Our project is related to automated detection and classification of diabetic retinopathy using deep learning. In this, we detect and classify the lesions. We use bench mark dataset that contain 1451 images. we use Mask RCNN model for training dataset, Faster RCNN for detection and SVM and Convo
Automated detection and classification of diabetic retinopathy using deep learning
Our project is related to automated detection and classification of diabetic retinopathy using deep learning. In this, we detect and classify the lesions. We use bench mark dataset that contain 1451 images. we use Mask RCNN model for training dataset, Faster RCNN for detection and SVM and Convolutional Neural Network for classification. We find the best accuracy and comparison this accuracy with previous ones.
To predict the severity level of DR using retinal images.
To provide the capability to localize the exact lesion.
To enhance classification and detection classification on benchmark dataset with 6 classes.
To detect eye disease faster.
Dataset preprocessing and data annotation.
Faster RCNN for detection.
Convolutional Neural Network for classsification.
It can facilitate the fast processsing in eye disease.
This system helpful in health care centres.
MATLAB based application capable of classifying the given input retinal images and also localize the lesion in retinaal images.
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| smartphone retinal camera with accessories | Equipment | 1 | 60000 | 60000 |
| Total in (Rs) | 60000 |
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