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j_ijssis-2026-0029_tab_002
| Stage 1 | Algorithm 1 | Process |
|---|---|---|
| Input: Retinal Image (cropped) (ImgRGB) | ||
| Output: Lab-enhanced fundus image (Img(RGB)') | ||
| 1: Split ImgRGB in RGB color-space into red, green, and blue channels | ||
| 2: redChannel = ImgRGB[:,:,0] | ||
| 3: blueChannel = ImgRGB[:,:,1] | ||
| 4: greenChannel= ImgRGB[:,:,2] | ||
| 5: Calculate the variance σ2 of the blue channel (equation 1). 6: Convert ImgRGB to ImgLab-color-space (equations (2)–(5)). 7: Split ImgLab in Lab-color-space into individual channels | ||
| 8: LChannel = ImgLab[:,:,0] | ||
| 9: aChannel = ImgLab[:,:,1] | ||
| 10: bChannel= ImgLab[:,:,2] | ||
| 11: Variance threshold θ = 1500 of the blue channel, | ||
| 12: if σ2 ≤ θ then | ||
| 13: a’ = Apply CLAHE on ‘aChannel’ obtained from Step 9 | ||
| 14: Calculate the size of rows and columns of L-Channel as (r, c) | ||
| 15: Generate a 3D array ImgLab ’ with zeros and for given channel values | ||
| 16: ImgLa’b[:,:,0] = LChannel | ||
| 17: ImgLa’b[:,:,1] = a’ | ||
| 18: ImgLa’b[:,:,2] = bChannel | ||
| 19: else | ||
| 20: b’= Apply CLAHE on ‘aChannel’ obtained from Step 10 | ||
| 21: Calculate the rows and columns size of L-Channel = (r, c) | ||
| 22: Generate a 3D array ImgLab ’ with zeros and given channel values | ||
| 23: ImgLab’ [:,:,0] = LChannel | ||
| 24: ImgLab ’ [:,:,1] = aChannel | ||
| 25: ImgLab’ [:,:,2] = b’ | ||
| 26: end if | ||
| 27: Img(RGB)’ = Convert Img(La’b or ImgLab’ to RGB color-space (equations (6)–(9)) | ||
An analysis of the comparative study of the advantages and disadvantages of the approaches
| Inventors | Year of invention | Process | Advantages | Disadvantages |
|---|---|---|---|---|
| Mondal et al. [31] | 2023 | Hybrid DenseNet101–ResNeXt | Enhanced accuracy across DR classes | Does not classify all DR subtypes |
| Elgafi et al. [32] | 2022 | OCT-based DR detection | High accuracy using LOSO cross-validation | Limited dataset (188 images) |
| Deepa et al. [33] | 2022 | MPDCNN | Enhanced DR grading accuracy | Less effective for DR detection at very high-risk |
| Bhimavarapu & Battineni [34] | 2022 | PBPSO | Fast implementation time | Segmentation of the optic disc was not done by this method |
| AbdelMaksoud et al. [35] | 2022 | DenseNet–EyeNet hybrid | Accurate DR vs. normal classification | Not applicable to OCTA images |
| Kalaiselvi R & Vinayaki VD [38] | 2022 | R-Convolutional Network attached with Window Grouping Attention | Image segmentation becomes better | Accuracy is 95.4% |
| Gayathri et al. [42] | 2021 | M. CNN network with classifier J48 | Time value for complexity will be less in time | Unable to detect other retinal diseases |
| Erciyas & Barışçı [43] | 2021 | Faster R-CNN | Strong detection performance | Relied solely on accuracy as metric |
| Jia H et al. [40] | 2021 | MGS ROA DBN | Steady, along with dependable retina image classification | Shorter recognition rate (93.18%) |
| Bodapati et al. [45] | 2020 | Blended features–DNN | Quick convergence, lowered overfitting | Misclassified proliferative DR as moderate |