The exact positioning of features within the sequence is important in Deoxyribonucleic Acid (DNA) sequence classification, as it encodes the unique genetic information of each organism. In Convolutional Neural Networks (CNNs), pooling techniques are vital for efficient feature extraction. However, traditional pooling techniques demonstrated some limitations in domain-specific pooling for sequence-based data analysis, specifically, lack of positional sensitivity, thereby, encountering information loss. To address these constraints, this study introduces Horizontal Sequence Pooling (HSP), a novel pooling technique that enhances feature extraction by applying positional pooling of sequences across the horizontal axis of the feature maps. The CNN model framework was optimized through data preprocessing and hyper-parameter tuning. The results validate that HSP significantly outperforms traditional pooling techniques across multiple metrics. It achieved a reduction in feature parameters by as high as 96% and validation loss by 19%. Furthermore, HSP attained the highest accuracy of 96%, a Matthews Correlation Coefficient (MCC) of 96%, and an Area-Under-the-Curve Precision and Recall (AUC-PR) score of 99%, indicating its superior ability to balance precision and recall. These results underscore HSP’s efficiency in feature extraction and its capability to handle complex, imbalanced datasets, making it a highly effective method for DNA sequence classification in CNN architectures.

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