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

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.

Anahtar Kelimeler:

Yapay Sinir Ağları (YSA), Koagülasyon, Karbonizasyon

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

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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