Deep Learning Based Classification of Diabetic Retinopathy

Project Domain / Category

Data Science / Machine Learning

Introduction:

We aim to utilize Python and deep learning techniques to detect diabetic retinopathy in retinal images. Diabetic retinopathy is a complication of diabetes that affects the eyes and can lead to blindness if not diagnosed and treated early. By leveraging deep learning models, we can automate the classification of retinal images into different stages of the disease, thereby aiding early diagnosis and treatment planning.

Functional Requirements for Web Application:

1.      Getting User Input:

○        Develop a user interface where users can upload or input an image of a retinal scan.

2.      Classify the Image:

○        Use the deployed deep learning model to predict the severity of diabetic retinopathy in the uploaded image.

○        Preprocess the uploaded image to ensure it matches the format expected by the model.

○        Pass the preprocessed image through the deployed model for inference.

The following steps are to be followed carefully:

i.  Data Pre-processing:

■       Normalize the image data and apply augmentation techniques to improve model performance.

■       Split the dataset into training and testing sets for model evaluation.

○                                            ii. Algorithm Selection:

■       Choose appropriate deep learning models such as Convolutional Neural Networks (CNNs) or pre-trained models like ResNet, Inception, or EfficientNet.

■       Experiment with different models to determine the most effective one for this classification task.

○        iii. Feature Extraction:

■       Extract relevant features from retinal images to assist in classification.

■       Use techniques like edge detection, histogram equalization, and vessel segmentation to enhance feature representation.

○                                            iv. Confusion Matrix:

■       Create confusion matrices to evaluate the performance of classification models.

■       Analyze the confusion matrices to assess the accuracy, precision, recall, and F1- score of the models.

○                                            v. Model Implementation:

■       Implement selected deep learning models using TensorFlow/Keras or PyTorch.

■       Train the models on the training dataset and evaluate their performance using the testing dataset.

○                                            vi. Accuracy Evaluation:

■       Calculate the accuracy of each model on the testing dataset.

■       Compare the accuracy of different models to identify the best-performing one.