Rust-Cameroon/malaria-model

Malaria Blood smear detector # Malaria Detection CNN (Rust + Burn) > **AI system for malaria detection from blood smear images** > Implemented in Rust (Burn) with an Axum inference API and a Yew web UI. ## Overview Get dataset here dataset and add it to project root at `/data` if you want to run the training. This model predicts: - **Infected vs Uninfected** (gating) - **Species**: Falciparum, Malariae, Ovale, Vivax (plus an internal Uninfected class) - **Stage presence** (multi-label): Ring (R), Trophozoite (T), Schizont (S), Gametocyte (G) Stage labels are weak (image-level presence inferred from filename tokens) and are treated as presence probabilities. ## Repository Layout - `src/bin/mpidb_prep.rs`: crop generation + `manifest.csv` writer - `src/training.rs`: training entry point - `src/bin/server.rs`: inference API (Axum) - `inference-ui/`: web UI (Yew) - `DEV_GUIDE.md`: detailed crop/manifest strategy ## Requirements - Rust toolchain - UI: `trunk` + target `wasm32-unknown-unknown` ## Data Preparation (Crops + Manifest) This project trains from a CSV manifest generated by `mpidb_prep`. Expected input folders: 1) **MP-IDB species dataset** (infected) (one folder per species): ```text data/ ├── Falciparum/ │ ├── img/... │ └── gt/... ├── Malariae/ │ ├── img/... │ └── gt/... ├── Ovale/ │ ├── img/... │ └── gt/... └── Vivax/ ├── img/... └── gt/... ``` 2) **Uninfected negatives**: ```text data/ └── Uninfected/ ├── cell_1.png ├── cell_2.png └── ... ``` Generate crops + `manifest.csv`: ```bash cargo run --bin mpidb_prep -- data mpidb_crops 128 25 ``` Outputs: - `mpidb_crops/ /*.png` (infected crops) - `mpidb_crops/Uninfected/*.png` (uninfected crops) - `mpidb_crops/manifest.csv` Manifest schema: ```text crop_path,infected,species,stage_r,stage_t,stage_s,stage_g,source_image_id ``` For a detailed explanation of the cropping strategy and leakage-safe splitting, see: - `DEV_GUIDE.md` ## Training ```bash cargo run --release ``` Training reads the manifes …