Açıklanabilir Yapay Zeka Kullanılarak Meyve Sınıflandırması için Makine Öğrenmesi ve Transfer Öğrenme Yaklaşımlarının Kıyaslanması

Öz

Bu çalışmada, meyve görüntüsü sınıflandırması için geleneksel makine öğrenmesi algoritmaları ve modern derin öğrenme tekniklerinin karşılaştırmalı bir analizini sunuyoruz. Random Forest ve eXreme Gradient Boosting gibi geleneksel sınıflandırıcılar, sinir ağları ve Residual Network (ResNet), Visual Geometry Group 16 (VGG16) ve EfficientNet-B0 gibi en güncel transfer öğrenme modelleri ile birlikte değerlendirildi. Bunlar arasında, ince ayarlı ResNet50 mimarisi, %97.57'lik en yüksek sınıflandırma doğruluğunu elde ederek hem geleneksel yaklaşımları hem de sığ ağları açıkça geride bırakmıştır. Bu çalışmanın temel katkılarından biri, model açıklanabilirliğine verilen önemdir. Derin modellerin karar verme sürecini görselleştirmek için Gradyan Ağırlıklı Sınıf Aktivasyon Eşlemesi (Grad-CAM) ve Grad-CAM++ tekniklerini kullandık ve giriş görüntülerindeki ilgi bölgelerine dair içgörüler sunduk. Açıklanabilirliğin bu şekilde bütünleştirilmesi, özellikle gerçek dünyada çalıştırma veya kullanım senaryolarında kritik öneme sahip olan sınıflandırma sisteminin şeffaflığını ve güvenilirliğini artırır. Bulgular, model seçiminde hem doğruluğun hem de yorumlanabilirliğin önemini vurgulamaktadır ve ince ayarlı derin ağların açıklanabilirlik araçlarıyla birleştirilmesinin, görüntü tabanlı sınıflandırma görevleri için sağlam ve bilgilendirici bir çerçeve sağladığını öne sürmektedir.

Anahtar Kelimeler

• Derin Öğrenme
• Açıklanabilir Yapay Zeka
• GradCAM
• Kalıntı Ağlar
• Transfer Öğrenmesi

Benchmarking Machine Learning and Transfer Learning Approaches for Fruit Classification Using Explainable Artificial Intelligence

Öz

In this study, we investigate traditional machine learning methods with respect to state-of-the-art deep learning approaches for fruit image classification. Conventional classifiers such as Random Forest, and eXreme Gradient Boosting were evaluated alongside neural networks and state-of-the-art transfer learning models, including Residual Network (ResNet), Visual Geometry Group 16 (VGG16), and EfficientNet-B0. The fine-tuned ResNet50 model obtained the maximum classification accuracy as 97.57%, substantially surpassing other traditional solutions as well as shallow networks. One important contribution of this work is in the focus on model explainability. To understand the reasoning from the deep models, we used Gradient-weighted Class Activation Mapping (Grad-CAM) and Grad-CAM++ techniques to visualize what kind of decision is helping the model making predictions as well as by considering what regions in input images are being looked up for the concerned class. This explainability integration provides the transparency and explainability of classification system, being crucial for its real world applicability. The results underline the significance of both model capability and explainability when it comes to model selection, and show that fine-tuned deep networks combined with explainability tools is a strong and useful framework for image based classification.

Anahtar Kelimeler

• Deep Learning
• Explainable Artificial Intelligence
• GradCAM
• Residual Networks
• Transfer Learning

Kaynakça

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