxygen and causing blood coagulation problems. In this article, we propose a new computational tool, named LeukNet, a Convolutional Neural Network (CNN) architecture based on the VGG-16 convolutional blocks, to facilitate the leukemia diagnosis from blood smear images. We evaluated different architectures and fine-tuning methods using 18 datasets containing 3536 images with distinct characteristics of color, texture, contrast, and resolution. Additionally, data augmentation operations were applied to increase the training set by up to 20 times. The k-fold cross-validation (k = 5) results achieved 98.28% of accuracy. A cross-dataset validation technique, named LeaveOne-Dataset-Out Cross-Validation (LODOCV), is also proposed to evaluate the developed model’s generalization capability. The accuracy of using LODOCV on the ALL-IDB 1, ALL-IDB 2, and UFG datasets was 97.04%, 82.46%, and 70.24%, respectively, overcoming the current state-of-the-art results and offering new guidelines for image-based computer-aided diagnosis (CAD) systems in this area.

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Voiceprint Identification for Limited Dataset Using the Deep Migration Hybrid Model Based on Transfer Learning

Sensors ◽

10.3390/s18072399 ◽

2018 ◽

Vol 18 (7) ◽

pp. 2399 ◽

Cited By ~ 9

Author(s):

Cunwei Sun ◽

Yuxin Yang ◽

Chang Wen ◽

Kai Xie ◽

Fangqing Wen

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Hybrid Model ◽

Data Augmentation ◽

Small Sample ◽

Restricted Boltzmann Machine ◽

Small Samples ◽

Boltzmann Machine ◽

Training Time

The convolutional neural network (CNN) has made great strides in the area of voiceprint recognition; but it needs a huge number of data samples to train a deep neural network. In practice, it is too difficult to get a large number of training samples, and it cannot achieve a better convergence state due to the limited dataset. In order to solve this question, a new method using a deep migration hybrid model is put forward, which makes it easier to realize voiceprint recognition for small samples. Firstly, it uses Transfer Learning to transfer the trained network from the big sample voiceprint dataset to our limited voiceprint dataset for the further training. Fully-connected layers of a pre-training model are replaced by restricted Boltzmann machine layers. Secondly, the approach of Data Augmentation is adopted to increase the number of voiceprint datasets. Finally, we introduce fast batch normalization algorithms to improve the speed of the network convergence and shorten the training time. Our new voiceprint recognition approach uses the TLCNN-RBM (convolutional neural network mixed restricted Boltzmann machine based on transfer learning) model, which is the deep migration hybrid model that is used to achieve an average accuracy of over 97%, which is higher than that when using either CNN or the TL-CNN network (convolutional neural network based on transfer learning). Thus, an effective method for a small sample of voiceprint recognition has been provided.

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Tomato pest classification using deep convolutional neural network with transfer learning, fine tuning and scratch learning

Intelligent Decision Technologies ◽

10.3233/idt-200192 ◽

2021 ◽

pp. 1-10

Author(s):

Gayatri Pattnaik ◽

Vimal K. Shrivastava ◽

K. Parvathi

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Data Augmentation ◽

State Of The Art ◽

Deep Convolutional Neural Network ◽

Fine Tuning ◽

Tomato Plants ◽

Random Weights

Pests are major threat to economic growth of a country. Application of pesticide is the easiest way to control the pest infection. However, excessive utilization of pesticide is hazardous to environment. The recent advances in deep learning have paved the way for early detection and improved classification of pest in tomato plants which will benefit the farmers. This paper presents a comprehensive analysis of 11 state-of-the-art deep convolutional neural network (CNN) models with three configurations: transfers learning, fine-tuning and scratch learning. The training in transfer learning and fine tuning initiates from pre-trained weights whereas random weights are used in case of scratch learning. In addition, the concept of data augmentation has been explored to improve the performance. Our dataset consists of 859 tomato pest images from 10 categories. The results demonstrate that the highest classification accuracy of 94.87% has been achieved in the transfer learning approach by DenseNet201 model with data augmentation.

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Oversampling Based on Data Augmentation in Convolutional Neural Network for Silicon Wafer Defect Classification

Knowledge Innovation Through Intelligent Software Methodologies, Tools and Techniques - Frontiers in Artificial Intelligence and Applications ◽

10.3233/faia200547 ◽

2020 ◽

Author(s):

Uzma Batool ◽

Mohd Ibrahim Shapiai ◽

Nordinah Ismail ◽

Hilman Fauzi ◽

Syahrizal Salleh

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Silicon Wafer ◽

Data Augmentation ◽

Imbalanced Data ◽

Training Data ◽

Defect Classification ◽

Learning Method ◽

Test Set

Silicon wafer defect data collected from fabrication facilities is intrinsically imbalanced because of the variable frequencies of defect types. Frequently occurring types will have more influence on the classification predictions if a model gets trained on such skewed data. A fair classifier for such imbalanced data requires a mechanism to deal with type imbalance in order to avoid biased results. This study has proposed a convolutional neural network for wafer map defect classification, employing oversampling as an imbalance addressing technique. To have an equal participation of all classes in the classifier’s training, data augmentation has been employed, generating more samples in minor classes. The proposed deep learning method has been evaluated on a real wafer map defect dataset and its classification results on the test set returned a 97.91% accuracy. The results were compared with another deep learning based auto-encoder model demonstrating the proposed method, a potential approach for silicon wafer defect classification that needs to be investigated further for its robustness.

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