Comparative Analysis of Machine Learning Algorithms for Early Prediction of Parkinson’s Disorder based on Voice Features
Volume-4 | Issue-4

Sentiment Analysis of Nepali COVID19 Tweets Using NB, SVM AND LSTM
Volume-3 | Issue-3

Detection of Fake Job Advertisements using Machine Learning algorithms
Volume-4 | Issue-3

AI-Integrated Proctoring System for Online Exams
Volume-4 | Issue-2

Deniable Authentication Encryption for Privacy Protection using Blockchain
Volume-3 | Issue-3

Smart Fashion: A Review of AI Applications in Virtual Try-On & Fashion Synthesis
Volume-3 | Issue-4

Blockchain-Enabled Federated Learning on Kubernetes for Air Quality Prediction Applications
Volume-3 | Issue-3

Automated Waste Sorting with Delta Arm and YOLOv8 Detection
Volume-6 | Issue-3

Enhancing Health Monitoring using Efficient Hyperparameter Optimization
Volume-4 | Issue-4

Leather Defect Segmentation Using Semantic Segmentation Algorithms
Volume-4 | Issue-2

Real Time Anomaly Detection Techniques Using PySpark Frame Work
Volume-2 | Issue-1

Deniable Authentication Encryption for Privacy Protection using Blockchain
Volume-3 | Issue-3

Smart Fashion: A Review of AI Applications in Virtual Try-On & Fashion Synthesis
Volume-3 | Issue-4

Sentiment Analysis of Nepali COVID19 Tweets Using NB, SVM AND LSTM
Volume-3 | Issue-3

Audio Tagging Using CNN Based Audio Neural Networks for Massive Data Processing
Volume-3 | Issue-4

Frontiers of AI beyond 2030: Novel Perspectives
Volume-4 | Issue-4

Smart Medical Nursing Care Unit based on Internet of Things for Emergency Healthcare
Volume-3 | Issue-4

Early Stage Detection of Crack in Glasses by Hybrid CNN Transformation Approach
Volume-3 | Issue-4

ARTIFICIAL INTELLIGENCE APPLICATION IN SMART WAREHOUSING ENVIRONMENT FOR AUTOMATED LOGISTICS
Volume-1 | Issue-2

Deep Convolution Neural Network Model for Credit-Card Fraud Detection and Alert
Volume-3 | Issue-2

Home / Archives / Volume-6 / Issue-2 / Article-3

Volume - 6 | Issue - 2 | june 2024

Classifications of CNV, DME, Drusen and Normal in Retinal Optical Coherence using CNN Open Access
T. R. Ganesh Babu  , R. Praveena, Gurram Puneeth, T. Satish, Chemala Vinod Kumar   164
Pages: 149-157
Cite this article
Babu, T. R. Ganesh, R. Praveena, Gurram Puneeth, T. Satish, and Chemala Vinod Kumar. "Classifications of CNV, DME, Drusen and Normal in Retinal Optical Coherence using CNN." Journal of Artificial Intelligence and Capsule Networks 6, no. 2 (2024): 149-157
Published
11 May, 2024
Abstract

In this research, the importance of Optical Coherence Tomography (OCT) in diagnosing and monitoring various retinal disorders, including Drusen, Diabetic Macular Edema (DME), and Choroidal Neovascularization (CNV), is highlighted. These conditions can have a significant impact on retinal health and vision. The research presents a technique that utilizes batch normalization for preprocessing OCT images. For classification of retinal disorders, the research employs the Inception v3 architecture, which is known for its effectiveness in image classification tasks. The performance of the proposed technique is evaluated using performance metrics such as sensitivity, specificity, accuracy, and precision. In this work, a total of 3,133 images were obtained from Kaggle.com. Among these, 710 images were classified as CNV, 895 as DME, 725 as drusen, and 804 as normal retinal images. Python was used for both designing and Google colab was used for executing the algorithm.

Keywords

Retinal Imaging Automated Classification Deep Learning Drusen Retinal OCT image

×

Currently, subscription is the only source of revenue. The subscription resource covers the operating expenses such as web presence, online version, pre-press preparations, and staff wages.

To access the full PDF, please complete the payment process.

Subscription Details

Category Fee
Article Access Charge
15 USD
Open Access Fee Nil
Annual Subscription Fee
200 USD
After payment,
please send an email to irojournals.contact@gmail.com / journals@iroglobal.com requesting article access.
Subscription form: click here