Department of Computer Science & Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh.
Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh.
Sensors (Basel). 2023 Sep 7;23(18):7724. doi: 10.3390/s23187724.
Colorectal polyps in the colon or rectum are precancerous growths that can lead to a more severe disease called colorectal cancer. Accurate segmentation of polyps using medical imaging data is essential for effective diagnosis. However, manual segmentation by endoscopists can be time-consuming, error-prone, and expensive, leading to a high rate of missed anomalies. To solve this problem, an automated diagnostic system based on deep learning algorithms is proposed to find polyps. The proposed IRv2-Net model is developed using the UNet architecture with a pre-trained InceptionResNetV2 encoder to extract most features from the input samples. The Test Time Augmentation (TTA) technique, which utilizes the characteristics of the original, horizontal, and vertical flips, is used to gain precise boundary information and multi-scale image features. The performance of numerous state-of-the-art (SOTA) models is compared using several metrics such as accuracy, Dice Similarity Coefficients (DSC), Intersection Over Union (IoU), precision, and recall. The proposed model is tested on the Kvasir-SEG and CVC-ClinicDB datasets, demonstrating superior performance in handling unseen real-time data. It achieves the highest area coverage in the area under the Receiver Operating Characteristic (ROC-AUC) and area under Precision-Recall (AUC-PR) curves. The model exhibits excellent qualitative testing outcomes across different types of polyps, including more oversized, smaller, over-saturated, sessile, or flat polyps, within the same dataset and across different datasets. Our approach can significantly minimize the number of missed rating difficulties. Lastly, a graphical interface is developed for producing the mask in real-time. The findings of this study have potential applications in clinical colonoscopy procedures and can serve based on further research and development.
结直肠息肉是一种癌前病变，可导致更严重的疾病，即结直肠癌。使用医学成像数据准确分割息肉对于有效的诊断至关重要。然而，内镜医师的手动分割可能既耗时、易错又昂贵，导致异常漏诊率高。为了解决这个问题，提出了一种基于深度学习算法的自动化诊断系统来发现息肉。所提出的 IRv2-Net 模型是使用 UNet 架构和预训练的 InceptionResNetV2 编码器开发的，从输入样本中提取大多数特征。使用测试时间增强 (TTA) 技术，利用原始、水平和垂直翻转的特征，获得精确的边界信息和多尺度图像特征。使用准确性、Dice 相似系数 (DSC)、交并比 (IoU)、精度和召回率等多个指标比较了许多最先进 (SOTA) 模型的性能。在所提出的模型在 Kvasir-SEG 和 CVC-ClinicDB 数据集上进行测试，在处理看不见的实时数据方面表现出卓越的性能。它在接收器操作特征 (ROC-AUC) 和精度-召回率 (AUC-PR) 曲线下的面积覆盖率方面达到最高。该模型在同一数据集和不同数据集内的不同类型息肉（包括更大、更小、过饱和、无蒂或扁平息肉）的定性测试结果均表现出色。我们的方法可以显著减少漏诊难度的数量。最后，开发了一个图形界面来实时生成掩模。本研究的结果具有在临床结肠镜检查程序中的潜在应用，并可以根据进一步的研究和开发来提供帮助。
