README.md

---
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

<p align="center">
  <a href="https://wandb.ai/nbamine-fsdm/ham10000-benchmarks">
    <img src="https://img.shields.io/badge/Weights_&_Biases-FFBE00?style=for-the-badge&logo=WeightsAndBiases&logoColor=white" />
  </a>
  <a href="https://github.com/NBAmine/Vision-models-comparaison">
    <img src="https://img.shields.io/badge/GitHub-181717?style=for-the-badge&logo=github&logoColor=white" />
  </a>
</p>

## 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.