Team03

Yixuan Wu (st178402)
Nan Jiang (st181029)

Diabetic Retinopathy Detection

Diabetic retinopathy, a major cause of blindness linked to diabetes, is one of the major causes of blindness in the Western world. The traditional manual screening process for DR detection, however, is both expensive and time-consuming. To address these issues, we have developed deep neural network models, which are designed to streamline the DR detection process, enhancing efficiency and reliability.

Binary Classification task

Configuration Setup

Before you start, you need to configure the paths for your project files and dataset to ensure the code can access the required files correctly.

1. Open Configuration File

First, navigate to the configs/config.gin file.

2. Set Dataset Name

For this project, we specifically use the IDRID dataset for the binary classification task. So please check

load.name='idrid'

3. Set Project Folder Location

In the configuration file, you need to specify the location of your project files. For example, if your project is located at /misc/home/RUS_CIP/st178402/dl-lab-23w-team03/diabetic_retinopathy, you should set it as follows:

load.data_dir='/misc/home/RUS_CIP/st178402/dl-lab-23w-team03/diabetic_retinopathy'

4. Set Dataset Path

Next, specify the path to your IDRID dataset. If the dataset is located at /misc/home/data/IDRID_dataset, configure it like this:

prepare_image_paths_and_labels.data_dir='/misc/home/data/IDRID_dataset'

Training the Model

To train the model for the binary classification task of diabetic retinopathy detection, follow these steps:

1. Navigate to `main.py`.

2. Choose a Model

This project offers four different models. You will need to select which model you wish to use for training. We have 3 models for binary classification: simple CNN, transferlearning with renset50 and densnet121. Change the model_name variable to the name of the model you want to use. For example:

model_name = 'simple_cnn'

Also, update the folder variable to match the model_name:

folder = 'simple_cnn'

3. Configure Training Parameters

Next, you need to set your preferred training parameters in the configs/config.gin file. Open configs/config.gin. Set the total number of training steps, log interval, and checkpoint interval as follows:

train.Trainer.total_steps = 10000
train.Trainer.log_interval = 100
train.Trainer.ckpt_interval = 100

4. Training

Training logs and results will be stored under experiment/your_model_name. To start training, execute the command with the appropriate flags.

main.py --train=true --mode=binary

for example

flags.DEFINE_boolean("train",True, "Specify whether to train or evaluate a model.")
flags.DEFINE_string("mode", "binary", "Specify the classification mode: binary or multiclass.")

Finally, run main.py and train the model.

Results

1.Evaluation and Metrics

To start evaluation, execute the command with the appropriate flags.

main.py --train=False --mode=binary

for example

flags.DEFINE_boolean("train",False, "Specify whether to train or evaluate a model.")
flags.DEFINE_string("mode", "binary", "Specify the classification mode: binary or multiclass.")

For simple CNN, we get the result:

Label	Precision	Recall	Specificity	F1-Score
0	0.846	0.688	0.891	0.759
1	0.766	0.891	0.688	0.824

For transferlearning, we get the results:

Model Name	Accuracy
ResNet50	61.17%
DenseNet121	65.05%

2.Deep Visualization

Navigate to visualization/gradcam.py
Place an image, such as IDRiD_102.jpg, in the same directory as gradcam.py for easy access.
Update the grad_cam Function Parameters Ensure the grad_cam function is correctly configured:
Change model.get_layer('your_layer_name').output to target the specific layer whose outputs you want to visualize. Typically, this is the last convolutional layer of the model. Replace your_layer_name with the actual name of the layer you wish to use.
Ensure Correct Model Path The model path is specified as follows:

experiments/your_model_id/ckpts/saved_model

Multi-Class Classification task

Configuration Setup

1. Set Dataset Name

For this project, we specifically use the Kaggle Challenge Dataset provided by EyePACS dataset for the binary classification task. So please check

load.name='eyepacs'

2. Set Dataset Path

Go to input_pipeline/dataset.py and find function get_eyepacs_tfrecord(), the base path is according to the location of EyePACS dataset.Like:

    base_path = '/misc/home/data/tensorflow_datasets/diabetic_retinopathy_detection/btgraham-300/3.0.0'

Training

Like binary task, change the model_name and 'folder'

main.py --train=true --mode=multi

Results

By using simple CNN model, we get the results:

Label	Precision	Recall	Specificity	F1-Score
0	0.636	0.786	0.338	0.703
1	0.040	0.094	0.929	0.056
2	0.220	0.149	0.853	0.178
3	0.304	0.092	0.983	0.141
4	0.458	0.127	0.987	0.199

Regression task

For the regression task, we utilise a single model called 'simple_cnn_regression'.

Training

Navigate directly to main.py and modify the model_name to simple_cnn_regression and as well as the folder.

Results

For regression task, we choose mean squared error and mean absolute error as our metrics.

Model	Value
Test MAE	1.99
Test MSE	4.76

Human Activity Recognition

In this task, we use 2 kinds of labeling ways: S2S and S2L. Yixuan is responsible for S2S and Nan is responsible for S2L. Thus, we have 2 kinds of codes.

S2S

Configuration Setup

1. Open Configuration File

First, navigate to the configs/config.gin file.

2. Set Dataset Name

For this project, we specifically use the IDRID dataset for the binary classification task. So please check

load.name='HAPT'

Set Dataset Path

Go to input_pipeline/preprocessing_and_visualization.py and find data_dir and labels_file the path is according to the location of HAPT dataset.

    data_dir = '/misc/home/data/HAPT_dataset/RawData'
    labels_file = '/misc/home/data/HAPT_dataset/RawData/labels.txt'

Run it to get combined datasets.

