Chauhan Nitin Kumar, Singh Krishna, Kumar Amit, Mishra Ashutosh, Gupta Sachin Kumar, Mahajan Shubham, Kadry Seifedine, Kim Jungeun
Department of ECE, Indore Institute of Science & Technology, Indore, 453331, India.
DSEU Okhla Campus-I, Formerly G. B. Pant Engineering College, New Delhi, 110020, India.
Sci Rep. 2025 Apr 14;15(1):12801. doi: 10.1038/s41598-025-97719-4.
Current artificial intelligence (AI) trends are revolutionizing medical image processing, greatly improving cervical cancer diagnosis. Machine learning (ML) algorithms can discover patterns and anomalies in medical images, whereas deep learning (DL) methods, specifically convolutional neural networks (CNNs), are extremely accurate at identifying malignant lesions. Deep models that have been pre-trained and tailored through transfer learning and fine-tuning become faster and more effective, even when data is scarce. This paper implements a state-of-the-art Hybrid Learning Network that combines the Progressive Resizing approach and Principal Component Analysis (PCA) for enhanced cervical cancer diagnostics of whole slide images (WSI) slides. ResNet-152 and VGG-16, two fine-tuned DL models, are employed together with transfer learning to train on augmented and progressively resized training data with dimensions of 224 × 224, 512 × 512, and 1024 × 1024 pixels for enhanced feature extraction. Principal component analysis (PCA) is subsequently employed to process the combined features extracted from two DL models and reduce the dimensional space of the feature set. Furthermore, two ML methods, Support Vector Machine (SVM) and Random Forest (RF) models, are trained on this reduced feature set, and their predictions are integrated using a majority voting approach for evaluating the final classification results, thereby enhancing overall accuracy and reliability. The accuracy of the suggested framework on SIPaKMeD data is 99.29% for two-class classification and 98.47% for five-class classification. Furthermore, it achieves 100% accuracy for four-class categorization on the LBC dataset.
当前的人工智能（AI）趋势正在彻底改变医学图像处理，极大地改善宫颈癌诊断。机器学习（ML）算法可以发现医学图像中的模式和异常，而深度学习（DL）方法，特别是卷积神经网络（CNN），在识别恶性病变方面极其准确。通过迁移学习和微调进行预训练和定制的深度模型，即使在数据稀缺的情况下也能变得更快、更有效。本文实现了一种先进的混合学习网络，该网络结合了渐进式调整大小方法和主成分分析（PCA），用于增强对全切片图像（WSI）幻灯片的宫颈癌诊断。ResNet-152和VGG-16这两个经过微调的DL模型与迁移学习一起使用，在尺寸为224×224、512×512和1024×1024像素的增强和渐进式调整大小的训练数据上进行训练，以增强特征提取。随后采用主成分分析（PCA）来处理从两个DL模型中提取的组合特征，并减少特征集的维度空间。此外，在这个减少的特征集上训练了两种ML方法，支持向量机（SVM）和随机森林（RF）模型，并使用多数投票方法整合它们的预测，以评估最终的分类结果，从而提高整体准确性和可靠性。所提出框架在SIPaKMeD数据上的二类分类准确率为99.29%，五类分类准确率为98.47%。此外，它在LBC数据集上的四类分类准确率达到了100%。
