An Exploratory of Hybrid Techniques on Deep Learning for Image Classification

Suganthi, M.; Sathiaseelan, J. G. R.

doi:10.1109/icccsp49186.2020.9315270

Cited by 11 publications

(11 citation statements)

References 4 publications

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“…Although highly reliable, standalone DL algorithms have limited efficiencies which may be compensated for by integrating other DL algorithms to form a hybrid model for improved learning and predictive efficiencies [23,24]. For instance, beyond the general retraining issues inherent in closed set classification systems, CNNs are strongly affected by inputs with dynamic transient behaviour while DNNs are easily fooled by inputs due to their high dependence on a priori knowledge [25].…”

Section: Related Workmentioning

confidence: 99%

D-dCNN: A Novel Hybrid Deep Learning-Based Tool for Vibration-Based Diagnostics

Akpudo

Hur

2021

Energies

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This paper develops a novel hybrid feature learner and classifier for vibration-based fault detection and isolation (FDI) of industrial apartments. The trained model extracts high-level discriminative features from vibration signals and predicts equipment state. Against the limitations of traditional machine learning (ML)-based classifiers, the convolutional neural network (CNN) and deep neural network (DNN) are not only superior for real-time applications, but they also come with other benefits including ease-of-use, automated feature learning, and higher predictive accuracies. This study proposes a hybrid DNN and one-dimensional CNN diagnostics model (D-dCNN) which automatically extracts high-level discriminative features from vibration signals for FDI. Via Softmax averaging at the output layer, the model mitigates the limitations of the standalone classifiers. A diagnostic case study demonstrates the efficiency of the model with a significant accuracy of 92% (F1 score) and extensive comparative empirical validations.

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Section: Related Workmentioning

confidence: 99%

D-dCNN: A Novel Hybrid Deep Learning-Based Tool for Vibration-Based Diagnostics

Akpudo

Hur

2021

Energies

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“…This paper used parallel atrous convolutions to realize the variant of the ASPP module that used multiple sampling rates to extract features and then merged them. The four parallel sampling rates of the ASPP module are large, respectively [6,12,18,24]. However, the information extracted by atrous convolutions with large sampling rates only was conducive to the segmentation of some large objects, but not small objects.…”

Section: Figure 5: Examples Of the Dataset Imagesmentioning

confidence: 99%

“…Third, different shapes of lesions have irregularly connected blocks. In response to these problems, this paper studies the segmentation and recognition of the Zanthoxylum rust area based on deep learning [24][25][26][27][28][29]. Our main contributions are as follows: 1) we construct a fine-grained Zanthoxylum rust image dataset.…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Image Segmentation Model for Fine-Grained Recognition of Zanthoxylum Rust

Yang¹,

Xu²,

Wei³

et al. 2022

Computers, Materials &Amp; Continua

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Zanthoxylum bungeanum Maxim, generally called prickly ash, is widely grown in China. Zanthoxylum rust is the main disease affecting the growth and quality of Zanthoxylum. Traditional method for recognizing the degree of infection of Zanthoxylum rust mainly rely on manual experience. Due to the complex colors and shapes of rust areas, the accuracy of manual recognition is low and difficult to be quantified. In recent years, the application of artificial intelligence technology in the agricultural field has gradually increased. In this paper, based on the DeepLabV2 model, we proposed a Zanthoxylum rust image segmentation model based on the FASPP module and enhanced features of rust areas. This paper constructed a fine-grained Zanthoxylum rust image dataset. In this dataset, the Zanthoxylum rust image was segmented and labeled according to leaves, spore piles, and brown lesions. The experimental results showed that the Zanthoxylum rust image segmentation method proposed in this paper was effective. The segmentation accuracy rates of leaves, spore piles and brown lesions reached 99.66%, 85.16% and 82.47% respectively. MPA reached 91.80%, and MIoU reached 84.99%. At the same time, the proposed image segmentation model also had good efficiency, which can process 22 images per minute. This article provides an intelligent method for efficiently and accurately recognizing the degree of infection of Zanthoxylum rust.

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“…Many researchers have demonstrated deep learning efficiency as a feature extraction method in recent years (Kraus, Grys, Ba, Chong, Frey, Boone and Andrews, 2017). Moreover, many works in different tasks (Govindaswamy, Montague, Raicu and Furst, 2020), (Wang, Zhang and Hao, 2019), (Basly, Ouarda, Sayadi, Ouni and Alimi, 2020), (Alzubaidi, Fadhel, Al-Shamma, Zhang and Duan, 2020), (Aurelia, Rustam, Wibowo and Setiawan, 2020), (Mu and Qiao, 2019), (Suganthi and Sathiaseelan, 2020), (Oltu, Güney, Dengiz and Ağıldere, 2021), (Bodapati and Veeranjaneyulu, 2019), (Karungaru, Dongyang and Terada, 2021), (Öznur Özaltın and Özgür Yeniay, 2021) show the effectiveness of using ML classifier to classify the data based on features extracted through deep CNN compared to end-to-end deep learning.…”

Section: Introductionmentioning

confidence: 99%

“…In (Suganthi and Sathiaseelan, 2020), the authors proposed a comparative study for the efficiency for classifying the image with different aspects of machine learning and CNN. They concluded that researches from different fields consider the combination of CNN model with different ML is more efficient than the normal CNN.…”

Section: Introductionmentioning

confidence: 99%

Multi-features extraction based on deep learning for skin lesion classification

2022

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For various forms of skin lesion, many different feature extraction methods have been investigated so far. Indeed, feature extraction is a crucial step in machine learning processes. In general, we can distinct handcrafted and deep learning features. In this paper, we investigate the efficiency of using 17 commonly pre-trained convolutional neural networks (CNN) architectures as feature extractors and of 24 machine learning classifiers to evaluate the classification of skin lesions from two different datasets: ISIC 2019 and PH2. In this research, we find out that a DenseNet201 combined with Fine KNN or Cubic SVM achieved the best results in accuracy (92.34% and 91.71% ) for the ISIC 2019 dataset. The results also show that the suggested method outperforms others approaches with an accuracy of 99% on the PH2 dataset.

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An Exploratory of Hybrid Techniques on Deep Learning for Image Classification

Cited by 11 publications

References 4 publications

D-dCNN: A Novel Hybrid Deep Learning-Based Tool for Vibration-Based Diagnostics

D-dCNN: A Novel Hybrid Deep Learning-Based Tool for Vibration-Based Diagnostics

Multi-Scale Image Segmentation Model for Fine-Grained Recognition of Zanthoxylum Rust

Multi-features extraction based on deep learning for skin lesion classification

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