Performance Analysis of OCT Angiography Assessment of Central Retinal Vein Occlusion
Implementation Plan:
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Step 1: Initially we collect and load the input images from an OCTA Dataset.
Step 2: Next we preprocess the image using a Gaussian filter, Data Augmentation, Image Normalization.
Step 3: Next we perform Image segmentation using Preprocessed Image.
Step 4: Next we perform the Feature Extraction step by using Segmented Image.
Step 5: Next, we approach the Statistical Analysis based on Features extracted in the previous step.
Step 6: Next, we Visualize the Vessel Density and Features for Data Visualization Step.
Step 7: The performance of these work is measured through the following performance metrics,
7.1: Accuracy (%)
7.2: Precision (%)
7.3: F-Score (%)
7.4: Sensitivity (%)
7.5: Recall (%)
Software Requirement:
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1. Development Tool: Python 3.11.4
2. Development OS: Windows 10(64-bit)
Dataset Link:-
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1.Retinal OCT and OCTA data (raw) (kaggle.com)
Note:-
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1) If the above plan does not satisfy your requirement, please provide the processing details, like the above step-by-step.
2) Please note that this implementation plan does not include any further steps after it is put into implementation.
