cerviscan-b0

CerviScan AI — EfficientNet-B0 Cervical Cytology Classifier

5-class Pap smear cell classifier (EfficientNet-B0, timm backbone + custom head, ~4.3M parameters) trained on the SIPaKMeD dataset for AI-assisted cervical cancer screening in resource-limited settings.

Classes

Dyskeratotic · Koilocytotic · Metaplastic · Parabasal · Superficial-Intermediate

Results

80/20 stratified split · 810 validation images · seed 42

Overall accuracy: 98.40% · macro F1: 0.9840

Binary triage view (Normal vs Abnormal)

The deployment decision is binary — flag for human review or clear:

Of the 13 total 5-class errors, 10 are harmless abnormal-subtype swaps (still routed to human review) and 1 is a false alarm — only 3 abnormal cells escaped as normal. Koilocytotic ↔ Metaplastic is the dominant confusion pair, consistent with published SIPaKMeD benchmarks.

Files

Usage

import numpy as np
import onnxruntime as ort
from PIL import Image

MEAN = np.array([0.485, 0.456, 0.406], dtype=np.float32)
STD  = np.array([0.229, 0.224, 0.225], dtype=np.float32)
CLASSES = ["Dyskeratotic", "Koilocytotic", "Metaplastic", "Parabasal",
           "Superficial-Intermediate"]

img = Image.open("cell.png").convert("RGB").resize((224, 224))
x = (np.asarray(img, dtype=np.float32) / 255.0 - MEAN) / STD
x = x.transpose(2, 0, 1)[None]  # HWC -> CHW, add batch dim

sess = ort.InferenceSession("cervical_b0_fp16.onnx",
                            providers=["CPUExecutionProvider"])
logits = sess.run(None, {sess.get_inputs()[0].name: x})[0]
probs = np.exp(logits) / np.exp(logits).sum()
print(CLASSES[int(probs.argmax())], f"{probs.max():.1%}")

Training details

• Architecture: timm efficientnet_b0 (num_classes=0) + head:Dropout(0.4) → Linear(1280→256) → ReLU → Dropout(0.2) → Linear(256→5)
• Data: SIPaKMeD, 4,049 isolated cell images, 80/20 stratified split
• Augmentation: random horizontal flip, random rotation (±30°), color jitter (0.2)
• Optimization: AdamW (lr 1e-4, weight decay 1e-4), cosine annealing,batch size 32, early stopping (patience 7) on validation accuracy

Limitations & disclaimer

Trained and validated on isolated cells from a single public dataset (SIPaKMeD); performance on whole slides, different scanners, stains, or clinical populations is untested. Research prototype — not a medical device. Class order and normalization statistics are embedded in the checkpoint for verification.

Dataset citation

SIPaKMeD — Pap smear cell images: https://www.kaggle.com/datasets/akshaykrishnan/sipakmed5

• Training notebook: https://www.kaggle.com/ethioel/cervicalcancerdetector