Prediction of Covid-19 and Pneumonia Diseases from Lung X-ray Images Using Quantum Convolutional Neural Networks

Yıl 2024, Cilt: 14 Sayı: 2, 37 - 51, 23.07.2024

Seçmen Şahin , Güneş Harman

Öz

Quantum Convolutional Neural Networks (QCNNs) aim to enhance the capabilities of convolutional neural networks by leveraging the strengths of quantum computing. They operate by locally transforming input data using quantum circuits. In this study, two models have been built on a quantum-encoded COVID-19 dataset. Model-1 classifies between ‘Normal Person’ and ‘Covid19/Viral Pneumonia’, while Model-2 classifies between ‘Covid-19’ and ‘Viral Pneumonia’. Three different classifications have been made based on the number of qubits (feature count) for these models. For Quantum Classifier 1, approximately 70% accuracy was achieved by extracting 11 features from the 256-feature input data obtained through basic data analysis. In Quantum Classifier 2, using the TruncatedSVD method, each image’s 256 features were reduced to 4, resulting in 72% accuracy. Finally, Quantum Classifier 3 achieved an unexpected 76% accuracy using only 2 features. These models provide significant insights into diagnosing diseases from lung X- Ray images and demonstrate how quantum computers can be effectively utilized in the healthcare domain. Additionally, the impact of different parameters in the “default qubit” device of Pennylane on model performance has been investigated. The study highlights how Quantum Classifier 3 achieves high accuracy by significantly reducing the data dimension, indicating the potential of QCNNs to provide high performance with less resource usage in the future.

Anahtar Kelimeler

Covid’19, quantum circuit, quantum machine learning, quantum convolutional neural networks

Akciğer Röntgen Görüntülerinden Covid-19 ve Zatürre Hastalığının Kuantum Evrişimli Sinir Ağları Yöntemi ile Tahmini

Öz

Kuantum Evrişimli Sinir Ağları (QCNN’ler) kuantum hesaplamanın güçlü yönlerinden faydalanarak evrişimli sinir ağlarının yeteneklerini artırmayı amaçlar. Girdi verilerini yerel olarak dönüştürerek ve kuantum devreleri kullanarak çalışırlar. Bu çalışmada, kuantum kodlu bir COVID-19 veri kümesi üzerinde iki model oluşturulmuştur. Model-1, ‘Normal Kişi’ ile ‘Covid19/Viral Pnömoni’ arasında sınıflandırma yaparken, Model-2 ‘Covid-19’ ile ‘Viral Pnömoni’ arasında sınıflandırma yapmaktadır. Oluşturulan bu modeller için kübit sayısına göre (öznitelik sayısı) 3 farklı sınıflandırma yapılmıştır. Kuantum Sınıflandırıcı 1 için, temel veri analizi ile elde edilen 256 özellikli giriş verisinden 11 özellik çıkarılarak yaklaşık %70 doğruluk elde edilmiştir. Kuantum Sınıflandırıcı 2’de, TruncatedSVD yöntemi kullanılarak her bir görüntünün 256 özelliği 4’e indirilmiş ve %72 doğruluk elde edilmiştir. Son olarak Kuantum Sınıflandırıcı 3’te sadece 2 özellik kullanarak beklenmedik bir şekilde %76 doğruluk elde ettiği belirtilmiştir Bu modeller, akciğer röntgen görüntülerinden hastalık teşhisi konusunda önemli bilgiler sağlamakta ve kuantum bilgisayarlarının sağlık alanında nasıl etkili bir şekilde kullanılabileceğini göstermektedir. Ayrıca, Pennylane’in “varsayılan qubit” cihazındaki farklı parametrelerin model performansına etkisi incelenmiştir. Çalışmada, Kuantum Sınıflandırıcı 3’ün veri boyutunu önemli ölçüde azaltarak yüksek doğruluk oranına nasıl ulaştığı, QCNN’lerin gelecekte daha az kaynak kullanımıyla yüksek performans sağlama potansiyelini göstermektedir.

Anahtar Kelimeler

Covid’19, kuantum devresi, kuantum makine öğrenmesi, kuantum evrişimli sinir ağları

Kaynakça

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