An Efficient Diabetic Retinopathy Detection and Classification System Using LRKSA-CNN and KM-ANFIS

If Diabetic Retinopathy (DR) is not diagnosed in the early stages, it leads to impaired vision and often causes blindness. So, diagnosis of DR is essential. For detecting DR and its diverse stages, various approaches were developed. However, they are limited in considering microstructural changes of visual pathways associated with the visual impairment of DR. Thus, this work proposes an effective Linearly Regressed Kernel and Scaled Activation-based Convolution Neural Network (LRKSA-CNN) to diagnose DR utilizing multimodal images. Primarily, the input Optical Coherence Tomography (OCT) image is preprocessed for contrast enhancement utilizing Contrast-Limited Adaptive Histogram Equalization (CLAHE) and resolution enhancement utilizing the Gaussian Mixture Model (GMM). Likewise, the Magnetic Resonance Imaging (MRI) image’s contrast is also improved, and edge sharpening is performed utilizing Unsharp Mask Filter (USF). Then, preprocessed images are segmented utilizing the Intervening Contour Similarity Weights-based Watershed Segmentation (ICSW-WS) algorithm. Significant features are extracted from the segmented regions. Next, important features are chosen utilizing the Min-max normalization-based Green Anaconda Optimization (MM-GAO) algorithm. By utilizing the LRKSA-CNN technique, the selected features were classified into DR and Non-Diabetic Retinopathy (NDR). Hence, utilizing the Krusinka Membership-based Adaptive Neuro Fuzzy Interference System (KM-ANFIS), various stages of DR were classified based on the presence of intermediate features. Lastly, the proposed system achieves superior outcomes than the baseline systems.

@article{Kumari2024,
  title     = {An Efficient Diabetic Retinopathy Detection and Classification System Using LRKSA-CNN and KM-ANFIS},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {12},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Rachna Kumari and Sanjeev Kumar and Sunila Godara},
  doi       = {10.14569/IJACSA.2024.0151263},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151263}
}