Autoaudiogram: Object detection-based audiogram classification.

AutoAudiogram is an object detection-based audiogram classification system. The model uses object detection to detect the audiological symbols from the audiogram and then use them to classify the degree of hearing loss severity.

For the data collection, we annotation 200 audiograms including graphs, tables, and 8 audiological symbols.

Get Started

Clone the repository and navigate into the project directory

git clone https://github.com/biodatlab/autoaudiogram.git
cd autoaudiogram

Quick Started Inference

To make an inference on a given audiogram, navigate to the root of directory. Then, you can access the sample image in the sample_images directory.

from processing import feature_extraction, preprocessing_feature, classified_feature
image_dir = "sample_images/" # change this to the directory of the image
filename = "1.jpg" # change this to the filename of the image
# extract audiological symbols from image
extract_rt_df, extract_lt_df = feature_extraction(
    image_dir, filename,
    box_model_path="models/yolov8_box.pt",
    symbol_model_path="models/yolov8_symbol.pt"
)
# preprocess data for classification
feature_rt_df, feature_lt_df = preprocessing_feature(extract_rt_df, extract_lt_df)
# classify hearing severity
print(classified_feature(feature_rt_df, feature_lt_df))

To run the demo app

gradio app.py

or you can use the demo.ipynb as a starter.

Training Graph and Table Detection Model

Navigate to the directory cd src/data_extracter/graph_table and organize the folder in this directory as follows

graph_table
|____data
| |____images
| |____labels
|____runs
|____config.yaml
|____eval.ipynb
|____train.ipynb
|____train.py

Then run train.py using

python -m train

Audiological symbol training

Navigate to the directory cd src/data_extracter/symbol_detection and organize the folder as follows

symbol_detection
|____data
| |____images
| |____labels
|____runs
|____config.yaml
|____eval.ipynb
|____train.ipynb
|____train.py

Then run train.py using

python -m train

Severity classification

Navigate to the directory cd src/classification and organize folder as follows

|classification
|____data
| |____train_extract.csv # example file name
| |____test_extract.csv
| |____train_true.csv
| |____test_true.csv
|____evalute
| |____extract
| |____true
|____model
|____eval.ipynb
|____train.ipynb

python -m train --path <path to train file> # python -m train --path "data/data_extract.csv"

Evaluation

Object Detection

mAP score of each audiological symbol

Symbols	⭕	❌	<	>	🔳	△	[	]
YOLOv5	0.957	0.925	0.848	0.841	0.833	0.986	0.826	0.875
YOLOv8	0.945	0.945	0.857	0.876	0.915	0.85	0.817	0.944

Hearing severity classification

Hearing loss classification performance of top-5 models using AutoML.

Automatic feature extraction

Model	Test Accuracy	Test AUC	Test Recall	Test Precision	Test F1 Score
Gradient Boosting Classifier	0.9401	0.9856	0.9401	0.9419	0.9388
Light Gradient Boosting Machine	0.9472	0.9842	0.9472	0.9472	0.9472
Random Forest Classifier	0.933	0.9806	0.933	0.9352	0.9317
Decision Tree Classifier	0.9344	0.9522	0.9344	0.9346	0.9335
Extra Trees Classifier	0.9044	0.9815	0.9044	0.9043	0.9033

Manual feature extraction

Model	Test Accuracy	Test AUC	Test Recall	Test Precision	Test F1 Score
Gradient Boosting Classifier	0.9629	0.9892	0.9629	0.9631	0.9629
Light Gradient Boosting Machine	0.9629	0.9861	0.9629	0.9630	0.9629
Random Forest Classifier	0.9643	0.9865	0.9643	0.9645	0.9643
Decision Tree Classifier	0.9272	0.9528	0.9272	0.9286	0.9275
Extra Trees Classifier	0.9501	0.9834	0.9501	0.9506	0.9500

Data Access Request

Access to our private dataset of 200 audiograms with annotation requires completion of a form. This ensures security and suitability for research/educational purposes. Submit the form here for consideration and we will grant access to the the dataset via Google Drive.