hybrid-qcnn-medical-diagnostics

name	hybrid-qcnn-medical-diagnostics
description	Hybrid classical-quantum diagnostic framework using QCNNs for multi-class medical image classification
version	1.0
source	arXiv:2511.12386
arxiv_id	2511.12386
authors	Shabnam Sodagari, Tommy Long
published	2025-11-15
categories	cs.CV
created	2026-07-08
trigger_words	["quantum diagnostics","QCNN","quantum medical","quantum classification","medical image quantum","hybrid quantum medical","quantum convolutional neural network"]

Hybrid QCNN Medical Diagnostics

Overview

Hybrid classical-quantum diagnostic framework for multi-class medical image classification. Uses pretrained classical encoders to extract features, then embeds them into quantum states processed by Quantum Convolutional Neural Networks (QCNNs).

Paper: "Leveraging Quantum-Based Architectures for Robust Diagnostics" (arXiv:2511.12386)

Results

• Kidney CT classification: 99% accuracy
• Cervical cell (pap smear) classification: 97% accuracy
• Brain tumor (MRI) classification: 99% accuracy
• Consistently outperforms classical CNN baselines with fewer trainable parameters

Architecture Pattern

1. Medical Image -> Pretrained Encoder -> Quantum Encoding (Angle/Amplitude) -> QCNN -> Classification

Three-Stage Pipeline

1. Preprocessing: Dataset-specific preprocessing and transfer learning
2. Feature Extraction: Pretrained encoder extracts latent features from medical images
3. Quantum Processing: Features embedded into quantum states via angle or amplitude encoding, processed by QCNN

Encoding Strategies

• Angle Encoding: Maps features to rotation angles of qubits
• Amplitude Encoding: Maps features to amplitudes of quantum state vectors

QCNN Design

• Quantum convolutional layers for hierarchical feature extraction
• Quantum pooling for dimensionality reduction
• Measurement-based classification output

Implementation Notes

• Hybrid models achieve strong and stable convergence across diverse medical imaging tasks
• Fewer trainable parameters than classical CNN baselines
• Angle encoding works well for lower-dimensional feature spaces
• Amplitude encoding suitable for higher-dimensional representations (requires log(N) qubits for N features)

Key Insights

1. Quantum-enhanced architectures show promise for medical diagnostics with compact models
2. Transfer learning + quantum processing is an effective hybrid strategy
3. QCNNs generalize across multiple medical imaging modalities (CT, MRI, microscopy)
4. Quantum models can match or exceed classical performance with fewer parameters

When to Use

• Multi-class medical image classification tasks
• Settings requiring compact, expressive models
• Hybrid classical-quantum pipeline design