Technology

Models & architecture

Explore the models behind each phase of the pipeline — from the 7-model ensemble to the 11-class object detector.

Phase 1 models

Phase 2 classifier

Phase 3 classes

Phase 1 — Fecal detection ensemble

Seven TensorFlow / Keras architectures, each fine-tuned independently on the same fecal-detection dataset. Their outputs are combined through majority voting to produce a single fecal vs non-fecal decision.

VGG19

Deep CNN

19 weight layers, well-studied baseline for transfer learning with strong feature extraction.

ResNet50

Residual Network

50-layer residual network with skip connections that prevent vanishing gradients.

DenseNet169

Dense Blocks

169-layer network where every layer is connected to every other, maximizing feature reuse.

EfficientNetB0

Compound Scaling

Balanced depth, width, and resolution scaling for best accuracy-per-FLOP.

MobileNetV2

Lightweight CNN

Inverted residuals and linear bottlenecks — optimized for mobile and edge deployment.

NASNetMobile

NAS-optimized

Architecture discovered via neural architecture search, tuned for mobile-scale compute.

ConvNeXtBase

Modern CNN

A pure ConvNet that matches vision transformers by modernizing classic design choices.

View on Hugging Face

Phase 2 — Helminth screening

Binary classifier — Helminths vs Non-Helminths

A single dedicated model receives confirmed fecal slides and determines whether parasitic helminth eggs or organisms are present. This binary gate prevents the heavier object-detection model from running on clean samples, improving both speed and specificity.

Phase 3 — Species identification

11-class object detection with bounding boxes

Helminth-positive slides are scanned by an object-detection model trained to localize and classify eggs or organisms from 11 parasitic species. Each detection includes a bounding box drawn directly on the microscopy image, along with a species label and confidence score.

Detectable species

Ascaris lumbricoidesCapillaria philippinensisEnterobius vermicularisFasciolopsis buskiHookworm eggHymenolepis diminutaHymenolepis nanaOpisthorchis viverrineParagonimus sppTaenia spp. eggTrichuris trichiura

Interactive diagram

How ensemble voting works

Each Phase-1 model independently votes. A simple majority (≥4 of 7) determines the outcome.

VGG19Fecal

ResNet50Fecal

DenseNet169Fecal

EfficientNetB0Fecal

MobileNetV2Fecal

NASNetMobileNon-fecal

ConvNeXtBaseNon-fecal

Majority vote: Fecal

5 of 7 models agree · Advance to Phase 2

See the pipeline in action

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