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Derin Öğrenme Metodu ve Ayrık Dalgacık Dönüşümü Kullanarak BT Görüntülerinden Akciğer Kanseri Teşhisi

Year 2022, Volume: 4 Issue: 2, 141 - 154, 26.10.2022
https://doi.org/10.46387/bjesr.1114243

Abstract

Derin Öğrenmenin (DÖ) teknikleriyle erken kanser tanısı son dönemlerde araştırmacılar arasında en çok üzerinde durulan konu olmuştur. Ayrıca pek çok araştırmada görüldüğü üzere DÖ’nın tıp alanında kullanımı günümüzde daha da önem kazanmaktadır. Araştırmacılar sağlık alanında çoğunlukla kanser ve kanser türleri teşhis ederken DÖ tekniklerinden yararlanmaktadır. Bunun başlıca nedeni akciğer kanserinden ölüm riskinin yüksek olmasıdır. Bu tür hastalıkların tanısında BT görüntülerinin net olmamasından dolayı, doğru karar vermede uzmanlar görüş ayrılıkları yaşamaktadır. Bu ve benzeri hastalıkları erken ve doğru tanılayabilen ve daha güvenilir sonuçlar verebilen DÖ karar verme mekanizmaları bir seçenek haline gelmiştir. Yapılan araştırmalara göre akciğer kanseri, dünya çapında ölümlerin en önde gelen nedenleri arasındadır. Akciğer kanseri sadece 2019 yılında tahmini 1,76 milyon insanın ölümden sorumludur. Sebepleri artıkça (ortalama aile öyküsü, sigara, yüksek tansiyon ve diğer popüler tıbbi nedenler) ölüm oranı ortalaması %80'in üzerinde arttığı gözlemlenmiştir. Olgular erken tanı konup, tedavi edilirse kanser kaynaklı ölümlerin oranının azalmakta olduğu görülmüştür. Hastalığın doğru saptanması tedavi edilmesinde önemli rol oynamaktadır.
Bu çalışmada Ayrık Dalgacık Dönüşümü (ADD) yaklaşımı ile DÖ tekniği birleştirilerek, 6053 akciğer tomografi veri seti (veri kaynağı, yaş grubu, coğrafi bölge vb. kısa bilgi) üzerinde işlem yapılmıştır. Hastanın kanser olup olmadığı, kanser olduğu takdirde ise bunun iyi huylu (benign) ya da kötü huylu (malign) olduğuna karar verilmesine çalışılmaktadır. Bilgisayarlı Tomografi (BT), görüntülerde öncelikle görüntü işleme aşamalarının yanı sıra ADD ile öznitelik çıkarımı yapılıp elde edilen veriler DÖ ’ya girdi verisi olarak kullanılır. Bu çalışmada iki metot önerilmiştir. Birinci yöntemde VGG-16, Inception v4, MobileNet v3 kullanılırken ikinci yöntemde AlexNet yöntemi uygulanmaktadır. Bu yöntem hem ADD kullanımı hem de iki aşamalı olması yönüyle yaygın kullanılan diğer tekniklerden farklıdır. Deneysel sonuçların yüksek performans gösterdiğini ve AlexNet’in %99, 86, MobileNet v3’ün %98,00, VGG-16 %95,50, Inception v4’ün ise %96,03 doğrulukta sonuç verdiği belirlenmiştir. Böylece akciğer hastalıklarının BT görüntülerinde kanser olup olmadığı, kanser ise hangi aşamada olduğu konusunda ön bilgi elde edilebilmektedir.

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Year 2022, Volume: 4 Issue: 2, 141 - 154, 26.10.2022
https://doi.org/10.46387/bjesr.1114243

Abstract

References

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Details

Primary Language Turkish
Subjects Computer Software
Journal Section Research Articles
Authors

Mehmet Akif Cifci 0000-0002-6439-8826

Publication Date October 26, 2022
Published in Issue Year 2022 Volume: 4 Issue: 2

Cite

APA Cifci, M. A. (2022). Derin Öğrenme Metodu ve Ayrık Dalgacık Dönüşümü Kullanarak BT Görüntülerinden Akciğer Kanseri Teşhisi. Mühendislik Bilimleri Ve Araştırmaları Dergisi, 4(2), 141-154. https://doi.org/10.46387/bjesr.1114243
AMA Cifci MA. Derin Öğrenme Metodu ve Ayrık Dalgacık Dönüşümü Kullanarak BT Görüntülerinden Akciğer Kanseri Teşhisi. BJESR. October 2022;4(2):141-154. doi:10.46387/bjesr.1114243
Chicago Cifci, Mehmet Akif. “Derin Öğrenme Metodu Ve Ayrık Dalgacık Dönüşümü Kullanarak BT Görüntülerinden Akciğer Kanseri Teşhisi”. Mühendislik Bilimleri Ve Araştırmaları Dergisi 4, no. 2 (October 2022): 141-54. https://doi.org/10.46387/bjesr.1114243.
EndNote Cifci MA (October 1, 2022) Derin Öğrenme Metodu ve Ayrık Dalgacık Dönüşümü Kullanarak BT Görüntülerinden Akciğer Kanseri Teşhisi. Mühendislik Bilimleri ve Araştırmaları Dergisi 4 2 141–154.
IEEE M. A. Cifci, “Derin Öğrenme Metodu ve Ayrık Dalgacık Dönüşümü Kullanarak BT Görüntülerinden Akciğer Kanseri Teşhisi”, BJESR, vol. 4, no. 2, pp. 141–154, 2022, doi: 10.46387/bjesr.1114243.
ISNAD Cifci, Mehmet Akif. “Derin Öğrenme Metodu Ve Ayrık Dalgacık Dönüşümü Kullanarak BT Görüntülerinden Akciğer Kanseri Teşhisi”. Mühendislik Bilimleri ve Araştırmaları Dergisi 4/2 (October 2022), 141-154. https://doi.org/10.46387/bjesr.1114243.
JAMA Cifci MA. Derin Öğrenme Metodu ve Ayrık Dalgacık Dönüşümü Kullanarak BT Görüntülerinden Akciğer Kanseri Teşhisi. BJESR. 2022;4:141–154.
MLA Cifci, Mehmet Akif. “Derin Öğrenme Metodu Ve Ayrık Dalgacık Dönüşümü Kullanarak BT Görüntülerinden Akciğer Kanseri Teşhisi”. Mühendislik Bilimleri Ve Araştırmaları Dergisi, vol. 4, no. 2, 2022, pp. 141-54, doi:10.46387/bjesr.1114243.
Vancouver Cifci MA. Derin Öğrenme Metodu ve Ayrık Dalgacık Dönüşümü Kullanarak BT Görüntülerinden Akciğer Kanseri Teşhisi. BJESR. 2022;4(2):141-54.