Wound Classification Using DL

[Khushi-Pushkar]

Is your feature request related to a problem? Please describe. Wound classification using deep learning (DL) leverages convolutional neural networks (CNNs) to accurately analyze and classify wound images based on type, severity, and healing stage. This automated system enables real-time monitoring, integrates with electronic health records for seamless documentation, and is accessible via mobile devices, ensuring scalability across diverse healthcare settings. By providing consistent and precise wound assessments, this technology enhances patient outcomes and supports clinicians with reliable diagnostic tools.

dataset I'll use :- https://www.kaggle.com/datasets/yasinpratomo/wound-dataset

Describe the solution you'd like

• Data Preparation: Collect and preprocess a balanced dataset of real and fake face images, including normalization, resizing, and augmentation.
• Base Model Selection: EfficientNetB0,VGG16 , Xception , InceptionV3 like 5 different models excluding its top layers, to leverage its learned features.
• Model Construction: Add custom layers on top of the base model for binary classification, compiling with appropriate loss and metrics.
• Initial Training: Train the model with the base layers frozen to only update the new layers.
• Fine-Tuning: Unfreeze some or all of the base model layers and continue training with a lower learning rate to fine-tune the entire network.
• EDA analysis.
• Comaprioson using performance matrices such as accuracy scores , confusion matrix etc.

[invigorzz313]

@Khushi-Pushkar the issue is assigned.

[Khushi-Pushkar]

ok