Title: Comprehensive analytical research on highly similar immunoassay data based on ResNet and convolutional neural networks

Authors: Jianzhang Li; Zixuan Zhao

Addresses: School of Mathematics and Physics, Xi'an Jiaotong-Liverpool University, Suzhou, 215000, China ' School of Mathematics and Physics, Xi'an Jiaotong-Liverpool University, Suzhou, 215000, China

Abstract: Image recognition has become essential in biomedical research, particularly for disease diagnosis and biomarker detection. While convolutional neural networks (CNNs) have achieved success in image tasks, their performance declines with complex, noisy biomedical data. This study compares ResNet and traditional CNNs in antibody immune detection. ResNet's residual connections effectively address vanishing gradients in deep networks, improving training stability and maintaining consistent performance. Its superior feature extraction captures subtle image variations, achieving high classification accuracy under noisy conditions. ResNet also shows strong robustness across different network depths. Experimental results demonstrate that ResNet outperforms conventional CNNs in detection accuracy, especially with specialised biomedical images, and holds significant promise for clinical application. The findings indicate that ResNet is a more reliable and accurate framework for biomedical image recognition, especially in complex and high-noise environments, offering advantages in both research and practical diagnostic contexts.

Keywords: residual networks; ResNet; convolutional neural networks; CNNs; immunoassay data; recognition.

DOI: 10.1504/IJICA.2025.149773

International Journal of Innovative Computing and Applications, 2025 Vol.15 No.4, pp.246 - 255

Received: 28 Jun 2025
Accepted: 18 Aug 2025
Published online: 12 Nov 2025 *

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