Classification of Lymph Node Tumor Images Using Adaptive Filtering in Convolutional Neural Networks

Abstract

Tumors of the lymphatic system can be either benign or malignant. However, because traditional diagnostic approaches rely on subjective assessment by individual pathologists and often lead to significant discrepancies and misclassifications, early and accurate diagnosis is important for successful treatment and improved scientific outcomes. We compare an adaptive convolutional neural network (CNN) model, trained on filter shapes, with regular CNNs by benchmarking them for image classification. This adaptive model automatically retunes filter size based on statistical tests of images, improving feature extraction and classification performance. More specifically, it achieves an accuracy of around 92%, which is about 10- 13% higher than random predictions using most standard models; however, it also has high precision and recall, 94% and approximately 4.5% higher in this metric, and 85%, with a further 27% in-crease relative to standard benchmarks used for others. An F1-score of 89% represents an 18% uplift, while AUC, at 97, outperforms the same model without ours by up to 11%. In ad-dition, the mean squared error (MSE) is reduced to 0.096 for the adaptive model, compared with 0.24 for traditional methods. The results show that an adaptive filter can benefit CNN-based image classification. This exploratory research opens a new opportunity to apply deep learning methodologies in practice, supporting their integration into real-world applications such as medical imaging, autonomous systems, and security analysis. Although a small step, it shows the model as an efficient, fast, and scalable classification solution.