Template-Type: ReDIF-Article 1.0
Author-Name:Muhammad Adee Asghar, Sobia Shafiq,Jawwad Ibrahim,Malaika Saeed
Author-Email:adeel.asghar@numl.edu.pk
Author-Workplace-Name:Department  of  Computer  Science,  National  University  of  Modern  Languages,  Khadim Hussain Road, Rawalpindi
Title:Digital Retinal Fundus Imaging: An AI-Assisted Effective Machine Learning Model for Detecting Ocular Pathology
Abstract:Ocular pathology is the study of employing digital fundus imaging to diagnose various eye-related  diseases.  Macular  degeneration,  cataracts,  glaucoma,  and  diabetic retinopathy are among these eye diseases. To distinguish between these illnesses, a manual examination of the human eye is performed. Since the work is arduous, we have used many  complex  machine  learning  techniques  in  this  paper  to  automatically  identify  eye disorders using digital retinal fundus imaging. In our initial stage, the datasetis de-noised to avoid  misclassification.  Additionally,  we  use  Contrasted  Limited  Adaptive  Histogram Equalization  (CLAHE)  to  enhance  the  images.  By  adjusting  the  histograms'  adaptive equalization  parameters,  it  is  possible  to  improve  the  fundus  image  on  each  of  theRGB channels   separately.   With   the   help   of   three   distinct   deep   CNN   models; AlexNet, GoogLeNet, and ResNet50, high-quality features were extracted in the second phase. After merging  the  features,  a  composite  feature  vector  was  created.  This  is  done  to  choose characteristics of superior quality. The Bag of Deep Features (BoDF) was used to choose features of the highest caliber. BoDF will assist in lowering the size of the feature so that it can be recognized quickly. Using Mutual Information (MI), comparable features were also eliminated.  Support  Vector  Machine  (SVM)  and  Decision  Tree  (DT)  were  then  used  to classifythe  model's  output  to  identify  ocular  diseases.  The  STARE  dataset  is  used  in  this research. When compared to current state-of-the-art models, the proposed model is more appropriate and provides an overall classification performance of 94.8% in almost 3 seconds.
Keywords:OcularPathology; Retinal Fundus Imaging; Deep Learning; Bag of Deep Features; Mutual Information
Journal:International Journal of Innovations in Science and Technology
Pages:881-896
Volume:7
Issue:2
Year:2025
Month:May
File-URL:https://journal.50sea.com/index.php/IJIST/article/view/1396/1895
File-Format: Application/pdf
File-URL:https://journal.50sea.com/index.php/IJIST/article/view/1396
File-Format: text/html
Handle: RePEc:abq:IJIST:v:7:y:2025:i:2:p:881-896