A biological image classification method based on improved CNN

Qin, Jiaohua; Pan, Wenyan; Xiang, Xuyu; Tan, Yun; Hou, Guimin

doi:10.1016/j.ecoinf.2020.101093

Cited by 111 publications

(64 citation statements)

References 15 publications

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“…Since 2012, when Krizhevskys' Neural Network (NN) won the ImageNet competition [37], AIbased solutions have become common for image identification tasks [38]. Furthermore, Machine Learning (ML) and Deep Learning (DL) techniques are being applied for object detection [39], image classification [40] and sound recognition [41].…”

Section: Identification Algorithmsmentioning

confidence: 99%

Comprehensive Bird Preservation at Wind Farms

Gradolewski

Dziak

Martynow³

et al. 2021

Sensors

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Wind as a clean and renewable energy source has been used by humans for centuries. However, in recent years with the increase in the number and size of wind turbines, their impact on avifauna has become worrisome. Researchers estimated that in the U.S. up to 500,000 birds die annually due to collisions with wind turbines. This article proposes a system for mitigating bird mortality around wind farms. The solution is based on a stereo-vision system embedded in distributed computing and IoT paradigms. After a bird’s detection in a defined zone, the decision-making system activates a collision avoidance routine composed of light and sound deterrents and the turbine stopping procedure. The development process applies a User-Driven Design approach along with the process of component selection and heuristic adjustment. This proposal includes a bird detection method and localization procedure. The bird identification is carried out using artificial intelligence algorithms. Validation tests with a fixed-wing drone and verifying observations by ornithologists proved the system’s desired reliability of detecting a bird with wingspan over 1.5 m from at least 300 m. Moreover, the suitability of the system to classify the size of the detected bird into one of three wingspan categories, small, medium and large, was confirmed.

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Section: Identification Algorithmsmentioning

confidence: 99%

Comprehensive Bird Preservation at Wind Farms

Gradolewski

Dziak

Martynow³

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…The application field of convolutional neural network is quite extensive, such as image recognition [18][19][20], image classification [21][22][23][24], target tracking [25][26][27][28], text analysis [29][30][31][32], target detection, and image retrieval [33,34]. It is a powerful tool for image processing and research.…”

Section: Semantic Segmentation Based On Convolutional Neural Networkmentioning

confidence: 99%

A video coverless information hiding algorithm based on semantic segmentation

Pan

Qin

Tan

et al. 2020

J Image Video Proc.

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Due to the fact that coverless information hiding can effectively resist the detection of steganalysis tools, it has attracted more attention in the field of information hiding. At present, most coverless information hiding schemes select text and image as transmission carriers, while there are few studies on emerging popular media such as video, which has more abundant contents. Taking the natural video as the carrier is more secure and can avoid the attention of attackers. In this paper, we propose a coverless video steganography algorithm based on semantic segmentation. Specifically, to establish the mapping relationship between secret information and video files effectively, this paper introduces the deep learning based on semantic segmentation network to calculate the statistical histogram of semantic information. To quickly index the sender's secret message to the corresponding video frame, we build a three-digit index structure. The receiver can extract the valid video frame from the three-digit index information and restore the secret information. On the one hand, the neural network is trained through the original image and the noisy image in this scheme; therefore, it can not only effectively resist the interference of noises, but also accurately extract the robust deep features of the image. The frames of video generate the robust mapping to the secret information after the semantic information statistics. On the other hand, semantic segmentation belongs to pixel-level segmentation, which has high requirements for network parameters, so it is difficult for attackers to decrypt and recover secret information. Since this scheme does not modify the primitiveness of video data, it can effectively resist steganalysis tools. The experimental results and analysis show that the video coverless information hiding scheme has a large capacity and a certain resistance to noise attack.

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“…This helps CNN to settle based on all layers instead of one nal layer. CNN's are more sophisticated and can capture image information on a larger scale compared to traditional image processing methods [9].…”

Section: Introductionmentioning

confidence: 99%

DenseNet Convolutional Neural Networks Application for Predicting COVID-19 Using CT Image

Hasan

Bao

Shawon

2020

Preprint

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Public health and human lives recently have been impacted by the devastating effect of Coronavirus 2019. This catastrophic effect has destroyed the human experience by creating a chaotic healthcare situation infinitely more destructive than the Second World War. Strong communicable characteristics of COVID-19 among human communities make the world’s situation a severe pandemic. Due to the unavailable vaccine of COVID-19 to control rather than cure, early and accurate detection of the virus can be a promising technique for tracking and preventing the infection spreading (e.g., by isolating the patients). This situation indicates to improve auxiliary COVID-19 detection technique. Computed tomography (CT) imaging is a mostly used technique for pneumonia because of its common availability. The application of artificial intelligence systems integrated with images can be a promising alternative for the identification of COVID-19. This paper presents a promising technique of predicting COVID-19 patients from the CT image using convolutional neural networks (CNN). The novel approach is based on DenseNet is the updated CNN architecture in the present state to detect COVID-19. The results outperformed 92% accuracy, with 95% recall showing good performance for the identification of COVID-19.

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A biological image classification method based on improved CNN

Cited by 111 publications

References 15 publications

Comprehensive Bird Preservation at Wind Farms

Comprehensive Bird Preservation at Wind Farms

A video coverless information hiding algorithm based on semantic segmentation

DenseNet Convolutional Neural Networks Application for Predicting COVID-19 Using CT Image

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