Parçacık Sürüsü Optimizasyon Algoritması ile Optimize Edilmiş Evrişimsel Sinir Ağı Kullanılarak Dermoskopik Görüntülerden Cilt Kanserinin Sınıflandırılması

Öz

Dermoskopik görüntülerden cilt lezyonlarını sınıflandırmak için güçlü bir tıbbi karar destek sistemi oluşturmak cilt kanserinin teşhisi için önemli bir adımdır. Laboratuvarlarda cilt kanseri teşhisi için gerçekleştirilen manuel araştırma, insan yorgunluğu, birlikte çalışabilirlik hataları vb. gibi belirli faktörler nedeniyle hatalara açıktır. Bununla birlikte, cilt lezyonlarının karmaşık morfolojik yapısından dolayı eğitimli verilerin kullanılmasında ciddi sorunlar yaşanmaktadır. Son yıllarda, Evrişimli Sinir Ağı (CNN) kullanılarak dermoskopik görüntülerden cilt kanseri türlerini tespit etmede önemli ilerlemeler kaydedilmiştir. Bu çalışmanın temel amacı, farklı sınıf sayılarına sahip cilt kanseri türlerini içeren dermoskopik görüntüleri yüksek doğrulukla otomatik olarak sınıflandırmak için CNN tabanlı bir model geliştirmektir. Çalışmada, evrimsel bir algoritmanın yanlış sınıflandırma oranını azaltmak üzere bir derin öğrenme modeline entegre edildiği bir metodoloji önerilmiştir. CNN hiper-parametreleri, cilt lezyonlarını dört farklı türde sınıflandırmada ağ performansını iyileştirmek için Parçacık Sürüsü Optimizasyon (PSO) algoritması kullanılarak optimize edilmiştir. Önerilen yöntem ile %99,33 doğruluk, %94,65 duyarlılık, %98,87 özgüllük ve 0,983 AUC sonuçlarına ulaşılarak birleştirilmiş ISIC-2019 ve Asian-dermoscopy veri kümeleri üzerinde test edilmiştir. Sonuçlar, Genetik Algoritmalar (GA), Diferansiyel Evrim (DE) ve Gri Kurt Optimizasyonu (GWO) algoritmaları gibi benzer kanıtlanmış algoritmalarla karşılaştırılmıştır. Deneysel sonuçlar, cilt kanseri sınıflandırması için CNN hiper-parametrelerini optimize etmede PSO’nun verimliliğini göstermiştir.

Anahtar Kelimeler

• Cilt kanseri
• CNN
• Dermoskopik görüntü
• Sınıflandırma
• PSO

Classification of Skin Cancer from Dermoscopic Images using Convolutional Neural Network Optimized with Particle Swarm Optimization Algorithm

Öz

Creating a powerful medical decision support system to classify skin lesions from dermoscopic images is an important step for the diagnosis of skin cancer. Manual research for skin cancer diagnosis in laboratories, human fatigue, interoperability errors, etc. It is prone to errors due to certain factors such as However, there are serious problems in using educated data due to the complex morphological structure of skin lesions. In recent years, significant advances have been made in detecting types of skin cancer from dermoscopic images using the Convolutional Neural Network (CNN). The main aim of this study is to develop a CNN-based model to automatically classify dermoscopic images containing skin cancer types with different class numbers with high accuracy. In the study, a methodology is proposed in which an evolutionary algorithm is integrated into a deep learning model to reduce the misclassification rate. CNN hyper-parameters were optimized using the Particle Swarm Optimization (PSO) algorithm to improve network performance in classifying skin lesions into four different types. The proposed method was tested on the combined ISIC-2019 and Asian-dermoscopy datasets, achieving 99.33% accuracy, 94.65% sensitivity, 98.87% specificity, and 0.983 AUC results. The results are compared with similar proven algorithms such as Genetic Algorithms (GA), Differential Evolution (DE), and Gray Wolf Optimization (GWO) algorithms. Experimental results demonstrated the efficiency of PSO in optimizing CNN hyper-parameters for skin cancer classification.

Kaynakça

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