AK-DL: A Shallow Neural Network Model for Diagnosing Actinic Keratosis with Better Performance than Deep Neural Networks

Wang, Liyang; Chen, Angxuan; Zhang, Yan; Wang, Xiaoya; Xue, Yong

doi:10.3390/diagnostics10040217

Cited by 23 publications

(14 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Computer vision technology has an important role to play in various industries, and plant pest identification is no exception [ 25 , 26 ]. Bedi, P. and Gole, P. et al proposed a novel hybrid model of CAE and CNN for plant disease detection, which can be performed automatically.…”

Section: Related Workmentioning

confidence: 99%

High-Performance Plant Pest and Disease Detection Based on Model Ensemble with Inception Module and Cluster Algorithm

Cheng

Cui

et al. 2023

Plants

View full text Add to dashboard Cite

Protecting crop yields is the most important aspect of agricultural production, and one of the important measures in preserving yields is the control of crop pests and diseases; therefore, the identification of crop pests and diseases is of irreplaceable importance. In recent years, with the maturity of computer vision technology, more possibilities have been provided for implementing plant disease detection. However, although deep learning methods are widely used in various computer vision tasks, there are still limitations and obstacles in practical applications. Traditional deep learning-based algorithms have some drawbacks in this research area: (1) Recognition accuracy and computational speed cannot be combined. (2) Different pest and disease features interfere with each other and reduce the accuracy of pest and disease diagnosis. (3) Most of the existing researches focus on the recognition efficiency and ignore the inference efficiency, which limits the practical production application. In this study, an integrated model integrating single-stage and two-stage target detection networks is proposed. The single-stage network is based on the YOLO network, and its internal structure is optimized; the two-stage network is based on the Faster-RCNN, and the target frame size is first clustered using a clustering algorithm in the candidate frame generation stage to improve the detection of small targets. Afterwards, the two models are integrated to perform the inference task. For training, we use transfer learning to improve the model training speed. Finally, among the 37 pests and 8 diseases detected, this model achieves 85.2% mAP, which is much higher than other comparative models. After that, we optimize the model for the poor detection categories and verify the generalization performance on open source datasets. In addition, in order to quickly apply this method to real-world scenarios, we developed an application embedded in this model for the mobile platform and put the model into practical agricultural use.

show abstract

Section: Related Workmentioning

confidence: 99%

High-Performance Plant Pest and Disease Detection Based on Model Ensemble with Inception Module and Cluster Algorithm

Cheng

Cui

et al. 2023

Plants

View full text Add to dashboard Cite

show abstract

“…As can be seen in the original VGG19, it has FCLs followed by a SoftMax classifier that can classify 1000 different objects. Typically, the fully connected layer is utilized to further purify the features recovered by the convolutional layer, and so it plays a critical role in mapping the distributed feature to the sample space representation [ 74 ]. Here in the shallow architecture, to classify the features that were extracted via SCNN layers, a fully connected layer should be added.…”

Section: Proposed Frameworkmentioning

confidence: 99%

Feature selection of pre-trained shallow CNN using the QLESCA optimizer: COVID-19 detection as a case study

2023

View full text Add to dashboard Cite

“…Wang et al created an SCNN to detect actinic keratosis [ 43 ]. A proposed network consists of two convolutional layers, a max-pooling layer, and a fully-connected layer.…”

Section: Related Workmentioning

confidence: 99%

A Multi-Purpose Shallow Convolutional Neural Network for Chart Images

Bajić

Orel

Habijan

2022

Sensors

View full text Add to dashboard Cite

Charts are often used for the graphical representation of tabular data. Due to their vast expansion in various fields, it is necessary to develop computer algorithms that can easily retrieve and process information from chart images in a helpful way. Convolutional neural networks (CNNs) have succeeded in various image processing and classification tasks. Nevertheless, the success of training neural networks in terms of result accuracy and computational requirements requires careful construction of the network layers’ and networks’ parameters. We propose a novel Shallow Convolutional Neural Network (SCNN) architecture for chart-type classification and image generation. We validate the proposed novel network by using it in three different models. The first use case is a traditional SCNN classifier where the model achieves average classification accuracy of 97.14%. The second use case consists of two previously introduced SCNN-based models in parallel, with the same configuration, shared weights, and parameters mirrored and updated in both models. The model achieves average classification accuracy of 100%. The third proposed use case consists of two distinct models, a generator and a discriminator, which are both trained simultaneously using an adversarial process. The generated chart images are plausible to the originals. Extensive experimental analysis end evaluation is provided for the classification task of seven chart classes. The results show that the proposed SCNN is a powerful tool for chart image classification and generation, comparable with Deep Convolutional Neural Networks (DCNNs) but with higher efficiency, reduced computational time, and space complexity.

show abstract

AK-DL: A Shallow Neural Network Model for Diagnosing Actinic Keratosis with Better Performance than Deep Neural Networks

Cited by 23 publications

References 37 publications

High-Performance Plant Pest and Disease Detection Based on Model Ensemble with Inception Module and Cluster Algorithm

High-Performance Plant Pest and Disease Detection Based on Model Ensemble with Inception Module and Cluster Algorithm

Feature selection of pre-trained shallow CNN using the QLESCA optimizer: COVID-19 detection as a case study

A Multi-Purpose Shallow Convolutional Neural Network for Chart Images

Contact Info

Product

Resources

About