Deep Learning for Diagnosis of Lung Cancer from CT Images

Year 2022, Volume: 24 Issue: 71, 487 - 500, 16.05.2022

Mehmet Cifci

https://doi.org/10.21205/deufmd.2022247114

Cited By: 2

Abstract

Early cancer diagnosis with Deep Learning (DL) techniques has been the most emphasized subject among researchers recently. In addition, as seen in many studies, the use of DL in the field of medicine is gaining more importance today. Researchers generally use DL techniques to diagnose cancer and cancer types in the health field. In the diagnosis of lung cancer, due to the inconsistency of Computed Tomography (CT) images, experts have disagreements in making the right decision. DL decision-making mechanisms that can diagnose these and similar diseases early and accurately and provide more reliable results have become an option. Studies show that lung cancer is among the leading causes of death worldwide. Lung cancer caused an estimated 1.76 million deaths in 2019. It has been observed that as the causes of lung cancer increase, the average mortality rate increases by more than 80%. It has been remarked that the rate of cancer-related deaths decreases if the cases are diagnosed and treated early. Accurate detection of the disease plays an important role in its treatment.
In this study, the 6053 lung CT data set was processed with the DL techniques. It is tried to decide whether the patient has cancer and if it is cancer, it is benign or malignant. In the lung CT dataset, after the image processing stages, feature extraction is performed, and the data obtained are used as input data in DL. In this study, two methods are proposed: VGG-16, Inception v4, MobileNet v3 are used in the first method, while the AlexNet method is used in the second method. This study differs from other commonly used techniques in that it has two stages. It was determined that the experimental results showed high performance and AlexNet gave 0.96 accuracies, MobileNet v3 0.81, VGG-16 0.84, Inception v4 0.86 accuracies. Thus, preliminary information can be obtained about whether there is cancer in the CT images of lung patients, and if it is cancer, at what stage the disease is.

Keywords

Computed Tomography (CT), Lung cancer, Deep Learning (DL), VGG-16, MobileNet v3, AlexNet, Inception v4, Convolutional Neural Networks (CNN)

Derin Öğrenme Metodu Kullanarak BT Görüntülerinden Akciğer Kanseri Teşhisi

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’nün tıp alanında kullanımı günümüzde daha da önem kazanmaktadır. Araştırmacılar sağlık alanında kanser ve kanser türlerini teşhis etmede genellikle DÖ tekniklerinden yararlanmaktadır. Akciğer kanseri tanısında Bilgisayarlı Tomografi (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 çapındaki ölümlerin önde gelen nedenleri arasındadır. Akciğer kanseri, sadece 2019 yılında tahmini 1,76 milyon insanın ölümüne sebep olmuştur. Akciğer kanserinin sebepleri arttıkça bu hastalıktan ölüm oranının %80'in üzerine çıktığı gözlemlenmiştir. Olgular erken tanı konup, tedavi edilirse kanser kaynaklı ölümlerin oranının azalmakta olduğu görülmüştür. Hastalığın doğru saptanması, tedavi edilmesinde önemli rol oynamaktadır.
Bu çalışmada DÖ tekniği ile, 6053 akciğer tomografi veri seti üzerinde işlem yapılmıştır. Hastanın kanser olup olmadığına, kanser ise bunun iyi huylu (benign) ya da kötü huylu (malign) olduğuna karar verilmesine çalışılmaktadır. Akciğer BT veri kümesinde görüntü işleme aşamalarının ardından öznitelik çıkarımı yapılıp elde edilen veriler DÖ ’de girdi verisi olarak kullanılmaktadır. Bu çalışmada iki metot önerilmiştir: Birinci yöntemde VGG-16, Inception v4, MobileNet v3 kullanılırken ikinci yöntemde AlexNet yöntemi uygulanmaktadır. İki farklı aşamanın sebebi verinin farklı oranlarda bölünmesidir. Bu çalışma, iki aşamalı olması yönüyle yaygın kullanılan diğer tekniklerden farklıdır. Deneysel sonuçların yüksek performans gösterdiği ve AlexNet’in 0.96, MobileNet v3’ün 0.81, VGG-16 0.84, Inception v4’ün ise 0.86 doğrulukta sonuç verdiği belirlenmiştir. Böylece akciğer hastalarının BT görüntülerinde kanser olup olmadığı, kanser ise hastalığın hangi aşamada olduğu konusunda ön bilgi elde edilebilmektedir.

Keywords

Bilgisayarlı Tomografi, Akciğer kanseri, Derin Öğrenme, VGG-16, MobileNet v3, AlexNet, Inception v4, Evrişimli Sinir Ağları

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