Training the Model

To train the model for human activity recognition, follow these steps:

Navigate to main.py.
Choose a Model This project offers four different models. You will need to select which model you wish to use for training. We have 2 models for S2S: RNN model and GRU model. Change the model_name variable to the name of the model you want to use. For example:
```
model_name = 'rnn'
```
Also, update the folder variable to match the model_name:
```
folder = 'rnn_model'
```
Configure Training Parameters Next, you need to set your preferred training parameters in the configs/config.gin file. Open configs/config.gin. Set the total number of training steps, log interval, and checkpoint interval as follows:

train.Trainer.total_steps = 10000
train.Trainer.log_interval = 100
train.Trainer.ckpt_interval = 100

Training Training logs and results will be stored under experiment/your_model_name. To start training, execute the command with the appropriate flags.

main.py --train=true

for example

flags.DEFINE_boolean("train",True, "Specify whether to train or evaluate a model.")

Finally, run main.py and train the model.

Results

Evaluation and Metrics To start evaluation, execute the command with the appropriate flags.

main.py --train=False

for example

flags.DEFINE_boolean("train",False, "Specify whether to train or evaluate a model.")

For RNN model, we get the result:

Label	Precision	Recall	Specificity	F1-Score
WALKING	0.982	0.915	0.997	0.947
WALKING_UPSTAIRS	0.960	0.962	0.994	0.961
WALKING_DOWNSTAIRS	0.920	0.977	0.988	0.948
SITTING	0.933	0.940	0.987	0.936
STANDING	0.950	0.926	0.989	0.938
LAYING	0.981	0.974	0.996	0.977
STAND_TO_SIT	0.526	0.805	0.994	0.636
SIT_TO_STAND	0.797	0.798	0.998	0.797
SIT_TO_LIE	0.696	0.617	0.996	0.654
LIE_TO_SIT	0.444	0.659	0.992	0.531
STAND_TO_LIE	0.557	0.629	0.993	0.591
LIE_TO_STAND	0.584	0.535	0.994	0.558

For GRU model, we get the result:

Label	Precision	Recall	Specificity	F1-Score
WALKING	0.969	0.951	0.995	0.960
WALKING_UPSTAIRS	0.969	0.932	0.995	0.950
WALKING_DOWNSTAIRS	0.930	0.979	0.990	0.954
SITTING	0.798	0.972	0.961	0.876
STANDING	0.992	0.820	0.998	0.898
LAYING	0.969	0.976	0.993	0.972
STAND_TO_SIT	0.567	0.883	0.995	0.691
SIT_TO_STAND	0.694	0.807	0.997	0.746
SIT_TO_LIE	0.710	0.701	0.996	0.705
LIE_TO_SIT	0.713	0.559	0.996	0.627
STAND_TO_LIE	0.642	0.752	0.994	0.693
LIE_TO_STAND	0.363	0.483	0.991	0.414

Visualization RNN model：

GRU model：

S2L

In sequence to label method, we create a label for each window, then we start to train and evaluate our model.

Configuration Setup

Before you start, you need to configure the paths for your project files and dataset to ensure the code can access the required files correctly.

1. Open Configuration File

First, navigate to the configs/config.gin file.

2. Set Dataset Name

For this project, we specifically use the IDRID dataset for the binary classification task. So please check

load.name='HAPT'

3. Set Project Folder Location

In the configuration file, you need to specify the location of tfrecord files, which are goting to be generated after you run main.py. For example, if your project is located at 'D:\HAR\pythonProject' , you should set it as follows:

load.data_dir=r'D:\HAR\pythonProject\input_pipeline_s2l'

4. Set Dataset Path

The dataset is located at r'/home/data/HAPT_dataset/RawData' on the server, so set preprocessor.file_path = r'/home/data/HAPT_dataset/RawData' .

Training

Go to main.py, set the Flag to be True, and type your model_name and folder, there are two options, namely, 'lstm' or 'crnn', then train the selected model.

Evaluation

Go to main.py, set the flag to be False, and type your model_name and folder, same as above, then it produces the result.

visualization

Go to visualization.py and run it. You will get the visualization for a sequence for ground truth and prediction.

optimization

Go to optimization.py and run it to do tuning.

Result

for CRNN model we have a nice balanced accuracy of 84.46% and following confusion matrix:

And visualization as follows:

Ground truth
Prediction

Name		Name	Last commit message	Last commit date
Latest commit History 71 Commits
diabetic_retinopathy		diabetic_retinopathy
human_activity_recognition		human_activity_recognition
human_activity_recognition_s2l		human_activity_recognition_s2l
.gitignore		.gitignore
README.md		README.md
requirements.txt		requirements.txt

NANDKLFSAJDKJHAjkhdsakxz1312/dllab

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Team03

Diabetic Retinopathy Detection

Binary Classification task

Configuration Setup

1. Open Configuration File

2. Set Dataset Name

3. Set Project Folder Location

4. Set Dataset Path

Training the Model

1. Navigate to main.py.

2. Choose a Model

3. Configure Training Parameters

4. Training

Results

1.Evaluation and Metrics

2.Deep Visualization

Multi-Class Classification task

Configuration Setup

1. Set Dataset Name

2. Set Dataset Path

Training

Results

Regression task

Training

Results

Human Activity Recognition

S2S

Configuration Setup

1. Open Configuration File

2. Set Dataset Name

Set Dataset Path

Training the Model

Results

S2L

Configuration Setup

1. Open Configuration File

2. Set Dataset Name

3. Set Project Folder Location

4. Set Dataset Path

Training

Evaluation

visualization

optimization

Result

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1. Navigate to `main.py`.

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