Kundu Shreyan, Mukhopadhyay Souradeep, Talukdar Rahul, Kaplun Dmitrii, Voznesensky Alexander, Sarkar Ram
Department of Computer Science & Engineering, Institute of Engineering & Management, Kolkata, India, 700091.
Department of Computer Science & Automation, Indian Institute of Science, Bangalore, India, 560012.
Sci Rep. 2025 Jul 1;15(1):21225. doi: 10.1038/s41598-025-00217-w.
The most common types of kidneys and liver cancer are renal cell carcinoma (RCC) and hepatic cell carcinoma (HCC), respectively. Accurate grading of these carcinomas is essential for determining the most appropriate treatment strategies, including surgery or pharmacological interventions. Traditional deep learning methods often struggle with the intricate and complex patterns seen in histopathology images of RCC and HCC, leading to inaccuracies in classification. To enhance the grading accuracy for liver and renal cell carcinoma, this research introduces a novel feature selection and fusion framework inspired by economic theories, incorporating attention mechanisms into three Convolutional Neural Network (CNN) architectures-MobileNetV2, DenseNet121, and InceptionV3-as foundational models. The attention mechanisms dynamically identify crucial image regions, leveraging each CNN's unique strengths. Additionally, a Gini-based feature selection method is implemented to prioritize the most discriminative features, and the extracted features from each network are optimally combined using a fusion technique modeled after a linear production function, maximizing each model's contribution to the final prediction. Experimental evaluations demonstrate that this proposed approach outperforms existing state-of-the-art models, achieving high accuracies of 93.04% for RCC and 98.24% for LCC. This underscores the method's robustness and effectiveness in accurately grading these types of cancers. The code of our method is publicly available in https://github.com/GHOSTCALL983/GRADE-CLASSIFICATION .
最常见的肾癌和肝癌类型分别是肾细胞癌（RCC）和肝细胞癌（HCC）。准确对这些癌症进行分级对于确定最合适的治疗策略至关重要，这些策略包括手术或药物干预。传统的深度学习方法在RCC和HCC组织病理学图像中呈现的复杂模式上常常遇到困难，导致分类不准确。为了提高肝癌和肾细胞癌的分级准确性，本研究引入了一种受经济理论启发的新颖特征选择和融合框架，将注意力机制融入三种卷积神经网络（CNN）架构——MobileNetV2、DenseNet121和InceptionV3——作为基础模型。注意力机制动态识别关键图像区域，利用每个CNN的独特优势。此外，实施了一种基于基尼系数的特征选择方法，以对最具判别力的特征进行优先级排序，并且使用以线性生产函数为模型的融合技术对从每个网络提取的特征进行最优组合，从而最大化每个模型对最终预测的贡献。实验评估表明，所提出的方法优于现有的最先进模型，RCC的准确率达到93.04%，LCC的准确率达到98.24%。这凸显了该方法在准确分级这些类型癌症方面的稳健性和有效性。我们方法的代码可在https://github.com/GHOSTCALL983/GRADE-CLASSIFICATION上公开获取。
