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Image Search Results


Performance of the developed COVID-19 detection models on the unseen dataset.

Journal: Computers in Biology and Medicine

Article Title: COVID-19 detection in chest X-ray images using deep boosted hybrid learning

doi: 10.1016/j.compbiomed.2021.104816

Figure Lengend Snippet: Performance of the developed COVID-19 detection models on the unseen dataset.

Article Snippet: To identify the significance of exploitation of deep feature engineering, for comparison purposes, we have used a Softmax classifier-based implementation of existing CNN models as well.

Techniques:

Performance comparison of hybrid based DHL and Softmax classifier-based implementation of well-established CNN models.

Journal: Computers in Biology and Medicine

Article Title: COVID-19 detection in chest X-ray images using deep boosted hybrid learning

doi: 10.1016/j.compbiomed.2021.104816

Figure Lengend Snippet: Performance comparison of hybrid based DHL and Softmax classifier-based implementation of well-established CNN models.

Article Snippet: To identify the significance of exploitation of deep feature engineering, for comparison purposes, we have used a Softmax classifier-based implementation of existing CNN models as well.

Techniques: Comparison

ROC curve for the proposed frameworks (DHL, DBHL), the developed and well-established CNN Models. The square bracket values represent the tolerance or error, calculated at a 95% confidence interval .

Journal: Computers in Biology and Medicine

Article Title: COVID-19 detection in chest X-ray images using deep boosted hybrid learning

doi: 10.1016/j.compbiomed.2021.104816

Figure Lengend Snippet: ROC curve for the proposed frameworks (DHL, DBHL), the developed and well-established CNN Models. The square bracket values represent the tolerance or error, calculated at a 95% confidence interval .

Article Snippet: To identify the significance of exploitation of deep feature engineering, for comparison purposes, we have used a Softmax classifier-based implementation of existing CNN models as well.

Techniques:

Convolutional neural network (CNN) architectures used for comparisons in our research.

Journal: Sensors (Basel, Switzerland)

Article Title: Deep Learning-Based Multinational Banknote Type and Fitness Classification with the Combined Images by Visible-Light Reflection and Infrared-Light Transmission Image Sensors

doi: 10.3390/s19040792

Figure Lengend Snippet: Convolutional neural network (CNN) architectures used for comparisons in our research.

Article Snippet: Our training and testing experiments were conducted by using the MATLAB implementation of CNN [ ] on a desktop computer with the following configuration: Intel® CoreTM i7-3770K CPU @ 3.50 GHz [ ], 16 GB DDR3 memory, and NVIDIA GeForce GTX 1070 graphics card (1920 CUDA cores, 8 GB GDDR5 memory) [ ].

Techniques: