TeethXrayAI

A computer vision project for AI teeth xray analysis. This repo features a yolov8 object detection model based on the dentex dataset and a simple web interface for querying it. If you dont want to selfhost it then you can use my personal deployment https://xray.cyphersec.eu/

Content

1. Self hosting
2. Training
3. Evaluation

Self Hosting

If you dont want to use the demo deployment and deploy the webUI yourself you have 2 options. You can either download the pretrained yolov8 model that i trained or you can train it yourself. Download link: dental model After downloading it/training it just pop it into the root of this repo and name it "best.pt". After that run

python3 -m pip install -r requirements.txt

followed by

python3 object_detector.py

This should make the UI available on http://127.0.0.1:8080

Training Proccess

For training i used the YOLO package from the python ultralytics libary. Luckily this makes the training fairly easy. Step 1: Head over to roboflow and create an account. Once youre logged in go to https://universe.roboflow.com/dentex/dentex-3xe7e/dataset/2 and choose download dataset. Instead of downloading it directly i would recommend to get the download code in python and running that as the dataset has to be in ~/datasets/dentex-2/ in order for our training tool to find it. After the dir is in that location run

pip3 install ultralytics roboflow && yolo train data=data.yaml model=yolov8n.pt epochs=50 imgsz=640 cache=True

The model will be saved in ~/datasets/dentex-2/runs/detect/train/weights/best.pt.

Evaluation

For the hyperparameters that i suggested my metrics are

Ultralytics YOLOv8.0.227 🚀 Python-3.10.12 torch-2.1.0+cu121 CUDA:0 (Tesla T4, 15102MiB)
Model summary (fused): 168 layers, 3006428 parameters, 0 gradients, 8.1 GFLOPs
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:00<00:00,  4.30it/s]
                   all         40        218        0.4      0.533      0.458      0.283
                Caries         40        142      0.297      0.556      0.336      0.228
           Deep Caries         40         40      0.481      0.475      0.486      0.305
              Impacted         40         26      0.633          1      0.878      0.509
     Periapical Lesion         40         10      0.191        0.1       0.13     0.0902
Speed: 0.1ms preprocess, 1.9ms inference, 0.0ms loss, 2.4ms postprocess per image

look at them and contemplate wether you would get better results training it yourself.