Comparative Evaluation of Deep CNN Architectures for Pattern Recognition in Mouse Brain Tissue Section Images

Accurate pattern recognition in biological tissue section images is critical for advancing biomedical research, particularly in diagnostics and neuroscience. However, variability in cellular morphology, imaging artifacts, and structural heterogeneity poses significant challenges for automated analysis. Conventional image-processing approaches often lack generalizability across datasets, highlighting the need for more adaptive and scalable solutions. In this study, the performance of deep convolutional neural networks (CNNs), including AlexNet and ResNets (ResNet-18, ResNet-50, and Res-Net-101), for automated recognition of cellular patterns in adult mouse brain tissue section was evaluated. A labeled dataset was created based on the benchmark Atlas regions to enable systematic comparison of each model’s feature extraction and classification capabilities. The selected CNN architectures vary in depth and representational power, with residual network designed to improve gradient flow in deeper models. The research findings demonstrate that deeper architectures, particularly ResNet-50 and resNet-101, achieve higher classification accuracy and greater robustness to structural variability than AlexNet and ResNet-18, albeit at the cost of increased computational demands. These results underscore a trade-off between model complexity and efficiency while confirming the effectiveness of deep CNNs for biological image analysis. Overall, this work supports the development of scalable, high-throughput frameworks with applications in automated pathology, neuroscience imaging, and broader biomedical data analysis.