This project aims to develop a cost-effective solution for monitoring diabetic retinopathy (DR) using transfer learning and data preprocessing techniques. The goal is to leverage pre-trained deep learning models to detect signs of DR in retinal images and provide early detection and monitoring of the disease.
The dataset used in this project is the Diabetic Retinopathy Detection dataset from Kaggle. It contains over 35,000 retinal images with labels indicating the severity of DR, ranging from 0 (no DR) to 4 (proliferative DR). The images are of varying sizes and quality, and some may have artifacts or other abnormalities.
To preprocess the dataset, we first resize all the images to a common size of 224x224 pixels and convert them to grayscale to reduce the input dimensionality. We then normalize the pixel values to the range [0, 1] and perform data augmentation by randomly flipping and rotating the images during training.
We use transfer learning to leverage the pre-trained ResNet50 model, which has been trained on millions of images and has learned to recognize a wide range of features. We replace the final fully connected layer of the ResNet50 model with a new layer that has 5 output nodes, one for each severity level of DR. We then train the model on our preprocessed dataset using the cross-entropy loss function and the Adam optimizer.
We train the model for a specified number of epochs and evaluate its performance on a held-out validation set. We monitor the validation loss and accuracy to detect overfitting and early stopping is used to prevent further training if the validation loss does not improve after a certain number of epochs.
This project demonstrates the potential of transfer learning and data preprocessing for developing cost-effective solutions for medical image analysis. By leveraging pre-trained models and carefully preprocessing the data, we can achieve high accuracy with minimal resources and provide early detection and monitoring of diseases such as diabetic retinopathy.