Ejiyi Chukwuebuka Joseph, Qin Zhen, Agbesi Victor K, Yi Ding, Atwereboannah Abena A, Chikwendu Ijeoma A, Bamisile Oluwatoyosi F, Bakanina Kissanga Grace-Mercure, Bamisile Olusola O
College of Nuclear Technology and Automation Engineering, & Sichuan Industrial Internet Intelligent Monitoring and Application Engineering Research Center, Chengdu University of Technology, Sichuan, Chengdu, China; Network and Data Security Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Sichuan, Chengdu, China.
Network and Data Security Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Sichuan, Chengdu, China.
Comput Biol Med. 2025 Feb;185:109494. doi: 10.1016/j.compbiomed.2024.109494. Epub 2024 Dec 4.
Cancer, a global health threat, demands effective diagnostic solutions to combat its impact on public health, particularly for breast, colon, and lung cancers. Early and accurate diagnosis is essential for successful treatment, prompting the rise of Computer-Aided Diagnosis Systems as reliable and cost-effective tools. Histopathology, renowned for its precision in cancer imaging, has become pivotal in the diagnostic landscape of breast, colon, and lung cancers. However, while deep learning models have been widely explored in this domain, they often face challenges in generalizing to diverse clinical settings and in efficiently capturing both local and global feature representations, particularly for multi-class tasks. This underscores the need for models that can reduce biases, improve diagnostic accuracy, and minimize error susceptibility in cancer classification tasks. To this end, we introduce ResoMergeNet (RMN), an advanced deep-learning model designed for both multi-class and binary cancer classification using histopathological images of breast, colon, and lung. ResoMergeNet integrates the Resboost mechanism which enhances feature representation, and the ConvmergeNet mechanism which optimizes feature extraction, leading to improved diagnostic accuracy. Comparative evaluations against state-of-the-art models show ResoMergeNet's superior performance. Validated on the LC-25000 and BreakHis (400× and 40× magnifications) datasets, ResoMergeNet demonstrates outstanding performance, achieving perfect scores of 100 % in accuracy, sensitivity, precision, and F1 score for binary classification. For multi-class classification with five classes from the LC25000 dataset, it maintains an impressive 99.96 % across all performance metrics. When applied to the BreakHis dataset, ResoMergeNet achieved 99.87 % accuracy, 99.75 % sensitivity, 99.78 % precision, and 99.77 % F1 score at 400× magnification. At 40× magnification, it still delivered robust results with 98.85 % accuracy, sensitivity, precision, and F1 score. These results emphasize the efficacy of ResoMergeNet, marking a substantial advancement in diagnostic and prognostic systems for breast, colon, and lung cancers. ResoMergeNet's superior diagnostic accuracy can significantly reduce diagnostic errors, minimize human biases, and expedite clinical workflows, making it a valuable tool for enhancing cancer diagnosis and treatment outcomes.
癌症作为一种全球健康威胁，需要有效的诊断解决方案来应对其对公众健康的影响，尤其是乳腺癌、结肠癌和肺癌。早期准确诊断对于成功治疗至关重要，这促使计算机辅助诊断系统作为可靠且经济高效的工具兴起。组织病理学以其在癌症成像方面的精确性而闻名，已成为乳腺癌、结肠癌和肺癌诊断领域的关键。然而，尽管深度学习模型在该领域已得到广泛探索，但它们在推广到不同临床环境以及有效捕捉局部和全局特征表示方面往往面临挑战，尤其是对于多类任务。这凸显了对能够减少偏差、提高诊断准确性并在癌症分类任务中最小化错误易感性的模型的需求。为此，我们引入了ResoMergeNet（RMN），这是一种先进的深度学习模型，用于使用乳腺癌、结肠癌和肺癌的组织病理学图像进行多类和二元癌症分类。ResoMergeNet集成了增强特征表示的Resboost机制和优化特征提取的ConvmergeNet机制，从而提高了诊断准确性。与现有最先进模型的比较评估显示了ResoMergeNet的卓越性能。在LC - 25000和BreakHis（400倍和40倍放大）数据集上进行验证，ResoMergeNet表现出色，在二元分类的准确性、敏感性、精确性和F1分数方面均达到了100%的完美分数。对于来自LC25000数据集的五类多类分类，它在所有性能指标上均保持令人印象深刻的99.96%。当应用于BreakHis数据集时，ResoMergeNet在400倍放大时实现了99.87%的准确率、99.75%的敏感性、99.78%的精确性和99.77%的F1分数。在40倍放大时，它仍然取得了稳健的结果，准确率、敏感性、精确性和F1分数均为98.85%。这些结果强调了ResoMergeNet的有效性，标志着乳腺癌、结肠癌和肺癌诊断及预后系统取得了重大进展。ResoMergeNet卓越的诊断准确性可以显著减少诊断错误，最小化人为偏差，并加快临床工作流程，使其成为提高癌症诊断和治疗效果的有价值工具。
