Automated Diabetic Retinopathy Grading based on the Modified Capsule Network Architecture

Kumari, Prabha; Saxena, Priyank

doi:10.1080/03772063.2023.2185304

Cited by 4 publications

(1 citation statement)

References 43 publications

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“…where, 𝑁(℘) is the overall sum of boxes that involves a portion of the tree and 𝜌 𝑘 = 𝜇 𝑘 Ω ∈ (0,1) is the quantity of retinal tree enclosed in the k-the box; 𝜇 𝑘 is the sum of pixels in the k-the box and Ω is the entire sum of pixels in the tree. A correlation structure is formulated by the correlation integral approximated by the following summation formal [29]:…”

Section: Multi-scale Fractal Dimension (Mfd)mentioning

confidence: 99%

Enhancing Image Quality Through a Novel Multiscale Fractal Dimension Formulated by the Characteristic Function

Rashied,

Jeribi

2024

MMEP

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Fractal dimensions have been widely utilized as analytical tools in image processing due to their potential to uncover intricate patterns. This study introduces a novel multiscale fractal dimension (MFD), derived from the characteristic function (CF), which exhibits unique properties, including self-similarity. One significant aspect of image processing research involves the effective reduction of noise, which can interfere with image clarity during transmission. Noise in images poses challenges to their utilization across various applications. In recent years, the strategy of decreasing noise in multiplicative pictures (DNM) has been extensively adopted by researchers to tackle this issue. In this context, the newly proposed MFD is applied to DNM as an innovative method for enhancing image quality. Preliminary results indicate the proposed approach's efficacy, thereby suggesting its potential utility in advanced image processing applications.

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Section: Multi-scale Fractal Dimension (Mfd)mentioning

confidence: 99%