A Genetic Algorithm Based Medical Image Watermarking for Improving Robustness and Fidelity in Wavelet Domain

Balasamy, K.; Balakrishnan, Muppanat Raman; Christopher, A. Gilligan

doi:10.1007/978-981-15-5679-1_27

Cited by 8 publications

(1 citation statement)

References 11 publications

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“…The 2015-ILSVRC competition was won by a CNN with 152 layers. In addition, ResNet improves by 28% on the well-known cOco132 example dataset for image recognition [22,23]. For the most part, ResNet takes use of the concept of bypass channels in the "road network," as seen in the mathematical Eq.…”

Section: Deep Residual Networkmentioning

confidence: 99%

Deep Learning and Classification Algorithms for COVID-19 Detection

Sidheeque¹,

Sumathy²,

M³

2022

IJACSA

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The imaging modalities of chest X-rays and computed tomography (CT) are commonly utilized to quickly and accurately diagnose COVID-19. Due to time and human error, it is exceedingly difficult to manually identify the infection using radio imaging. COVID-19 identification is being mechanized and improved with the use of artificial intelligence (AI) tools that have already showed promise. This study employs the following methodology: The chest footage was pre-processed by setting equalizing the histogram, sharpening it, and so on. The transformed chest images are then retrieved through shallow and high-level feature mapping over the backbone network. To further improve the classification performance of the convolutional neural network, the model uses self-attained mechanism through feature maps. Numerous simulations show that CT image classification and augmentation may be accomplished with higher efficiency and flexibility using the Inception-Resnet convolutional neural network than with traditional segmentation methods. The experiment illustrates the association between model accuracy, model loss, and epoch. Inception-statistical Resnet's measurement results are 98%, 91%, 91%.

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Section: Deep Residual Networkmentioning

confidence: 99%