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--- |
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language: |
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- en |
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base_model: |
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- Ultralytics/YOLOv5 |
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- Ultralytics/YOLOv8 |
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- Ultralytics/YOLO11 |
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- microsoft/resnet-18 |
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- google/vit-base-patch16-224 |
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- microsoft/swin-tiny-patch4-window7-224 |
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- facebook/convnext-tiny-224 |
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pipeline_tag: image-classification |
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--- |
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# HAM10000 Skin Lesion Classification |
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<p align="center"> |
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<a href="https://wandb.ai/nbamine-fsdm/ham10000-benchmarks"> |
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<img src="https://img.shields.io/badge/Weights_&_Biases-FFBE00?style=for-the-badge&logo=WeightsAndBiases&logoColor=white" /> |
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</a> |
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<a href="https://github.com/NBAmine/Vision-models-comparaison"> |
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<img src="https://img.shields.io/badge/GitHub-181717?style=for-the-badge&logo=github&logoColor=white" /> |
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</a> |
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</p> |
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## Model Description |
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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. |
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- **Task:** Binary Image Classification |
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- **Dataset:** HAM10000 (Human Against Machine) |
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- **Classes:** Benign, Malignant |
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- **Developer:** NBAmine |
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## Objective & Methodology |
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The goal of this project is to identify the most effective architecture for early skin cancer detection. We evaluate several models including: |
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- **YOLO Series:** v5n, v8n, 11n (Classification) |
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- **Transformers:** ViT-B/16, Swin Tiny |
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- **CNNs:** ResNet18, ConvNeXt Tiny |
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### Training Highlights |
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- **Input Resolution:** 224x224 pixels. |
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- **Augmentation:** 180° rotations and flips to ensure rotation invariance. |
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- **Class Balancing:** Strategic oversampling of the Malignant class to mitigate dataset imbalance and improve clinical reliability. |
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## Evaluation Metrics |
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The models are evaluated with a focus on sensitivity to ensure high detection rates for malignant cases: |
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- **Primary Metrics:** Recall (Malignant class), Macro F1-Score. |
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- **Secondary Metrics:** Accuracy, Precision, Validation Loss. |
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## Intended Use |
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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. |
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