Akciğer Görüntülerinden Tümörlü Verilerin Derin Sinir Ağları ve Evrişimsel Sinir Ağları ile Tahmini

Year 2024, Volume: 7 Issue: 1, 23 - 36

Volkan Çetin Çiğdem Bakır

https://doi.org/10.53448/akuumubd.1431051

Abstract

Akciğer tümörleri günümüzde sıklıkla görülür ve yaygın bir şekilde insanlarda ölümlere neden olan tehlikeli bir hastalıktır. Ancak çoğu zaman uzmanlar tarafından yapılan manuel tetkikler yanlış teşhise sebep verebilir. Bunun yerine bilgisayar destekli otomatik, doğru ve ayrıntılı yapılan erken kanser teşhisine ihtiyaç bulunmaktadır. Bu sebeple bu çalışmada akciğer hastalıkları ile yapılan çalışmalar ayrıntılı bir şekilde incelenmiştir. Çalışmanın ilk aşamasında 1190 akciğer tomografi görüntüsü önerilen derin öğrenme modelleri için hazırlanmıştır. İkinci aşamasında ise derin öğrenme modellerinden Evrişimsel Sinir Ağı (Convolutional Neural Network – CNN) ve Derin Sinir Ağları ( Deep Neural Network – DNN) kullanılarak akciğer tümörleri ile normal akciğer görüntülerinin tespiti gerçekleştirilmiştir. Kullanılan her modelin doğruluğu duyarlılık, kesinlik ve F1-Skor gibi farklı değerlendirme metrikleri ile hesaplanmış ve sonuçlar karşılaştırılmıştır. Ayrıca her model için performans analizleri yapılmış ve eğitim, test ve valid görüntüleri için karmaşıklık matrisleri ile ROC analizleri sunulmuştur.

Keywords

akciğer kanseri, derin sinir ağları, derin öğrenme, BT, teşhis

Prediction of Tumor Data from Lung Images with Deep Neural Networks and Convolutional Neural Networks

Abstract

Lung tumors are common today and are a dangerous disease that commonly causes death in people. However, manual examinations performed by experts can often lead to incorrect diagnosis. Instead, computer-assisted, automatic, accurate and detailed early cancer diagnosis is needed. For this reason, studies on lung diseases were examined in detail in this study. In the first stage of the study, 1190 lung tomography images were prepared for the proposed deep learning models. In the second stage, lung tumors and normal lung images were detected by using Convolutional Neural Network (CNN) and Deep Neural Network (DNN), which are deep learning models. The accuracy of each model used was calculated with different evaluation metrics such as sensitivity, precision and F1-Score, and the results were compared. In addition, performance analyzes were performed for each model, and complexity matrices and ROC analyzes were presented for training, testing and valid images.

Keywords

lung cancer, deep neural network, deep learning, CT, diagnosis

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