---
license: mit
language:
- en
tags:
- malaria
- medical-imaging
- blood-smear
- microscopy
- image-classification
- pytorch
- mobilenetv2
- transfer-learning
datasets:
- NIH-Malaria-Cell-Images
metrics:
- accuracy
- f1
- precision
- recall
pipeline_tag: image-classification
library_name: pytorch
---

# MobileNetV2 (Fine-Tuned) — Malaria Parasite Detection

**Recommended production model.** Fine-tuned MobileNetV2 for malaria parasite detection from thin blood smear microscopy images. Achieves **97.97% test accuracy** — the highest in our model family. Part of [LocalMedScan](https://github.com/Svetozar-Technologies/LocalMedScan).

## Model Details

| Property | Value |
|:---------|:------|
| **Architecture** | MobileNetV2 (ImageNet pretrained, fine-tuned) |
| **Parameters** | ~3.4M |
| **Model Size** | 14 MB |
| **Input** | RGB 224x224 blood smear cell images |
| **Output** | Binary classification: Parasitized vs Uninfected |
| **Training** | 3-phase progressive unfreezing from ImageNet weights |
| **Framework** | PyTorch |
| **License** | MIT |

## Performance

### Test Results (NIH Malaria Dataset, 2,757 test images)

| Metric | Without TTA | With 5-View TTA |
|:-------|:-----------:|:----------------:|
| **Accuracy** | 97.68% | **97.97%** |
| **Sensitivity** | 97.12% | 97.41% |
| **Specificity** | 98.22% | 98.51% |
| **Precision** | 98.19% | 98.49% |
| **F1 Score** | 97.65% | 97.95% |

### Comparison with PlasmoSENet

| Model | Params | Size | Accuracy (TTA) | F1 | Training |
|:------|:------:|:----:|:--------------:|:--:|:---------|
| **MobileNetV2** (this model) | 3.4M | 14 MB | **97.97%** | **97.95%** | Fine-tuned from ImageNet |
| [PlasmoSENet](https://huggingface.co/Svetozar1993/LocalMedScan-malaria-plasmosenet) | 10.6M | 41 MB | 96.55% | 96.47% | Trained from scratch |

MobileNetV2 outperforms PlasmoSENet by 1.42 percentage points while being 3x smaller and 3x faster. Transfer learning from ImageNet provides a decisive advantage on this dataset size (27K images).

**Use MobileNetV2 for deployment. Use PlasmoSENet for research** into domain-specific architectures or when ImageNet-free training is required.

## Key Achievements

1. **97.97% test accuracy** with 5-view TTA — exceeds most published results on this benchmark
2. **3-phase progressive unfreezing** prevents catastrophic forgetting while maximizing adaptation
3. **TransformSubset bug fix** — corrected a common PyTorch `random_split` bug where train/val/test subsets inadvertently share transforms, causing training to run without augmentation
4. **5-view TTA** exploiting blood cell rotational invariance (+0.29% accuracy)
5. **Lightweight** — 14 MB model runs on any device including mobile

## Training Details

### 3-Phase Progressive Unfreezing

- **Phase 1**: Classifier head only (frozen backbone)
- **Phase 2**: Last inverted residual blocks unfrozen, reduced LR
- **Phase 3**: Full end-to-end fine-tuning, further reduced LR

### Augmentation

Strong augmentation pipeline with RandomResizedCrop, RandomRotation(90), ColorJitter, GaussianBlur, and RandomErasing. The `TransformSubset` wrapper ensures training and validation use independent transforms.

## Dataset

**NIH Malaria Cell Images Dataset** (Rajaraman et al., 2018)
- 27,558 annotated cell images from Giemsa-stained thin blood smears
- 13,779 Parasitized + 13,779 Uninfected (balanced)
- Split: 80% train / 10% val / 10% test (seed=42)

## Usage

```python
import torch
import torchvision.models as models

# Load model
model = models.mobilenet_v2(weights=None)
model.classifier[1] = torch.nn.Linear(model.last_channel, 2)
state_dict = torch.load("model.pth", map_location="cpu", weights_only=True)
model.load_state_dict(state_dict)
model.eval()

# Inference
from torchvision import transforms
from PIL import Image

transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])

img = Image.open("blood_smear_cell.png").convert("RGB")
tensor = transform(img).unsqueeze(0)

with torch.inference_mode():
    probs = torch.softmax(model(tensor), dim=1)
    # probs[0][0] = Parasitized, probs[0][1] = Uninfected
```

## Links

- **GitHub**: [Svetozar-Technologies/LocalMedScan](https://github.com/Svetozar-Technologies/LocalMedScan)
- **PlasmoSENet Model**: [Svetozar1993/LocalMedScan-malaria-plasmosenet](https://huggingface.co/Svetozar1993/LocalMedScan-malaria-plasmosenet)
- **Dataset**: [NIH Malaria Cell Images](https://lhncbc.nlm.nih.gov/LHC-downloads/downloads.html#malaria-datasets)