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基于深度学习的胰腺肿瘤和非肿瘤 CT 扫描分类模型的最优设计。

Design of Optimal Deep Learning-Based Pancreatic Tumor and Nontumor Classification Model Using Computed Tomography Scans.

机构信息

Department of Computer Science College of Computing and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia.

Department of Oral & Maxillofacial Surgery and Diagnostic Sciences Faculty of Dentistry, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia.

出版信息

J Healthc Eng. 2022 Jan 12;2022:2872461. doi: 10.1155/2022/2872461. eCollection 2022.

DOI:10.1155/2022/2872461
PMID:35070232
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8769827/
Abstract

Pancreatic tumor is a lethal kind of tumor and its prediction is really poor in the current scenario. Automated pancreatic tumor classification using computer-aided diagnosis (CAD) model is necessary to track, predict, and classify the existence of pancreatic tumors. Artificial intelligence (AI) can offer extensive diagnostic expertise and accurate interventional image interpretation. With this motivation, this study designs an optimal deep learning based pancreatic tumor and nontumor classification (ODL-PTNTC) model using CT images. The goal of the ODL-PTNTC technique is to detect and classify the existence of pancreatic tumors and nontumor. The proposed ODL-PTNTC technique includes adaptive window filtering (AWF) technique to remove noise existing in it. In addition, sailfish optimizer based Kapur's Thresholding (SFO-KT) technique is employed for image segmentation process. Moreover, feature extraction using Capsule Network (CapsNet) is derived to generate a set of feature vectors. Furthermore, Political Optimizer (PO) with Cascade Forward Neural Network (CFNN) is employed for classification purposes. In order to validate the enhanced performance of the ODL-PTNTC technique, a series of simulations take place and the results are investigated under several aspects. A comprehensive comparative results analysis stated the promising performance of the ODL-PTNTC technique over the recent approaches.

摘要

胰腺肿瘤是一种致命的肿瘤，目前其预测效果非常差。使用计算机辅助诊断 (CAD) 模型对胰腺肿瘤进行自动分类，对于跟踪、预测和分类胰腺肿瘤的存在是必要的。人工智能 (AI) 可以提供广泛的诊断专业知识和准确的介入图像解释。基于此动机，本研究使用 CT 图像设计了一种基于最优深度学习的胰腺肿瘤和非肿瘤分类 (ODL-PTNTC) 模型。ODL-PTNTC 技术的目的是检测和分类胰腺肿瘤和非肿瘤的存在。所提出的 ODL-PTNTC 技术包括自适应窗口滤波 (AWF) 技术，以去除其中存在的噪声。此外，还采用旗鱼优化器的 Kapur 阈值 (SFO-KT) 技术进行图像分割处理。此外，使用胶囊网络 (CapsNet) 进行特征提取，以生成一组特征向量。此外，还采用政治优化器 (PO) 与级联前馈神经网络 (CFNN) 进行分类。为了验证 ODL-PTNTC 技术的增强性能，进行了一系列模拟，并在几个方面研究了结果。全面的对比结果分析表明，ODL-PTNTC 技术的性能优于最新方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d252/8769827/d2dba4c4470f/JHE2022-2872461.012.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d252/8769827/af98f1b4fb2c/JHE2022-2872461.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d252/8769827/17a3dfa94175/JHE2022-2872461.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d252/8769827/31b784ca6243/JHE2022-2872461.003.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d252/8769827/364d3aa5b253/JHE2022-2872461.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d252/8769827/e03a21ced032/JHE2022-2872461.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d252/8769827/c2feb19c3233/JHE2022-2872461.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d252/8769827/af349fa9b01e/JHE2022-2872461.009.jpg
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