Bias Mitigation in Ensemble-Based Meat Freshness Classification Using Grad-CAM

Öz

Visual biases in deep learning models, such as focusing on packaging trays instead of meat texture, reduce the reliability of computer vision systems in food safety applications. This study proposes a Grad-CAM-guided bias mitigation framework for multiclass meat freshness classification that combines explainable AI with a lightweight hybrid ensemble design. A MiniCAM attention module is integrated into MobileNetV2 to redirect model focus toward meat-specific visual cues, and its features are fused with complementary embeddings extracted from Xception. The final decision is obtained by combining the predictions of MobileNetV2 with classical classifiers (SVM and XGBoost) using test-time augmentation and grid-optimized weighted ensembling. The proposed framework achieves 99.78% accuracy on the held-out test set and 99.66% ± 0.23 average accuracy under 5-fold cross-validation, while maintaining real-time efficiency (4.3M parameters, 16.5 MB model size, and 825.1 FPS on a single GPU), and effectively suppresses non-informative background elements (e.g., packaging trays) as confirmed by Grad-CAM visualizations. These results demonstrate that integrating explainable bias mitigation with lightweight ensemble learning enables reliable and deployable meat freshness assessment for real-world food safety inspection.

Anahtar Kelimeler

• Bias Mitigation
• Computer Vision
• Ensemble Learning
• Food Safety
• Grad-CAM
• Meat Freshness

Grad-CAM Kullanılarak Topluluk Tabanlı Et Tazeliği Sınıflandırmasında Önyargının Azaltılması

Öz

Derin öğrenme modellerindeki görsel önyargılar, örneğin et dokusuna odaklanmak yerine ambalaj tepsilerine odaklanmak, gıda güvenliği uygulamalarında güvenilirliği azaltır. Bu çalışmada, hibrit topluluk öğrenmesiyle et tazeliği sınıflandırması için Grad-CAM rehberli bir önyargı azaltma çerçevesi önerilmektedir. Aktivasyon haritası analizi, temel modellerin sistematik olarak bilgilendirici olmayan arka plan unsurlarına dikkat ettiğini ortaya koymuştur. Bunu düzeltmek için MobileNetV2’ye bir mini kanal dikkat modülü (MiniCAM) entegre ettik, özelliklerini Xception gömüleriyle birleştirdik ve test-zamanı artırımı (TTA) ile ızgara-optimizasyonlu ağırlıklı topluluk kullanarak SVM ve XGBoost tahminlerini birleştirdik. Bu yaklaşım, modelin odağını ilgili ipuçlarına (renk, doku ve nem) başarıyla yönlendirmiştir. Bu ipuçları, taze, yarı taze ve bozulmuş sınıfları ayırt etmede kritik öneme sahiptir. Gerçek dünya verilerinden oluşan çok sınıflı bir veri seti üzerinde değerlendirilen önerilen topluluk, daha yüksek doğruluk ve dayanıklılık göstermiştir. Bu çalışma, görsel akıl yürütmeyi alan uzmanlığıyla birleştirir ve Açıklanabilir Yapay Zeka (XAI)’nın yüksek riskli gıda kalite denetiminde kullanılmasını önerir. Böylece, kaynak kısıtlı tedarik zincirlerinde güvenilir dağıtım sağlanabilir.

Anahtar Kelimeler

• Önyargı Azaltma
• Bilgisayarla Görme
• Topluluk Öğrenmesi
• Gıda Güvenliği
• Grad-CAM
• Et Tazeliği.

Kaynakça

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