NIMEQ-SACNet: A novel self-attention precision medicine model for vision-threatening diabetic retinopathy using image data

Bilal, Anas; Liu, Xiaowen; Shafiq, Muhammad; Ahmed, Zohaib; Long, Haixia

doi:10.1016/j.compbiomed.2024.108099

Cited by 28 publications

(1 citation statement)

References 64 publications

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“…Our study builds upon foundational work in automated disease classification and prediction, leveraging advancements in optimization algorithms, deep learning models, and decision support systems as detailed in recent literature [ 36 – 38 ]. It showcases the efficiency of DL in capturing significant information that may be missed by human techniques [ 39 ].…”

Section: Literature Reviewmentioning

confidence: 99%

A novel fusion of genetic grey wolf optimization and kernel extreme learning machines for precise diabetic eye disease classification

Khan,

Sun,

Khalid

et al. 2024

PLoS ONE

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In response to the growing number of diabetes cases worldwide, Our study addresses the escalating issue of diabetic eye disease (DED), a significant contributor to vision loss globally, through a pioneering approach. We propose a novel integration of a Genetic Grey Wolf Optimization (G-GWO) algorithm with a Fully Convolutional Encoder-Decoder Network (FCEDN), further enhanced by a Kernel Extreme Learning Machine (KELM) for refined image segmentation and disease classification. This innovative combination leverages the genetic algorithm and grey wolf optimization to boost the FCEDN’s efficiency, enabling precise detection of DED stages and differentiation among disease types. Tested across diverse datasets, including IDRiD, DR-HAGIS, and ODIR, our model showcased superior performance, achieving classification accuracies between 98.5% to 98.8%, surpassing existing methods. This advancement sets a new standard in DED detection and offers significant potential for automating fundus image analysis, reducing reliance on manual examination, and improving patient care efficiency. Our findings are crucial to enhancing diagnostic accuracy and patient outcomes in DED management.

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Section: Literature Reviewmentioning

confidence: 99%

A novel fusion of genetic grey wolf optimization and kernel extreme learning machines for precise diabetic eye disease classification

Khan,

Sun,

Khalid

et al. 2024

PLoS ONE

Self Cite

View full text Add to dashboard Cite

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A robust approach to satellite image encryption using chaotic map and circulant matrices

Kanwal,

Inam,

Shah

et al. 2024

Engineering Reports

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In the modern era, where satellite imagery is vital for applications like ecological monitoring and national security, ensuring the safety and integrity of these data repositories is crucial. This study presents an improved satellite image encryption technique that combines the cryptographic strength of the circulant matrix in the Hill cipher with the dynamic characteristics of the hyperbolic tangent tent map, further enhanced by the Kronecker XOR product. The algorithm initiates with computing alterations by a shift amount. After preserving the leftmost pixel in each row, it executes XOR operations between alternating rows, combining the value of the current even or odd row with corresponding pixels in the adjacent rows followed by encryption using the Hill cipher. The resulting image undergoes a diffusion process utilizing a hyperbolic tangent tent map. The Kronecker XOR product operation is then applied to individual pixels to produce a secure image followed by additional diffusion with keys from the hyperbolic tangent tent map to achieve the final encrypted image. We conducted simulations using MATLAB to assess the efficiency of the proposed satellite image encryption from theoretical and statistical perspectives. The results exhibit robust encryption performance as demonstrated by metrics such as an entropy value of 7.9982, a UACI of 33.5333%, and an NPCR of 99.6038%. The experiment results demonstrate the proposed image encryption scheme's reliability, practicability, and efficiency in securing satellite images during data storage and transmission. Comprehensive testing against various attacks including correlation, histogram, chi‐square, NPCR, PSNR, UACI, SSIM, key space and key sensitivity analysis confirms the scheme's robustness, efficiency and speed. These findings verify the scheme's ability to come across the most stringent encryption and decryption standards, making it an effective solution for securing sensitive satellite image data.

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Enhancing plant disease detection: a novel CNN-based approach with tensor subspace learning and HOWSVD-MDA

Ouamane,

Chouchane,

Himeur

et al. 2024

Neural Comput & Applic

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NIMEQ-SACNet: A novel self-attention precision medicine model for vision-threatening diabetic retinopathy using image data

Cited by 28 publications

References 64 publications

A novel fusion of genetic grey wolf optimization and kernel extreme learning machines for precise diabetic eye disease classification

A novel fusion of genetic grey wolf optimization and kernel extreme learning machines for precise diabetic eye disease classification

A robust approach to satellite image encryption using chaotic map and circulant matrices

Enhancing plant disease detection: a novel CNN-based approach with tensor subspace learning and HOWSVD-MDA

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