This study introduces a rigorous stacked ensemble framework for binary image classification, specifically targeting the Cats vs. Dogs dataset. The proposed method integrates eight transfer-learning convolutional neural networks (CNNs): Xception, VGG16, VGG19, InceptionV3, InceptionResNetV2, MobileNet, MobileNetV2, and DenseNet121 as base learners, combined via eight different meta-learners. Through extensive experimentation that included hyperparameter optimization, ablation studies, and robustness assessments (under Gaussian noise and image blur), the ensemble demonstrated superior accuracy and reliability. The optimal configuration, which utilized a Support Vector Machine (SVM) meta-learner, achieved an accuracy of 99.28%, surpassing prior benchmarks. Statistical analysis confirmed the significance of this improvement (paired t-test: t = 29.94, p < 0.0001). InceptionResNetV2 and Xception made the largest contributions to ensemble performance, as revealed by ablation studies. The model also maintained robust performance under noise (97.91% accuracy) and blur (98.43% accuracy), attesting to its suitability for practical applications such as veterinary diagnostics and pet identification. This work highlights the effectiveness of deep learning ensembles in binary image classification tasks and suggests future directions in adaptive ensemble weighting and cross-dataset validation.
Himel et al. (Sat,) studied this question.