An Effective Image Augmenting Technique in Detection of Lung Cancer Types

Year 2022, Volume: IDAP-2022 : International Artificial Intelligence and Data Processing Symposium , 12 - 20, 10.10.2022

Berna Arı , Ömer Faruk Alçin , Abdülkadir Şengür

https://doi.org/10.53070/bbd.1173074

Abstract

In recent years, the high performance of deep learning architectures on classification and prediction has increased the interest in these areas. The importance and sharing of data sets has come to the fore with the widespread use of computer-based decision support systems in the diagnosis of disease, especially in medical fields. However, the fact that the generated data sets are not sufficient for deep architectures can be a problem in terms of classification performance. Increasing the amount of data is often not possible because it is costly, time consuming and the relevant specialist is not always available. The mentioned situations necessitated the introduction of data augmenting methods and tending to this area. In this study, an Extreme Learning Machine Auto-Encoder (W-ELM-AE) based data augmentation method with Wavelet activation function is proposed. The proposed method has been tested on the lung cancer classification, which includes the largest percentage of cancer rates in the world. The augmented training dataset is applied as an input to the GoogLeNet architecture. The performance of W-ELM-AE has been compared with non-augmented and traditional augmenting methods. The proposed method shows a higher performance of 11.12% compared to the unaugmented case, and 2.55% higher than the dataset augmented with classical methods.

Keywords

data augmentation, oto-encoder, extreme learning machine, lung cancer, wavelet function

Akciğer Kanser Tipi Tespitinde Etkili Bir Görüntü Çoğullama Tekniği

Abstract

Son yıllarda derin öğrenme mimarilerinin sınıflama ve tahmin üzerine yüksek başarımlara sahip olması bu alanlara ilgiyi artırmıştır. Özellikle medikal alanlarda hastalık tanısında bilgisayar tabanlı karar destek sistemlerinin yaygınlaşması ile veri setlerinin önemi ve paylaşılması da ön plana çıkmıştır. Ancak oluşturulan veri setlerinin derin mimariler için yeterli veri sayısına sahip olmaması sınıflama performansı açısından sorun olabilmektedir. Veri miktarının artırılması ise çoğu zaman maliyetli, zaman alıcı ve ilgili uzmanın her zaman bulunamaması sebebiyle mümkün olamamaktadır. Bahsedilen durumlar veri çoğullama yöntemlerinin devreye girmesini ve bu alana yönelmeyi gerektirmiştir. Bu çalışmada Dalgacık aktivasyon fonksiyonlu Aşırı Öğrenme Makinası Oto Kodlayıcı (D-AÖM-OK) tabanlı veri artırma yöntemi önerilmiştir. Önerilen yöntem dünyadaki kanser oranının en büyük yüzdesini içeren akciğer kanser sınıflaması üzerinde test edilmiştir. Çoğullanan eğitim veri seti GoogLeNet mimarisine giriş olarak uygulanmıştır. D-AÖM-OK’ın performansı çoğullanmamış ve geleneksel çoğullama yöntemleri ile karşılaştırılmıştır. Önerilen yöntem çoğullanmamış duruma kıyasla %11,12, klasik yöntemlerle çoğullanmış veri setine göre ise %2,55 oranında daha yüksek başarım göstermektedir.

Keywords

veri çoğullama, otomatik kodlayıcı, aşırı öğrenme makineleri, akciğer kanseri, dalgacık fonksiyonu

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