İkili Görüntü Sınıflandırma için Evrişimsel Sinir Ağı Mimarilerinin Karşılaştırmalı Analizi: Cilt Kanseri Tespitinde Bir Vaka Çalışması

Year 2024, , 2008 - 2022, 15.12.2024

Şerife Gül Korkut , Hatice Kocabaş , Rifat Kurban

https://doi.org/10.31466/kfbd.1515451

Abstract

Bu çalışmada, ikili görüntü sınıflandırması için Evrişimsel Sinir Ağı (CNN) mimarilerinin kapsamlı bir karşılaştırmalı analizi sunulmuş ve transfer öğreniminin faydalarına vurgu yapılmıştır. MobileNetV3, VGG19, ResNet50 ve EfficientNetB0 gibi önde gelen CNN modellerinin ikili görüntülerden cilt kanseri sınıflandırmadaki performans ve doğruluğu değerlendirilmiştir. Önceden eğitilmiş bir yaklaşım kullanılarak, transfer öğreniminin bu mimarilerin etkinliği üzerindeki etkisi araştırılmış ve ikili görüntü sınıflandırması bağlamında güçlü ve zayıf yönleri belirlenmiştir. Bu makale, optimal CNN mimarisinin seçimi ve transfer öğreniminden yararlanarak ikili görüntü sınıflandırma uygulamalarında, özellikle tıbbi görüntü analiziyle ilgili olanlarda, üstün performans elde etme konusunda değerli içgörüler sağlamayı amaçlamaktadır.

Keywords

Evrişimsel Sinir Ağları (CNN, Transfer Öğrenimi, İkili Görüntü Sınıflandırma, CNN Mimari Karşılaştırması, Cilt Kanseri Tespiti

A Comparative Analysis of Convolutional Neural Network Architectures for Binary Image Classification: A Case Study in Skin Cancer Detection

Abstract

In this study, a comprehensive comparative analysis of Convolutional Neural Network (CNN) architectures for binary image classification is presented with a particular focus on the benefits of transfer learning. The performance and accuracy of prominent CNN models, including MobileNetV3, VGG19, ResNet50, and EfficientNetB0, in classifying skin cancer from binary images are evaluated. Using a pre-trained approach, the impact of transfer learning on the effectiveness of these architectures and identify their strengths and weaknesses within the context of binary image classification are investigated. This paper aims to provide valuable insights for selecting the optimal CNN architecture and leveraging transfer learning to achieve superior performance in binary image classification applications, particularly those related to medical image analysis.

Keywords

Convolutional Neural Networks (CNNs), Transfer Learning, Binary Image Classification, CNN Architecture Comparison, Skin Cancer Detection

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