Binary Image Classification with CNNs, Transfer Learning and Classical Models
, Dimitrios Vasileios Oikonomou2, Sofia Panagiota Chaliasou3 and Nikolaos Rigas4(This article belongs to the Special Issue on SP7 (Special Issue on Multidisciplinary Sciences and Advanced Technology (SI-MSAT 2025)) and the Section Artificial Intelligence – Computer Science (AIC))
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Oikonomou, N. V. , Oikonomou, D. V. , Chaliasou, S. P. and Rigas, N. (2026). Binary Image Classification with CNNs, Transfer Learning and Classical Models. Journal of Engineering Research and Sciences, 5(1), 66–75. https://doi.org/10.55708/js0501006
Nikolaos Vasileios Oikonomou, Dimitrios Vasileios Oikonomou, Sofia Panagiota Chaliasou and Nikolaos Rigas. "Binary Image Classification with CNNs, Transfer Learning and Classical Models." Journal of Engineering Research and Sciences 5, no. 1 (January 2026): 66–75. https://doi.org/10.55708/js0501006
N.V. Oikonomou, D.V. Oikonomou, S.P. Chaliasou and N. Rigas, "Binary Image Classification with CNNs, Transfer Learning and Classical Models," Journal of Engineering Research and Sciences, vol. 5, no. 1, pp. 66–75, Jan. 2026, doi: 10.55708/js0501006.
This study presents a comprehensive comparative analysis of binary face classification utilizing Deep Learning and traditional Machine Learning approaches. We evaluate three distinct modeling strategies: (1) End-to-end Convolutional Neural Networks (CNNs), including a baseline TensorFlow model and an optimized PyTorch architecture; (2) Hybrid CNN-MLP networks; and (3) Feature extraction via a pre-trained ResNet50 coupled with classical classifiers (Random Forest, Logistic Regression). The experimental dataset consists of 6,376 face images (5,102 training, 1,274 validation) derived from a Kaggle challenge. We implement rigorous data augmentation (rotation, shifts, flips) and regularization techniques (Dropout, Batch Normalization, Weight Decay) to mitigate overfitting. Results demonstrate that the optimized PyTorch CNN achieved the highest generalization performance with a validation accuracy of ~85.9% and an AUC of 0.94, utilizing AdamW optimizer and Cosine Annealing scheduling. Conversely, the classical models (Random Forest, Logistic Regression) utilizing ResNet50 features exhibited near-perfect training metrics (AUC ≈ 1.0) and competitive validation accuracy (>90%), highlighting the efficacy of transfer learning. We critically analyze the "underfitting" phenomenon observed in the baseline CNN (Training Accuracy < Validation Accuracy) attributing it to aggressive regularization. This work provides a clear roadmap for selecting between computational-heavy deep architectures and efficient feature-based classical models based on available resources and accuracy requirements.
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