ADNF-Clustering: An Adaptive and Dynamic Neuro-Fuzzy Clustering for Leukemia Prediction

TL;DR

This paper presents ADNF-Clustering, an innovative neuro-fuzzy streaming framework that enhances leukemia cell pattern recognition by adaptively updating clusters and quantifying uncertainty in real time.

Contribution

It introduces a novel adaptive neuro-fuzzy clustering method combining CNN features with online fuzzy clustering and entropy-based topology refinement for leukemia data analysis.

Findings

Achieved a silhouette score of 0.51 on leukemia microscopy data.

Demonstrated superior clustering cohesion and separation over static methods.

Enabled real-time, uncertainty-aware leukemia cell pattern recognition.

Abstract

Leukemia diagnosis and monitoring rely increasingly on high-throughput image data, yet conventional clustering methods lack the flexibility to accommodate evolving cellular patterns and quantify uncertainty in real time. We introduce Adaptive and Dynamic Neuro-Fuzzy Clustering, a novel streaming-capable framework that combines Convolutional Neural Network-based feature extraction with an online fuzzy clustering engine. ADNF initializes soft partitions via Fuzzy C-Means, then continuously updates micro-cluster centers, densities, and fuzziness parameters using a Fuzzy Temporal Index (FTI) that measures entropy evolution. A topology refinement stage performs density-weighted merging and entropy-guided splitting to guard against over- and under-segmentation. On the C-NMC leukemia microscopy dataset, our tool achieves a silhouette score of 0.51, demonstrating superior cohesion and separation over static baselines. The method's adaptive uncertainty modeling and label-free operation hold immediate potential for integration within the INFANT pediatric oncology network, enabling scalable, up-to-date support for personalized leukemia management.