Improving the security of medical image through neuro-fuzzy based ROI selection for reliable transmission

Balasamy, K.; Krishnaraj, N.; Vijayalakshmi, K.

doi:10.1007/s11042-022-12367-4

Cited by 20 publications

(2 citation statements)

References 44 publications

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“…However, the challenges may arise from relying on singular values for watermark robustness and using logistic mapping for sub-band swapping, posing susceptibility to specific attacks. More detailed explanation of ROI based techniques to secure digital images can be found in [51][52][53] . In 54 , Chai et al proposed a DNA-based method, involving the conversion of a plaintext image into a DNA matrix.…”

Section: Fibonacci Dwt and Dna Based Image Encryption Schemesmentioning

confidence: 99%

Enhancing image security via chaotic maps, Fibonacci, Tribonacci transformations, and DWT diffusion: a robust data encryption approach

Hazzazi,

Rehman,

Shafique

et al. 2024

Sci Rep

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In recent years, numerous image encryption schemes have been developed that demonstrate different levels of effectiveness in terms of robust security and real-time applications. While a few of them outperform in terms of robust security, others perform well for real-time applications where less processing time is required. Balancing these two aspects poses a challenge, aiming to achieve efficient encryption without compromising security. To address this challenge, the proposed research presents a robust data security approach for encrypting grayscale images, comprising five key phases. The first and second phases of the proposed encryption framework are dedicated to the generation of secret keys and the confusion stage, respectively. While the level-1, level-2, and level-2 diffusions are performed in phases 3, 4, and 5, respectively, The proposed approach begins with secret key generation using chaotic maps for the initial pixel scrambling in the plaintext image, followed by employing the Fibonacci Transformation (FT) for an additional layer of pixel shuffling. To enhance security, Tribonacci Transformation (TT) creates level-1 diffusion in the permuted image. Level-2 diffusion is introduced to further strengthen the diffusion within the plaintext image, which is achieved by decomposing the diffused image into eight-bit planes and implementing XOR operations with corresponding bit planes that are extracted from the key image. After that, the discrete wavelet transform (DWT) is employed to develop secondary keys. The DWT frequency sub-band (high-frequency sub-band) is substituted using the substitution box process. This creates further diffusion (level 3 diffusion) to make it difficult for an attacker to recover the plaintext image from an encrypted image. Several statistical tests, including mean square error analysis, histogram variance analysis, entropy assessment, peak signal-to-noise ratio evaluation, correlation analysis, key space evaluation, and key sensitivity analysis, demonstrate the effectiveness of the proposed work. The proposed encryption framework achieves significant statistical values, with entropy, correlation, energy, and histogram variance values standing at 7.999, 0.0001, 0.0156, and 6458, respectively. These results contribute to its robustness against cyberattacks. Moreover, the processing time of the proposed encryption framework is less than one second, which makes it more suitable for real-world applications. A detailed comparative analysis with the existing methods based on chaos, DWT, Tribonacci transformation (TT), and Fibonacci transformation (FT) reveals that the proposed encryption scheme outperforms the existing ones.

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Section: Fibonacci Dwt and Dna Based Image Encryption Schemesmentioning

confidence: 99%

Enhancing image security via chaotic maps, Fibonacci, Tribonacci transformations, and DWT diffusion: a robust data encryption approach

Hazzazi,

Rehman,

Shafique

et al. 2024

Sci Rep

View full text Add to dashboard Cite

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“…The ultimate result of a typical CNN network structure is often one or more fully-connected layers [17,18,19], however the network itself is typically composed of convolutional and pooling layers. The final output of the layer is obtained by applying a bias to fully connected layer and activation function.…”

Section: Proposed Modelmentioning

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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