README.md

metadata

license: apache-2.0
tags:
  - vision
  - image-classification

Surgicare

Surgicare (Surgical + Care)

SurgiCare is an AI system designed to support post-surgery patient recovery. In this repository, we focus on a wound classification model trained on an open-source dataset. Our objective is to improve the accuracy of wound detection and guide patients in managing their wound recovery efficiently.

• Online Demo: https://surgicare-demo.streamlit.app/

• Wound dataset: https://www.kaggle.com/datasets/ibrahimfateen/wound-classification)

• Github Repo: https://github.com/PogusTheWhisper/SurgiCare.git

• Pretrained Models:

  • Surgicare-V1-best: https://huggingface.co./PogusTheWhisper/SurgiCare/resolve/main/SurgiCare-V1-best.keras
  • Surgicare-V1-fast: https://huggingface.co./PogusTheWhisper/SurgiCare/resolve/main/SurgiCare-V1-mini-best-model.keras
  • Surgicare-V1-mini: https://huggingface.co./PogusTheWhisper/SurgiCare/resolve/main/SurgiCare-V1-mini-best-model.keras

Result of training!!

Efficientnet B3

• Accuracy: 0.9062 Approximately 11 seconds per image.
• I used EfficientNet-B3 to train for 25 epochs, monitoring the validation loss.

MobileNetV3Large

• Accuracy: 0.7969 Approximately 5 seconds per image.
• I used MobileNetV3Large to train for 50 epochs, monitoring the validation loss.

MobileNetV3Small

• Accuracy: 0.7812 Approximately 4 seconds per image.
• I used MobileNetV3Small to train for 50 epochs, monitoring the validation loss.