Medical image encryption and compression by adaptive sigma filterized synorr certificateless signcryptive Levenshtein entropy-coding-based deep neural learning

Selvi, C. Thirumarai; Amudha, J.; Sudhakar, R.

doi:10.1007/s00530-021-00764-y

Cited by 21 publications

(19 citation statements)

References 14 publications

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“…Suhail and Sankar 109 presented an application of image compression and encryption using an autoencoder and chaotic logistic map. Selvi et al 110 suggested a competent adaptive sigma filterized synorr certificateless signcryptive Levenshtein entropy coding-based deep neural learning technique trained on a dataset of chest x-ray images to develop the image encryption and compression (Table 9). 125 Train a neural architecture to learn the mapping algorithm between the key and the physical unclonable function…”

Section: Image Compression In Image Encryption Algorithmsmentioning

confidence: 99%

Survey on deep learning applications in digital image security

Bao

Ren

2021

Opt. Eng.

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In the digital era, sharing pictures on social media has become a common privacy issue. To prevent private images from being eavesdropped on and destroyed, developing secure and efficient image steganography, image cryptography, and image authentication has been difficult. Deep learning provides a solution for digital image security. First, we make an overall conclusion on deep learning applications in image steganography to generate five aspects: the cover image, stego-image, embedding change probabilities, coverless steganography, and steganalysis. Second, we also combine and compare deep learning methods used in six aspects: image cryptography from image compression, image resolution improvement, image object detection and classification, key generation, end-to-end image encryption, and image cryptoanalysis. Third, we collect deep learning methods in image authentication from five perspectives: image forgery detection, watermarked image generation, image watermark extraction and detection, image watermarking attack, and image watermark removal. Finally, we summarize future research directions of deep learning utilization in image steganography, image cryptography, and image authentication.

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Section: Image Compression In Image Encryption Algorithmsmentioning

confidence: 99%

Survey on deep learning applications in digital image security

Bao

Ren

2021

Opt. Eng.

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“…The benign lesions are represented by a round and smooth structure around the mass. 9 The calcification area appears as granular, popcorn, or a ring-like structure, whereas the density of the calcification region is higher with random distribution. 10 The malignant lesions appear as needle-shaped masses with irregular edges.…”

Section: Introductionmentioning

confidence: 97%

“…The two main representations of mammogram images are masses and calcifications. The benign lesions are represented by a round and smooth structure around the mass 9 . The calcification area appears as granular, popcorn, or a ring‐like structure, whereas the density of the calcification region is higher with random distribution 10 .…”

Section: Introductionmentioning

confidence: 99%

Categorization of breast masses based on deep belief network parameters optimized using chaotic krill herd optimization algorithm for frequent diagnosis of breast abnormalities

Chandraraju¹,

Jeyaprakash²

2022

Int J Imaging Syst Tech

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Several deep learning techniques are utilized for classification of the masses in mammogram images. But the existing method doesnot provide sufficient accuracy . for breast masses classification in mammogram images. In this manuscript, an efficient breast cancer image classification framework utilizing the deep belief network (DBN) through chaotic krill herd optimization (CKHO) algorithm for classification of masses in mammogram images is proposed. Initially the input mammogram images are pre‐processed by altered phase preserving dynamic range compression (APPDRC) method for removing the unwanted noise and artifacts. Then these pre‐processed images are assumed with DBN for classifying that mass in the mammogram images into normal, benign, and malignant lesions. Generally, DBN does not reveal any acceptance of optimization techniques for computing optimal parameters that guarantee an accurate classification. Therefore in this work, proposed CKHO algorithm is employed for optimizing weight parameters of the self‐attention convolutional neural network (SACNN). The simulation process is performed under MATLAB platform. Finally, the proposed DBN with chaotic krill herd optimization algorithm (DBN‐CKHO) attains high accuracy 44.5%, 33.42%, 55.23%, 62.35%, and 43.42% when compared with the existing method such as extreme learning machine classifier with moth flame optimization (ELMC‐MFO), Kernel extreme learning machine classifier with Chaotic salp swarm algorithm (KELMC‐CSSA), Extreme learning machine classifier with fruit fly optimization algorithm (ELMC‐FOA), Kernel extreme learning machine classifier with grasshopper optimization algorithm (KELMC‐GOA) and deep neural network with non‐dominated sorting genetic algorithm‐based classification approach.

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“…With the advent of the medical and health big data era, medical image data show an "explosive" growth [1]. As a result, the storage and transmission of a large number of medical images become more difcult.…”

Section: Introductionmentioning

confidence: 99%

“…Although the images reconstructed by lossless compression technology have no distortion and have a good reconstruction efect, the compression rate is relatively low and lossless compression technology cannot meet the requirements of the compression of the huge amount of medical images currently [7]. Te process of lossy compression technology is that the medical image data are compressed by mapping, quantization, coding, and entropy coding [1,8,9]. Quantization error caused by quantization coding is the main reason for rate distortion in the restoration process.…”

Section: Introductionmentioning

confidence: 99%

Medical Image Compression Based on Variational Autoencoder

Liu

Zhang

Guo

et al. 2022

Mathematical Problems in Engineering

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With the rapid growth of medical image data, it has become a current research hotspot that how to realize the large amounts of the real-time upload and storage of medical images with limited network bandwidth and storage space. However, currently, medical image compression technology cannot perform joint optimization of rate (the degree of compression) and distortion (reconstruction effect). Therefore, this study proposed a medical image compression algorithm based on a variational autoencoder. This algorithm takes rate and distortion as the common optimization goal and uses the residual network module to directly transmit information, which alleviates the contradiction between improving the degree of compression and optimizing the reconstruction effect. At the same time, the algorithm also reduces image loss in the medical image compression process by adding the residual network. The experimental results show that, compared with the traditional medical image compression algorithm and the deep learning compression algorithm, the algorithm in this study has smaller distortion, better reconstruction effect, and can obtain higher quality medical images at the same compression rate.

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Medical image encryption and compression by adaptive sigma filterized synorr certificateless signcryptive Levenshtein entropy-coding-based deep neural learning

Cited by 21 publications

References 14 publications

Survey on deep learning applications in digital image security

Survey on deep learning applications in digital image security

Categorization of breast masses based on deep belief network parameters optimized using chaotic krill herd optimization algorithm for frequent diagnosis of breast abnormalities

Medical Image Compression Based on Variational Autoencoder

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