Advanced concept for identifying chemico-biological interactions associated with programmed cell death using a multi-scale attention residual convolutional neural network.

Abstract

Early detection of programmed cell death (apoptosis) remains a significant challenge in microscopy and cell biology, particularly when relying on subtle nuclear texture changes observed in stained micrographs. Traditional machine learning models, such as decision trees and random forests, are limited in their methodological capacity and often fail to capture complex spatial relationships within nuclear architecture. Unlike these conventional approaches, our study introduces a novel Multi-Scale Attention Residual Convolutional Neural Network (MSA-RCNN) designed to learn discriminative features from nuclear chromatin patterns associated with early apoptotic changes. We suggest using quantifiers of the Gray-Level Entropy Matrix (GLEM), Run-Length Matrix (RLM), and Discrete Fourier Transform (DFT) as input parameters during training and testing, focusing on the combination of four parameters: RLM Short Run Emphasis, RLM Long Run Emphasis, GLEM Entropy, and DFT Magnitude Spectrum Mean. We also discuss the advantages and limitations of this approach and its potential to be included in future artificial intelligence-based sensing systems for apoptosis detection in research and clinical conditions. Like many deep learning models, the proposed MSA-RCNN is limited by challenges in interpretability and generalization, which future work will address through explainability analyses and validation on different datasets.