Raju Akella Subrahmanya Narasimha, Venkatesh K, Padmaja B, Kumar C H N Santhosh, Patnala Pattabhi Rama Mohan, Lasisi Ayodele, Islam Saiful, Razak Abdul, Khan Wahaj Ahmad
Department of Computer Science and Engineering (Data Science), Institute of Aeronautical Engineering, Dundigul, Hyderabad, Telangana, 500043, India.
Department of Networking and Communications, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamilnadu, 603203, India.
Sci Rep. 2024 Dec 3;14(1):30052. doi: 10.1038/s41598-024-81456-1.
Early detection of colorectal carcinoma (CRC), one of the most prevalent forms of cancer worldwide, significantly enhances the prognosis of patients. This research presents a new method for improving CRC detection using a deep learning ensemble with the Computer Aided Diagnosis (CADx). The method involves combining pre-trained convolutional neural network (CNN) models, such as ADaRDEV2I-22, DaRD-22, and ADaDR-22, using Vision Transformers (ViT) and XGBoost. The study addresses the challenges associated with imbalanced datasets and the necessity of sophisticated feature extraction in medical image analysis. Initially, the CKHK-22 dataset comprised 24 classes. However, we refined it to 14 classes, which led to an improvement in data balance and quality. This improvement enabled more precise feature extraction and improved classification results. We created two ensemble models: the first model used Vision Transformers to capture long-range spatial relationships in the images, while the second model combined CNNs with XGBoost to facilitate structured data classification. We implemented DCGAN-based augmentation to enhance the dataset's diversity. The tests showed big improvements in performance, with the ADaDR-22 + Vision Transformer group getting the best results, with a testing accuracy of 93.4% and an AUC of 98.8%. In contrast, the ADaDR-22 + XGBoost model had an AUC of 97.8% and an accuracy of 92.2%. These findings highlight the efficacy of the proposed ensemble models in detecting CRC and highlight the importance of using well-balanced, high-quality datasets. The proposed method significantly enhances the clinical diagnostic accuracy and the capabilities of medical image analysis or early CRC detection.
结直肠癌（CRC）是全球最常见的癌症形式之一，早期检测可显著提高患者的预后。本研究提出了一种新方法，即使用深度学习集成与计算机辅助诊断（CADx）来改进CRC检测。该方法包括使用视觉Transformer（ViT）和XGBoost将预训练的卷积神经网络（CNN）模型（如ADaRDEV2I - 22、DaRD - 22和ADaDR - 22）进行组合。该研究解决了医学图像分析中与数据集不平衡相关的挑战以及复杂特征提取的必要性。最初，CKHK - 22数据集包含24个类别。然而，我们将其细化为14个类别，这导致了数据平衡和质量的提高。这种改进使得能够进行更精确的特征提取并改善分类结果。我们创建了两个集成模型：第一个模型使用视觉Transformer来捕捉图像中的长距离空间关系，而第二个模型将CNN与XGBoost相结合以促进结构化数据分类。我们实施了基于深度卷积生成对抗网络（DCGAN）的增强来提高数据集的多样性。测试显示性能有了很大提高，ADaDR - 22 +视觉Transformer组取得了最佳结果，测试准确率为93.4%，曲线下面积（AUC）为98.8%。相比之下，ADaDR - 22 + XGBoost模型的AUC为97.8%，准确率为92.2%。这些发现突出了所提出的集成模型在检测CRC方面的有效性，并强调了使用平衡良好且高质量数据集的重要性。所提出方法显著提高了临床诊断准确性以及医学图像分析或早期CRC检测的能力。
