language:
- en
base_model:
- Ultralytics/YOLOv5
- Ultralytics/YOLOv8
- Ultralytics/YOLO11
- microsoft/resnet-18
- google/vit-base-patch16-224
- microsoft/swin-tiny-patch4-window7-224
- facebook/convnext-tiny-224
pipeline_tag: image-classification
HAM10000 Skin Lesion Classification
Model Description
This model is a fine-tuned version of a computer vision architecture (YOLO, Transformer, or CNN) trained on the HAM10000 dataset. The primary objective is the binary classification of skin lesions into two categories: Benign and Malignant. This work is part of a benchmark study evaluating modern architectures for dermatological triage.
- Task: Binary Image Classification
- Dataset: HAM10000 (Human Against Machine)
- Classes: Benign, Malignant
- Developer: NBAmine
Objective & Methodology
The goal of this project is to identify the most effective architecture for early skin cancer detection. We evaluate several models including:
- YOLO Series: v5n, v8n, 11n (Classification)
- Transformers: ViT-B/16, Swin Tiny
- CNNs: ResNet18, ConvNeXt Tiny
Training Highlights
- Input Resolution: 224x224 pixels.
- Augmentation: 180° rotations and flips to ensure rotation invariance.
- Class Balancing: Strategic oversampling of the Malignant class to mitigate dataset imbalance and improve clinical reliability.
Evaluation Metrics
The models are evaluated with a focus on sensitivity to ensure high detection rates for malignant cases:
- Primary Metrics: Recall (Malignant class), Macro F1-Score.
- Secondary Metrics: Accuracy, Precision, Validation Loss.
Intended Use
This model is intended for research and educational purposes only. It is designed as a decision-support tool for medical imaging analysis and is not a replacement for professional clinical diagnosis by a dermatologist.