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Early pigment spot segmentation and classification from iris cellular image analysis with explainable deep learning

Amjad R Khan1, Rabia Javed2, Tariq Sadad3

  • 1Department of Information Systems, Prince Sultan University, Riyadh 66833, Saudi Arabia.

Biochemistry and Cell Biology = Biochimie Et Biologie Cellulaire
|October 31, 2023
PubMed
Summary

This study introduces an explainable deep learning model for early iris pigment spot segmentation and classification, improving early diagnosis of retinal disorders and preventing blindness. The model accurately analyzes iris cellular images, outperforming existing methods.

Keywords:
healthrisksiris cellular imageslife expectancypigment spotsretinal disorders

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Area of Science:

  • Ophthalmology
  • Computer Science
  • Medical Imaging

Background:

  • Retinal disorders affect thousands globally, necessitating early diagnosis to prevent blindness.
  • Iris spot segmentation is crucial for analyzing iris cellular images, which are often degraded by noise and off-angles.
  • Current segmentation methods struggle with noisy, uncooperative iris images and lack efficiency.

Purpose of the Study:

  • To develop an explainable deep learning model for precise iris pigment spot segmentation and classification.
  • To improve early detection of eye syndromes by analyzing iris cellular images.
  • To overcome limitations of traditional, costly, and time-consuming diagnosis processes.

Main Methods:

  • An explainable deep learning model integrated with a multiclass support vector machine was developed.
  • The model was trained and tested on three benchmark datasets: MILE, UPOL, and Eyes SUB.
  • Iris cellular images were analyzed for early pigment spot segmentation and classification.

Main Results:

  • The proposed model demonstrated superior performance compared to existing methods in terms of classification errors.
  • The model effectively located micro-pigment spots on iris surfaces.
  • Experimental results on benchmark datasets confirmed the model's accuracy and effectiveness.

Conclusions:

  • The developed explainable deep learning model offers a promising approach for early iris pigment spot analysis.
  • This method can aid in the early diagnosis of retinal disorders, potentially preventing blindness.
  • The model's effectiveness in segmenting and classifying iris pigment spots enhances diagnostic capabilities.