scholarly journals An Accurate CT Saturation Classification Using a Deep Learning Approach Based on Unsupervised Feature Extraction and Supervised Fine-Tuning Strategy

Energies ◽  
2017 ◽  
Vol 10 (11) ◽  
pp. 1830 ◽  
Author(s):  
Muhammad Ali ◽  
Dae-Hee Son ◽  
Sang-Hee Kang ◽  
Soon-Ryul Nam
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Fine Tuning ◽  
Learning Approach ◽  
Tuning Strategy ◽  
Unsupervised Feature Extraction

scholarly journals Underwater Target Recognition Based on Multi-Decision LOFAR Spectrum Enhancement: A Deep-Learning Approach

2021 ◽  
Vol 13 (10) ◽  
pp. 265
Author(s):  
Jie Chen ◽  
Bing Han ◽  
Xufeng Ma ◽  
Jian Zhang
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Recognition Accuracy ◽  
Target Recognition ◽  
Signal To Noise Ratio ◽  
Recognition Performance ◽  
Low Frequency ◽  
Learning Approach ◽  
Decision Algorithm ◽  
Underwater Target

Underwater target recognition is an important supporting technology for the development of marine resources, which is mainly limited by the purity of feature extraction and the universality of recognition schemes. The low-frequency analysis and recording (LOFAR) spectrum is one of the key features of the underwater target, which can be used for feature extraction. However, the complex underwater environment noise and the extremely low signal-to-noise ratio of the target signal lead to breakpoints in the LOFAR spectrum, which seriously hinders the underwater target recognition. To overcome this issue and to further improve the recognition performance, we adopted a deep-learning approach for underwater target recognition, and a novel LOFAR spectrum enhancement (LSE)-based underwater target-recognition scheme was proposed, which consists of preprocessing, offline training, and online testing. In preprocessing, we specifically design a LOFAR spectrum enhancement based on multi-step decision algorithm to recover the breakpoints in LOFAR spectrum. In offline training, the enhanced LOFAR spectrum is adopted as the input of convolutional neural network (CNN) and a LOFAR-based CNN (LOFAR-CNN) for online recognition is developed. Taking advantage of the powerful capability of CNN in feature extraction, the recognition accuracy can be further improved by the proposed LOFAR-CNN. Finally, extensive simulation results demonstrate that the LOFAR-CNN network can achieve a recognition accuracy of 95.22%, which outperforms the state-of-the-art methods.

scholarly journals Classification of Remote Sensing Image Scenes Using Double Feature Extraction Hybrid Deep Learning Approach

2021 ◽  
Vol 3 (2) ◽  
pp. 133-149
Author(s):  
Akey Sungheetha ◽  
Rajesh Sharma R
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Deep Learning ◽  
Visual Recognition ◽  
Research Work ◽  
Recognition Rate ◽  
Remote Sensing Image ◽  
Quality Data ◽  
Learning Approach ◽  
Remote Sensing Images

Over the last decade, remote sensing technology has advanced dramatically, resulting in significant improvements on image quality, data volume, and application usage. These images have essential applications since they can help with quick and easy interpretation. Many standard detection algorithms fail to accurately categorize a scene from a remote sensing image recorded from the earth. A method that uses bilinear convolution neural networks to produce a lessweighted set of models those results in better visual recognition in remote sensing images using fine-grained techniques. This proposed hybrid method is utilized to extract scene feature information in two times from remote sensing images for improved recognition. In layman's terms, these features are defined as raw, and only have a single defined frame, so they will allow basic recognition from remote sensing images. This research work has proposed a double feature extraction hybrid deep learning approach to classify remotely sensed image scenes based on feature abstraction techniques. Also, the proposed algorithm is applied to feature values in order to convert them to feature vectors that have pure black and white values after many product operations. The next stage is pooling and normalization, which occurs after the CNN feature extraction process has changed. This research work has developed a novel hybrid framework method that has a better level of accuracy and recognition rate than any prior model.

Automatic feature extraction in large fusion databases by using deep learning approach

2016 ◽  
Vol 112 ◽  
pp. 979-983 ◽  
Author(s):  
Gonzalo Farias ◽  
Sebastián Dormido-Canto ◽  
Jesús Vega ◽  
Giuseppe Rattá ◽  
Héctor Vargas ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Learning Approach ◽  
Automatic Feature Extraction

scholarly journals Kudo’s Classification for Colon Polyps Assessment Using a Deep Learning Approach

2020 ◽  
Vol 10 (2) ◽  
pp. 501 ◽  
Author(s):  
Sebastian Patino-Barrientos ◽  
Daniel Sierra-Sosa ◽  
Begonya Garcia-Zapirain ◽  
Cristian Castillo-Olea ◽  
Adel Elmaghraby
Keyword(s):  
Deep Learning ◽  
Fine Tuning ◽  
Learning Approach ◽  
Colon Polyps ◽  
Cancer Death ◽  
Timely Manner ◽  
The World ◽  
Feature Extractor ◽  
The University ◽  
Deep Learning Model

Colorectal cancer (CRC) is the second leading cause of cancer death in the world. This disease could begin as a non-cancerous polyp in the colon, when not treated in a timely manner, these polyps could induce cancer, and in turn, death. We propose a deep learning model for classifying colon polyps based on the Kudo’s classification schema, using basic colonoscopy equipment. We train a deep convolutional model with a private dataset from the University of Deusto with and without using a VGG model as a feature extractor, and compared the results. We obtained 83% of accuracy and 83% of F1-score after fine tuning our model with the VGG filter. These results show that deep learning algorithms are useful to develop computer-aided tools for early CRC detection, and suggest combining it with a polyp segmentation model for its use by specialists.

scholarly journals Prediction of Recombination Spots Using Novel Hybrid Feature Extraction Method via Deep Learning Approach

2020 ◽  
Vol 11 ◽  
Author(s):  
Fatima Khan ◽  
Mukhtaj Khan ◽  
Nadeem Iqbal ◽  
Salman Khan ◽  
Dost Muhammad Khan ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Extraction Method ◽  
Learning Approach ◽  
Feature Extraction Method ◽  
Hybrid Feature Extraction
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