Multi-Class of Retinal Diseases Classification via Deep Learning Techniques Based on Fundus Images

Early detection of retinal disorders is highly important in making the decision at the right moment because of the importance of treatment and prevention of diseases that are a significant cause of blindness. Experts in diagnosing these diseases frequently encounter a number of obstacles, including time, as well as variances across observers because of the occurrence of a comparable set of symptoms among groups, and some may share Categories in similar instances to address these challenges. This work proposes a novel model of deep learning approaches concerning object detection and classification. By utilizing YOLOv8, YOLOv8m-cls was used, which is distinguished by excellent efficiency, accuracy, and speed and outperforms earlier investigations. The model was trained using publicly available data from the Kaggle database, which contains 4217 fundus images, including glaucoma, cataracts, diabetic retinopathy, and the final health condition. The accuracy was 94, the precision was 95%, the recall was 97%, and the F1-score was 96 after 0.5 hours of training. This study demonstrates, based on the data obtained, particularly with the new model YOLOv8m-clsIn, revolutionizing the diagnosis of eye diseases, which leads to better patient outcomes and health care provision.

• Publication year

  2024

• Venue

  Samarra Journal of Pure and Applied Science

• Publication date

  2024-09-30

• Fields of study

  Not labeled

• Identifiers
  DOI 10.54153/sjpas.2024.v6i3.881
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Deep learning-based classification of eye diseases using Convolutional Neural Network for OCT images

  2024cited by this paper
• Eye Disease Classification Using Deep Learning Techniques

  2023cited by this paper
• Feature Weighting for Parkinson's Identification using Single Hidden Layer Neural Network

  2023cited by this paper
• A Review: Data Pre-Processing and Data Augmentation Techniques

  2022cited by this paper
• Glaucoma Detection and Classification Using Improved U-Net Deep Learning Model

  2022cited by this paper
• Detection of diabetic retinopathy using deep learning methodology

  2021cited by this paper
• Classification of Fundus Images Based on Deep Learning for Detecting Eye Diseases

  2021cited by this paper
• Classification Model Evaluation Metrics

  2021cited by this paper
• CataractNet: An Automated Cataract Detection System Using Deep Learning for Fundus Images

  2021cited by this paper
• Computer-Aided Cataract Detection Using Random Forest Classifier

  2021cited by this paper
• Machine Learning Based Automated Segmentation and Hybrid Feature Analysis for Diabetic Retinopathy Classification Using Fundus Image

  2020cited by this paper
• Bagging of Convolutional Neural Networks for Diagnostic of Eye Diseases

  2020cited by this paper
• The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation

  2020cited by this paper
• Glaucoma diagnosis using transfer learning methods

  2019cited by this paper
• Ocular diseases diagnosis in fundus images using a deep learning: approaches, tools and performance evaluation

  2019cited by this paper
• Automated detection of Glaucoma using deep learning convolution network (G-net)

  2019cited by this paper
• A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends

  2019cited by this paper
• Convolutional Neural Network for Multi-class Classification of Diabetic Eye Disease

  2018cited by this paper
• A Review On Evaluation Metrics For Data Classification Evaluations

  2015cited by this paper
• Confusion Matrix-based Feature Selection

  2011cited by this paper
• Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation

  2011cited by this paper

• Comparison of VGG16 and VGG19 CNN Architecture for Rice Disease Image Classification

  2026cites this paper