Ashwini A, Chirchi Vanajaroselin, Balasubramaniam S, Shah Mohd Asif
Department of ECE, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, TamilNadu, India.
Department of ISE, Dayanand Sagar Academy of Technology and Management, Bangalore, Karnataka, India.
Sci Rep. 2025 May 25;15(1):18202. doi: 10.1038/s41598-025-02846-7.
Numerous contemporary computer-aided disease detection methodologies predominantly depend on feature engineering techniques; yet, they possess several drawbacks, including the presence of redundant features and excessive time consumption. Conventional feature engineering necessitates considerable manual effort, resulting in issues from superfluous features that diminish the model's performance potential. In contrast to recent effective deep-learning models, these may address these issues while concurrently obtaining and capturing intricate structures inside extensive medical image datasets. Deep learning models autonomously develop feature extraction abilities but require substantial computational resources and extensive datasets to yield significant abstraction methods. The dimensionality problem is a key challenge in healthcare research. Despite the hopeful advancements in illness identification with deep learning architectures in recent years, attaining high performance remains notably tough, particularly in scenarios with limited data or intricate feature spaces. This research endeavors to elucidate the integration of bio-inspired optimization techniques that improve disease diagnostics through deep learning models. The targeted feature selection of bio-inspired methods enhances computational efficiency and operational efficacy by minimizing model redundancy and computational costs, particularly when data availability is constrained. These algorithms employ natural selection and social behavior models to efficiently explore feature spaces, enhancing the robustness and generalizability of deep learning systems. This paper seeks to elucidate the efficacy of deep learning models in medical diagnostics by employing concepts and strategies derived from biological system ontologies, such as genetic algorithms, particle swarm optimization, ant colony optimization, artificial immune systems, and swarm intelligence. Bio-inspired methodologies have exhibited significant potential in addressing critical challenges in illness detection across many data types. It seeks to tackle the problem by creating bio-inspired optimization methods to enhance efficient and equitable deep learning for illness diagnosis. This work assists researchers in selecting the most effective bio-inspired algorithm for disease categorization, prediction, and the analysis of high-dimensional biomedical data.
众多当代计算机辅助疾病检测方法主要依赖于特征工程技术；然而，它们存在若干缺点，包括存在冗余特征和耗时过长。传统的特征工程需要大量的人工，导致出现多余特征的问题，从而降低了模型的性能潜力。与最近有效的深度学习模型相比，这些模型可能在解决这些问题的同时，还能获取并捕捉大量医学图像数据集中的复杂结构。深度学习模型能自主开发特征提取能力，但需要大量计算资源和广泛的数据集才能产生有效的抽象方法。维度问题是医疗保健研究中的一个关键挑战。尽管近年来深度学习架构在疾病识别方面取得了令人鼓舞的进展，但要实现高性能仍然非常困难，特别是在数据有限或特征空间复杂的情况下。本研究旨在阐明生物启发式优化技术的整合，这些技术通过深度学习模型改善疾病诊断。生物启发式方法的目标特征选择通过最小化模型冗余和计算成本来提高计算效率和操作效能，特别是在数据可用性受限的情况下。这些算法采用自然选择和社会行为模型来有效探索特征空间，增强深度学习系统的鲁棒性和通用性。本文旨在通过运用从生物系统本体论中衍生的概念和策略，如遗传算法、粒子群优化、蚁群优化、人工免疫系统和群体智能，来阐明深度学习模型在医学诊断中的功效。生物启发式方法在解决多种数据类型的疾病检测关键挑战方面已展现出巨大潜力。它试图通过创建生物启发式优化方法来解决该问题，以增强疾病诊断的高效和公平的深度学习。这项工作有助于研究人员选择最有效的生物启发式算法用于疾病分类、预测以及高维生物医学数据分析。
