Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia.
Department of Electrical Engineering and Biomedical Engineering Research Center, Yuan Ze University, Jungli 32003, Taiwan.
Sensors (Basel). 2021 Aug 20;21(16):5630. doi: 10.3390/s21165630.
Colorectal cancer has become the third most commonly diagnosed form of cancer, and has the second highest fatality rate of cancers worldwide. Currently, optical colonoscopy is the preferred tool of choice for the diagnosis of polyps and to avert colorectal cancer. Colon screening is time-consuming and highly operator dependent. In view of this, a computer-aided diagnosis (CAD) method needs to be developed for the automatic segmentation of polyps in colonoscopy images. This paper proposes a modified SegNet Visual Geometry Group-19 (VGG-19), a form of convolutional neural network, as a CAD method for polyp segmentation. The modifications include skip connections, 5 × 5 convolutional filters, and the concatenation of four dilated convolutions applied in parallel form. The CVC-ClinicDB, CVC-ColonDB, and ETIS-LaribPolypDB databases were used to evaluate the model, and it was found that our proposed polyp segmentation model achieved an accuracy, sensitivity, specificity, precision, mean intersection over union, and dice coefficient of 96.06%, 94.55%, 97.56%, 97.48%, 92.3%, and 95.99%, respectively. These results indicate that our model performs as well as or better than previous schemes in the literature. We believe that this study will offer benefits in terms of the future development of CAD tools for polyp segmentation for colorectal cancer diagnosis and management. In the future, we intend to embed our proposed network into a medical capsule robot for practical usage and try it in a hospital setting with clinicians.
结直肠癌已成为全球第三大常见癌症，也是全球癌症死亡率第二高的癌症。目前，光学结肠镜检查是诊断息肉和预防结直肠癌的首选工具。结肠筛查既耗时又高度依赖操作者。鉴于此，需要开发一种计算机辅助诊断（CAD）方法，以便自动对结肠镜图像中的息肉进行分割。本文提出了一种改进的 SegNet Visual Geometry Group-19（VGG-19），这是一种卷积神经网络，可作为 CAD 方法用于息肉分割。修改包括跳过连接、5×5 卷积滤波器以及并行应用的四个扩张卷积的串联。该模型使用 CVC-ClinicDB、CVC-ColonDB 和 ETIS-LaribPolypDB 数据库进行评估，结果发现，我们提出的息肉分割模型的准确性、敏感度、特异性、精度、平均交并比和骰子系数分别为 96.06%、94.55%、97.56%、97.48%、92.3%和 95.99%。这些结果表明，我们的模型在息肉分割的 CAD 工具的未来开发方面与文献中的先前方案表现一样好，甚至更好。我们相信，这项研究将有助于开发用于结直肠癌诊断和管理的 CAD 工具，用于息肉分割。未来，我们打算将我们提出的网络嵌入到医疗胶囊机器人中，以便实际使用，并在医院环境中与临床医生一起试用。
