A hybrid multi-scale convolution neural network with attention and texture features for improved image classification
Irpan Adiputra Pardosi, T. Henny Febriana Harumy, Syahril Efendi · Eastern-European Journal of Enterprise Technologies · 2025
The object of this study is the classification of low-resolution and multi-class images, represented by the CIFAR-10 benchmark dataset. It is challenging to accurately classify low-resolution and multi-class images because traditional CNNs usually have trouble identifying both global and complex texture patterns. To address this issue, this study employs the CIFAR-10 dataset as a representative benchmark for real-world scenarios where image quality is limited, such as in low-cost medical imaging, remote sensing, and security surveillance systems. The limited discriminability of traditional CNNs in these situations is the primary issue addressed. The proposed method employs three parallel convolutional streams with distinct kernel sizes (3 × 3, 5 × 5, and 7 × 7) to capture hierarchical spatial patterns, followed by the integration of two attention mechanisms – squeeze-and-excitation and convolutional block attention module – that adaptively emphasize the most relevant spatial and channel-wise information. In addition, structural texture descriptors such as Gray-level co-occurrence matrix, local binary pattern, and Gabor filters are computed independently and later fused with the deep representations to enrich the feature space. Experiments were carried out on the CIFAR-10 dataset under varying levels of class complexity: 10, 5, and 3 categories. The results reveal that the hybrid approach significantly improves precision, recall, and F1-score across all scenarios, with the highest accuracy of 90.87% obtained when only three classes are involved. These improvements are explained by the complementary nature of deep and handcrafted features, which together enable the model to learn both global semantics and fine-grained local textures can achieve higher classification accuracy, improved reliability, and reduced misclassification errors, ultimately enhancing the effectiveness of applications ranging from medical decision support to intelligent surveillance.