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Early diagnosis of lung cancer using deep learning and uncertainty measures

Yıl 2024, Cilt: 39 Sayı: 1, 385 - 400, 21.08.2023

Sema Üzülmez , Mehmet Akif Çifçi

https://doi.org/10.17341/gazimmfd.1094154
Cited By: 3

Öz

Kaynakça

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Derin öğrenme ve belirsizlik ölçeği ile akciğer kanserinin erken teşhisi

Yıl 2024, Cilt: 39 Sayı: 1, 385 - 400, 21.08.2023

Sema Üzülmez , Mehmet Akif Çifçi

https://doi.org/10.17341/gazimmfd.1094154
Cited By: 3

Öz

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.

Anahtar Kelimeler

Bilgisayarlı Tomografi (BT) Akciğer kanseri Derin Öğrenme (DÖ) VGG-16 MobileNet v3 AlexNet Inception v4 Evrişimli Sinir Ağları (ESA) Bilgisayarlı Tomografi (BT), Akciğer kanseri, Derin Öğrenme (DÖ), VGG-16, MobileNet v3, AlexNet, Inception v4, Evrişimli Sinir Ağları (ESA)

Kaynakça

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  • [11] L. Ebner et al., “Lung nodule detection by microdose CT versus chest radiography (standard and dual-energy subtracted), ” Am. J. Roentgenol., vol. 204, no. 4, pp. 727–735, 2015, doi: 10.2214/AJR.14.12921.
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  • [16] Y. Wang, “Interactive Machine Learning with Applications in Health Informatics, ” 2018.
  • [17] Q. Z. Song, L. Zhao, X. K. Luo, and X. C. Dou, “Using Deep Learning for Classification of Lung Nodules on Computed Tomography Images, ” J. Healthc. Eng., vol. 2017, 2017, doi: 10.1155/2017/8314740.
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  • [21] W. Alakwaa, M. Nassef, and A. Badr, “Lung cancer detection and classification with 3D convolutional neural network (3D-CNN), ” Int. J. Biol. Biomed. Eng., vol. 11, no. November, pp. 66–73, 2017, doi: 10.14569/ijacsa.2017.080853.
  • [22] Q. Z. Song, L. Zhao, X. K. Luo, and X. C. Dou, “Using Deep Learning for Classification of Lung Nodules on Computed Tomography Images, ” J. Healthc. Eng., 2017, doi: 10.1155/2017/8314740.
  • [23] T. Pandiangan, I. Bali, and A. R. J. Silalahi, “Early lung cancer detection using artificial neural network, ” Atom Indones., 2019, doi: 10.17146/aij.2019.860.
  • [24] T. Heeneman and M. Business Analytics, “Lung nodule detection by using Deep Learning, ” no. January, 2018.
  • [25] A. M. Mesleh, “Lung cancer detection using multi-layer neural networks with independent component analysis: A comparative study of training algorithms, ” Jordan J. Biol. Sci., 2017.
  • [26] R. Dey, Z. Lu, and Y. Hong, “Diagnostic classification of lung nodules using 3D neural networks, ” Proc. - Int. Symp. Biomed. Imaging, vol. 2018-April, pp. 774–778, 2018, doi: 10.1109/ISBI.2018.8363687.
  • [27] S. Li et al., “Predicting lung nodule malignancies by combining deep convolutional neural network and handcrafted features, ” Phys. Med. Biol., 2019, doi: 10.1088/1361-6560/ab326a.
  • [28] S. Shen, S. X. Han, D. R. Aberle, A. A. Bui, and W. Hsu, “An interpretable deep hierarchical semantic convolutional neural network for lung nodule malignancy classification, ” Expert Syst. Appl., 2019, doi: 10.1016/j.eswa.2019.01.048.
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  • [30] 6(1). http://doi.org/10.1186/s13613-015-0104-6 Deye, N., Vincent, F., Michel, P., Ehrmann, S., Da Silva, D., Piagnerelli, M., … Laterre, P.-F. (2016). Changes in cardiac arrest patientsâ€TM temperature management after the 2013 “TTM” trial: Results from an international survey. Annals of Intensive et al., “Understanding knowledge and attitudes about breast cancer and its treatment in Ethiopia, ” Ann. Glob. Heal., 2015.
  • [31] S. Engineering, “DEEP NEURAL NETWORKS FOR HUMAN MOTION ANALYSIS IN BIOMECHANICS APPLICATIONS by Deep Neural Networks for Human Motion Analysis in Biomechanics Applications By RAHIL MEHRIZI Dissertation Director : Kang Li, ” 2019.
  • [32] H. Polat and H. D. Mehr, “Classification of pulmonary CT images by using hybrid 3D-deep convolutional neural network architecture, ” Appl. Sci., vol. 9, no. 5, 2019, doi: 10.3390/app9050940.
  • [33] E. Matsuyama and D.-Y. Tsai, “Automated Classification of Lung Diseases in Computed Tomography Images Using a Wavelet Based Convolutional Neural Network, ” J. Biomed. Sci. Eng., vol. 11, no. 10, pp. 263–274, 2018, doi: 10.4236/jbise.2018.1110022.
  • [34] D. Zhang, L. Zou, X. Zhou, and F. He, “Integrating Feature Selection and Feature Extraction Methods with Deep Learning to Predict Clinical Outcome of Breast Cancer, ” IEEE Access, 2018, doi: 10.1109/ACCESS.2018.2837654.
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  • [36] 6(1). http://doi.org/10.1186/s13613-015-0104-6 Deye, N., Vincent, F., Michel, P., Ehrmann, S., Da Silva, D., Piagnerelli, M., … Laterre, P.-F. (2016). Changes in cardiac arrest patientsâ€TM temperature management after the 2013 “TTM” trial: Results from an international survey. Annals of Intensive et al., “Sampling patient demographics and treatment modalities using the iPad application ‘EMcounter’ in Mazabuka, Zambia, ” Ann. Glob. Heal., 2015.
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Toplam 57 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Sema Üzülmez 0000-0003-3181-3226

