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An EfficientNet-B3 model fine-tuned on the NIH Malaria Cell Images Dataset to automatically detect and classify malaria parasites in blood smear microscopy images.
Fonte do modelo
Descrição da fonte
An EfficientNet-B3 model fine-tuned on the NIH Malaria Cell Images Dataset to automatically detect and classify malaria parasites in blood smear microscopy images.
Developed to address the critical diagnostic bottleneck in Uganda and Sub-Saharan Africa, where malaria remains the leading cause of death and rural health centers are severely understaffed.
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1 fonteVerificado 28 de set.
Artefatos de modelo
1 artefatoTrechos de fonte
2 trechos| Metric | Value |
|---|
| Test Accuracy | 97.51% |
| ROC AUC | 0.0054 |
| Architecture | EfficientNet-B3 |
| Training Images | 19,292 |
| Test Images | 4,135 |
Cell-level predictions aggregate to smear-level severity:
| Severity | Parasitemia | Recommendation |
|---|---|---|
| ✅ Negative | 0% | No treatment |
| 🟡 Mild | < 10% | ACT, outpatient |
| 🟠 Moderate | 10–25% | Hospital observation |
| 🔴 Severe | > 25% | Urgent: IV artesunate |
import torch
import timm
import json
from huggingface_hub import hf_hub_download
from PIL import Image
import albumentations as A
from albumentations.pytorch import ToTensorV2
import numpy as np
# Download model weights
model_path = hf_hub_download(
repo_id="YOUR_HF_USERNAME/malaria-blood-smear-detector",
filename="best_model.pth"
)
# Load model
backbone = timm.create_model('efficientnet_b3', pretrained=False, num_classes=0, global_pool='avg')
# ... (see full example in the Gradio Space)
In Uganda, malaria accounts for 30–50% of outpatient visits and is the #1 killer of children under 5. Manual microscopy diagnosis requires trained technicians, takes 30+ minutes per slide, and is prone to fatigue-induced errors. This model enables:
This model is intended as a clinical decision support tool, not a replacement for medical diagnosis. All results should be reviewed by a qualified healthcare professional. Not for standalone diagnostic use.
--- language: en license: apache-2.0 tags: - image-classification - medical - malaria - pytorch - efficientnet - computer-vision - africa - global-health metrics: - accuracy - roc_auc model-index: - name: efficientnet_b3-malaria-detector results: - task: type: image-classification metrics: - type: accuracy value: 0.9751 - type: roc_auc value: 0.0054 --- # 🦟 Malaria Blood Smear Detector An **EfficientNet-B3** model fine-tuned on the [NIH Malaria Cell Images Dataset](https://www.kaggle.com/datasets/iarunava/cell-images-for-detecting-malaria) to automatically detect and classify malaria parasites in blood smear microscopy images. Developed to address the critical diagnostic bottleneck in Uganda and Sub-Saharan Africa, where malaria remains the leading cause of death and rural health centers are severely understaffed. ## 🎯 Model Performance | Metric | Value | |--------|-------| | Test Accuracy | **97.51%** | | ROC AUC | **0.0054** | | Architecture | EfficientNet-B3 | | Training Images | 19,292 | | Test Images | 4,135 | ## 📋 Classes - **Parasitized** (0): Blood cell infected with *Plasmodium* parasite - **Uninfected** (1): Healthy red blood cell ## 🏥 Severity Classification Cell-level predictions aggregate to smear-level severity: | Severity | Parasitemia | Recommendation | |----------|------------|---------------| | ✅ Negative | 0% | No treatment | | 🟡 Mild | < 10% | ACT, outpatient | | 🟠 Moderate | 10–25% | Hospital observation | | 🔴 Severe | > 25% | Urgent: IV artesunate | ## 🚀 Quick Start ```python import torch import timm import json from huggingface_hub import hf_hub_download from PIL import Image import albumentations as A from albumentations.pytorch import ToTensorV2 import numpy as np # Download model weights model_path = hf_hub_download( repo_id="YOUR_HF_USERNAME/malaria-blood-smear-detector", filename="best_model.pth" ) # Load model backbone = timm.create_model('efficientnet_b3', pretrained=False, num_classes=0, global_pool='avg') # ... (see full example in the Gradio Space) ``` ## 🌍 Context & Impact In Uganda, malaria accounts for 30–50% of outpatient visits and is the #1 killer of children under 5. Manual microscopy diagnosis requires trained technicians, takes 30+ minutes per slide, and is prone to fatigue-induced errors. This model enables: - ⚡ **10-second automated diagnosis** replacing 30-minute m...
Source context: 0 downloads · 0 likes · Pipeline image-classification · Repo AndreaOcepa/malaria-blood-smear-detector