Derin Sinir Ağları Kullanılarak Koyun-Keçi Çiçeği Hastalığının Tespiti ve Sınıflandırılması için Hibrit Bir Yaklaşım

Year 2022, , 1542 - 1554, 31.12.2022

Nilgün Şengöz

https://doi.org/10.31202/ecjse.1159621

Abstract

Yapay zeka, ve bunun alt dalları olan makine öğrenimi ve derin öğrenme medikal görüntüleme sistemlerinden yüz tanıma, otonom sürüş gibi bir çok farklı alanlarda kendini ispat etmiştir. Özellikle derin öğrenme modelleri günümüzde çok popüler olmuştur. Derin öğrenme modelleri yapısı itibariyle çok karmaşık olduğundan ötürü doğası gereği kara-kutu modellerine en iyi örneklerden birisidir. Bu durum yorumlanabilirlik ve açıklanabilirlik bağlamında son kullanıcıyı şüphe içerisinde bırakmaktadır. Bu nedenle, bu tür sistemlerin anlaşılabilir metotların açıklanabilir yapay zeka (AYZ) ile yapılması ihtiyacı son yıllarda yaygın olarak geliştirilmiştir. Bu bağlamda, çalışma neticesinde hibrid bir metot geliştirilmiş olup farklı derin öğrenme algoritmaları üzerinden yeni ve orijinal veri kümesi üzerinde sınıflandırma çalışması gerçekleştirilmiştir. Sınıflandırma doğruluğu %99,643 ile VGG16 mimarisi üzerinden Grad-CAM uygulaması gerçekleştirilmiş ve CLAHE metodu ile ön işlenen görüntülerin ısı haritaları çıkarılmıştır.

Keywords

derin öğrenme, CLAHE, açıklanabilir yapay zeka, koyun-keçi çiçek hastalığı

Thanks

Bu çalışmanın gerçekleştirilmesi için desteklerini esirgemeyen Prof. Dr. Özlem Özmen’e ve danışman hocam Prof. Dr. Tuncay Yiğit’e teşekkür ederim.

A Hybrid Approach for Detection and Classification of Sheep-Goat Pox Disease Using Deep Neural Networks

Abstract

Artificial intelligence and its sub-branches, machine learning and deep learning, have proven themselves in many different areas such as medical imaging systems, face recognition, autonomous driving. Especially deep learning models have become very popular today. Because deep learning models are very complex in nature, they are one of the best examples of black-box models. This situation leaves the end user in doubt in terms of interpretability and explainability. Therefore, the need to make such systems understandable methods with explainable artificial intelligence (XAI) has been widely developed in recent years. In this context, a hybrid method has been developed as a result of the study, and classification study has been carried out on the new and original dataset over different deep learning algorithms. Grad-CAM application was performed on VGG16 architecture with classification accuracy of 99.643% and heat maps of pre-processed images were obtained by CLAHE method.

Keywords

deep learning, CLAHE, explainable artificial intelligence, sheep-goat pox

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