Mehmet Akif Çifçi 0000-0002-6439-8826

Erken Görünüm Tarihi 10 Ağustos 2023
Yayımlanma Tarihi 21 Ağustos 2023
Gönderilme Tarihi 27 Mart 2022
Kabul Tarihi 26 Şubat 2023
Yayımlandığı Sayı Yıl 2024 Cilt: 39 Sayı: 1

Kaynak Göster

APA Üzülmez, S., & Çifçi, M. A. (2023). Derin öğrenme ve belirsizlik ölçeği ile akciğer kanserinin erken teşhisi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 39(1), 385-400. https://doi.org/10.17341/gazimmfd.1094154
AMA Üzülmez S, Çifçi MA. Derin öğrenme ve belirsizlik ölçeği ile akciğer kanserinin erken teşhisi. GUMMFD. Ağustos 2023;39(1):385-400. doi:10.17341/gazimmfd.1094154
Chicago Üzülmez, Sema, ve Mehmet Akif Çifçi. “Derin öğrenme Ve Belirsizlik ölçeği Ile akciğer Kanserinin Erken teşhisi”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 39, sy. 1 (Ağustos 2023): 385-400. https://doi.org/10.17341/gazimmfd.1094154.
EndNote Üzülmez S, Çifçi MA (01 Ağustos 2023) Derin öğrenme ve belirsizlik ölçeği ile akciğer kanserinin erken teşhisi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 39 1 385–400.
IEEE S. Üzülmez ve M. A. Çifçi, “Derin öğrenme ve belirsizlik ölçeği ile akciğer kanserinin erken teşhisi”, GUMMFD, c. 39, sy. 1, ss. 385–400, 2023, doi: 10.17341/gazimmfd.1094154.
ISNAD Üzülmez, Sema - Çifçi, Mehmet Akif. “Derin öğrenme Ve Belirsizlik ölçeği Ile akciğer Kanserinin Erken teşhisi”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 39/1 (Ağustos 2023), 385-400. https://doi.org/10.17341/gazimmfd.1094154.
JAMA Üzülmez S, Çifçi MA. Derin öğrenme ve belirsizlik ölçeği ile akciğer kanserinin erken teşhisi. GUMMFD. 2023;39:385–400.
MLA Üzülmez, Sema ve Mehmet Akif Çifçi. “Derin öğrenme Ve Belirsizlik ölçeği Ile akciğer Kanserinin Erken teşhisi”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, c. 39, sy. 1, 2023, ss. 385-00, doi:10.17341/gazimmfd.1094154.
Vancouver Üzülmez S, Çifçi MA. Derin öğrenme ve belirsizlik ölçeği ile akciğer kanserinin erken teşhisi. GUMMFD. 2023;39(1):385-400.

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