LocalMedScan-malaria-mobilenetv2

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.

Model Details

Performance

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

Comparison with PlasmoSENet

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

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
• PlasmoSENet Model: Svetozar1993/LocalMedScan-malaria-plasmosenet
• Dataset: NIH Malaria Cell Images