1940 NM FİBER LAZER KAYNAĞININ KARACİĞER DOKUSUNDAKİ ISIL HASARININ YAPAY SİNİR AĞLARI İLE TAHMİNİ

Year 2019, Volume: 24 Issue: 2, 583 - 594, 30.08.2019

Fikret Yıldız

https://doi.org/10.17482/uumfd.410963

Abstract

Bu
çalışmada Yapay Sinir Ağları (YSA) yöntemi kullanılarak 1940 nm dalgaboyuna
sahip lazer kaynağının karaciğer dokusu üzerinde oluşturduğu ısıl hasarların güç
ve uygulama süreleri ile arasındaki ilişkisi incelenmiştir. Farklı güç
değerlerine sahip lazer kaynağı koagülasyon ve karbonizasyon gözlenene kadar dokuya
farklı sürelerde uygulanmıştır. Buna bağlı olarak radyal ve düşey yönde oluşan
ısıl hasarlar deneysel olarak ölçülmüş ve kayıt altına alınmıştır. Bu
kayıtların %70’i Matlab ortamında geliştirilen YSA modellerini eğitmek için
kullanılmıştır. Lazer gücü ve uygulama süreleri model için giriş verileri,
koagülasyon/karbonizasyon oluşma durumu ve oluşan ısıl hasarlar ise (çap,
derinlik) modelin çıkış değerleri olarak kabul edilmiştir. Giriş verileri
kullanılarak beş farklı öğrenme (LM, GDA, GDX, CGP ve BFG) algoritmasının en
küçük kareler değeri (MSE) hesaplanmıştır ve karşılaştırılmıştır. Gizli
katmanında 14 tane nörona sahip GDX, 2-14-3 yapısı, en iyi MSE (7.58E-2)
sonucunu vermiştir ve eğitimde kullanılmayan veriler ile bu algoritmanın tahmin
etme performansını test etmek için kullanılmıştır. Geliştirilen modelin ne
kadar iyi çalıştığını anlamak için YSA tarafından tahmin edilen sonuçlar,
deneysel sonuçlar ile karşılaştırılmıştır. Minimum %2.7 ve  % 3.6 hata oranı ile dokuda oluşan ısıl çap
ve derinliklerinin tahmin edilebileceği gösterilmiştir. Bu sonuçlara göre,
medikal uygulamalarda YSA yönteminin lazere yardımcı bir araç olarak kullanılması,
çevre dokuların korunarak istenilen hedef bölgenin daha kontrollü ve daha
yüksek doğrulukla tedavisini mümkün kılabilir.

Keywords

Yapay Sinir Ağları (YSA), Koagülasyon, Karbonizasyon

Prediction of 1940 nm Fiber Laser Induced Thermal Damage Using Artificial Neural Networks

Abstract

These study
presents relation between power and application time of 1940 nm laser source and
thermal damage occurred on liver tissue using artificial neural networks (ANNs)
method. Laser source with different powers and application times implemented on
liver tissue until onset of coagulation and carbonization. Thermal damages
occurred in horizontal and vertical direction have been experimentally measured
and recorded. 70 % of this data was used to training ANN model, which was built
in Matlab environment. Power and application time were defined as input
parameters of model. Coagulation /carbonization occurrence,
diameter and depth of thermal damages were used
as output of model. This data was used to calculate and compare MSE value of
five different learning algorithm (LM, GDA, GDX, CGP ve BFG). GDX algorithm with a 14 neuron in hidden layer,
2-14-3, was resulted in minimum MSE value (7.58E-2) and remaining untrained data was used to show prediction performance
of GDX algorithm. ANN model outputs were compared with experimental results. It
was shown that diameter and depth of coagulation and carbonization can be
predicted using using ANN method with a minimum 2.7% and 3.6% success rate,
respectively. According to these results, ANN assisted laser
thermal therapies can provide more accurate treatment of undesired target tissue
(tumor) with a minimal damage of surrounding healthy tissues. 

Keywords

Artificial Neural Network (ANN), coagulation, carbonization

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