Thanya T, Jeslin T
PET Engineering College, Vallioor, India.
Universal College of Engineering and Technology, Vallioor, India.
Abdom Radiol (NY). 2025 Mar 6. doi: 10.1007/s00261-025-04860-9.
Computed Tomography (CT) imaging captures detailed cross-sectional images of the pancreas and surrounding structures and provides valuable information for medical professionals. The classification of pancreatic CT images presents significant challenges due to the complexities of pancreatic diseases, especially pancreatic cancer. These challenges include subtle variations in tumor characteristics, irregular tumor shapes, and intricate imaging features that hinder accurate and early diagnosis. Image noise and variations in image quality also complicate the analysis. To address these classification problems, advanced medical imaging techniques, optimization algorithms, and deep learning methodologies are often employed. This paper proposes a robust classification model called DeepOptimalNet, which integrates optimization algorithms and deep learning techniques to handle the variability in imaging characteristics and subtle variations associated with pancreatic tumors. The model uses a comprehensive approach to enhance the analysis of medical CT images, beginning with the application of the Gaussian smoothing filter (GSF) for noise reduction and feature enhancement. It introduces the Modified Remora Optimization Algorithm (MROA) to improve the accuracy and efficiency of pancreatic cancer tissue segmentation. The adaptability of modified optimization algorithms to specific challenges such as irregular tumor shapes is emphasized. The paper also utilizes Deep Transfer CNN with ResNet-50 (DTCNN) for feature extraction, leveraging transfer learning to enhance prediction accuracy in CT images. ResNet-50's strong feature extraction capabilities are particularly relevant to fault diagnosis in CT images. The focus then shifts to a Deep Cascade Convolutional Neural Network with Multimodal Learning (DCCNN-ML) for classifying pancreatic cancer in CT images. The DeepOptimalNet approach underscores the advantages of deep learning techniques, multimodal learning, and cascade architectures in addressing the complexity and subtle variations inherent in pancreatic cancer imaging, ultimately leading to more accurate and robust classifications. The proposed DeepOptimalNet achieves 99.3% accuracy, 99.1% sensitivity, 99.5% specificity, and 99.3% F-score, surpassing existing models in pancreatic tumor classification. Its MROA-based segmentation improves boundary delineation, while DTCNN with ResNet-50 enhances feature extraction for small and low-contrast tumors. Benchmark validation confirms its superior classification performance, reduced false positives, and improved diagnostic reliability compared to traditional deep learning methods.
计算机断层扫描（CT）成像可获取胰腺及其周围结构的详细横截面图像，为医学专业人员提供有价值的信息。由于胰腺疾病（尤其是胰腺癌）的复杂性，胰腺CT图像的分类面临重大挑战。这些挑战包括肿瘤特征的细微变化、不规则的肿瘤形状以及复杂的成像特征，这些都阻碍了准确和早期诊断。图像噪声和图像质量的变化也使分析变得复杂。为了解决这些分类问题，人们经常采用先进的医学成像技术、优化算法和深度学习方法。本文提出了一种名为DeepOptimalNet的强大分类模型，该模型集成了优化算法和深度学习技术，以处理成像特征的可变性以及与胰腺肿瘤相关的细微变化。该模型采用综合方法来加强对医学CT图像的分析，首先应用高斯平滑滤波器（GSF）进行降噪和特征增强。它引入了改进的吸盘鱼优化算法（MROA）来提高胰腺癌组织分割的准确性和效率。强调了改进的优化算法对不规则肿瘤形状等特定挑战的适应性。本文还利用带有ResNet-50的深度迁移卷积神经网络（DTCNN）进行特征提取，利用迁移学习提高CT图像中的预测准确性。ResNet-50强大的特征提取能力与CT图像中的故障诊断特别相关。然后重点转向用于CT图像中胰腺癌分类的具有多模态学习的深度级联卷积神经网络（DCCNN-ML）。DeepOptimalNet方法强调了深度学习技术、多模态学习和级联架构在解决胰腺癌成像中固有的复杂性和细微变化方面的优势，最终实现更准确、更可靠的分类。所提出的DeepOptimalNet在胰腺癌肿瘤分类中实现了99.3%的准确率、99.1%的灵敏度、99.5%的特异性和99.3%的F值，超过了现有模型。其基于MROA的分割改善了边界描绘，而带有ResNet-50的DTCNN增强了对小尺寸和低对比度肿瘤的特征提取。基准验证证实了其与传统深度学习方法相比具有卓越的分类性能、减少的假阳性以及提高的诊断可靠性。
