Enhanced Pooling Technique in Convolutional Neural Networks Model for Classification of Magnoliophyta Plant DNA Barcodes

Identifying plant Deoxyribonucleic acid (DNA) barcodes has gained substantial importance for biodiversity conservation and understanding evolutionary relationships. However, classifying these species remains challenging due to the complex nature of the DNA sequences. Convolutional Neural Networks (CNNs) have proven effective for pattern recognition tasks in DNA sequence classification, however, standard pooling techniques often result in the loss of important information. This study introduced a modified CNN model with an enhanced pooling technique called Horizontal Sequence Pooling, designed to retain important features and improve accuracy. The method is applied to classify the Magnoliophyta plant DNA barcodes of 75 genera and evaluated against the CNN model with standard max pooling and average pooling techniques. Results show that the proposed technique achieved the highest accuracy of 0.9801, the lowest validation loss of 0.0393, superior Area Under Curve – Precision and Recall (AUC-PR) of 0.9979, and Matthews Correlation Coefficient (MCC) of 0.9814, respectively, outperformed standard pooling techniques. These results indicate that Horizontal Sequence Pooling effectively extracts relevant features from DNA sequences, enhancing the classification accuracy, precision, recall, and balance across both positive and negative classes demonstrating its robustness in handling imbalanced datasets.