Work

MalariaNet: A Microcontroller-Deployable Malaria-Microscopy Detector for Point-of-Care Biosensing Under Leakage-Free Evaluation

Year: 2026

Type: article

Abstract: Compact malaria detectors for microcontrollers are almost always benchmarked on the NIH Malaria dataset with a per-cell random split. This leaks slide identity, because the cells come from only about 200 slides and a random split mixes same-slide cells across training and test. Under a leakage-free slide-disjoint protocol, the leakage fabricates architectural conclusions. Per-module ablation gains collapse to seed noise, and an apparent cross-site robustness variant loses most of its advantage. Headline accuracy falls from 97.1% to 95.6%, a gap that sits within the cross-seed noise, and all eight tested architectures move the same way. The evidence is this unanimous direction, not the size of any single gap. This self-critical finding is our main contribution. Two results still survive. First, MalariaNet, our 21K-parameter detector, reaches about 95.6% accuracy at 23.5KB of INT8 weights, with a numerically faithful on-chip forward on an STM32H743 at a 1.2FPS triage rate. Second, it is the second most interference-robust of the eight networks and the most robust microcontroller-deployable model. Scope is limited to P. falciparum thin-smear single cells. Slide-disjoint evaluation should become standard, and we provide MalariaNet as the first leakage-free, on-device-validated point-of-care malaria reference.

Source: Biosensors

DOI: 10.3390/bios16070358

Authors: Mengdi Hou, Gaoming He, Zongchang Liu, Jianbo Huang, Heliang Zou

Institutions: Wuzhou University, Guilin University of Technology

Language: English

Topic: Digital Imaging for Blood Diseases

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,654