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-large-turbo: https://huggingface.co/PogusTheWhisper/SurgiCare/resolve/main/SurgiCare-V1-large-turbo.keras
- Surgicare-V1-large: https://huggingface.co/PogusTheWhisper/SurgiCare/resolve/main/SurgiCare-V1-large.keras
- Surgicare-V1-medium: https://huggingface.co/PogusTheWhisper/SurgiCare/resolve/main/SurgiCare-V1-medium.keras
- Surgicare-V1-small: https://huggingface.co/PogusTheWhisper/SurgiCare/resolve/main/SurgiCare-V1-small.keras
Result of standard models
EfficientnetV2 B3
- Accuracy: 0.6884
Efficientnet B3
- Accuracy: 0.7436
MobileNetV3Large
- Accuracy: 0.6164
MobileNetV3Small
- Accuracy: 0.6199
Result of our models!!
EfficientnetV2 B3
- Accuracy: 0.9127
- Training Details: I used EfficientNet-B3 to train for 50 epochs, monitoring the validation loss.
Efficientnet B3
- Accuracy: 0.9062
- Training Details: I used EfficientNet-B3 to train for 25 epochs, monitoring the validation loss.
MobileNetV3Large
- Accuracy: 0.7969
- Training Details: I used MobileNetV3Large to train for 50 epochs, monitoring the validation loss.
MobileNetV3Small
- Accuracy: 0.7812
- Training Details: I used MobileNetV3Small to train for 50 epochs, monitoring the validation loss.
