基于光子晶体光纤的无声杀手自动诊断系统

Sunil Sharma, L. Tharani
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引用次数: 0

摘要

胰腺癌(PC)是一种早期诊断困难的致命疾病。这就是它被称为“沉默杀手”的原因。传统的诊断方法往往是侵入性的,可能导致误诊。本文的目的是提出基于光子晶体光纤(PCFs)的人工智能(AI)系统,使其成为诊断胰腺癌的有前途的工具。pcf是一种光纤(OFs),允许以高分辨率检测光,用于分析组织样本的生化成分,并将结果数据输入人工智能算法。所提出的系统有可能显著提高胰腺癌的早期发现和诊断,从而带来更好的结果。决策树(DT)模型的准确率为86.8%,灵敏度为81.6%,特异性为90.3%。支持向量机(SVM)模型的准确率为90.9%,灵敏度为95.7%,特异性为86.0%。k近邻(KNN)模型的准确率为90.8%,灵敏度为91.7%,特异性为89.1%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Photonic crystal fiber based automated system to diagnose silent killer
Pancreatic cancer (PC) is a lethal disease that is difficult to diagnose in its early stages. This is the reason it is deadly known as “The silent killer”. Traditional diagnostic methods are often invasive and can lead to misdiagnosis. The purpose of this manuscript is to propose photonic crystal fibers (PCFs) based artificial intelligence (AI) systems to materialize it as a promising tool for diagnosing pancreatic cancer. PCFs are optical fibers (OFs) that allow for the detection of light at high resolution and used to analyze the biochemical composition of tissues samples and feed the resulting data into an AI algorithm. The proposed system has the potential to significantly improve the early detection and diagnosis of pancreatic cancer, which lead to better outcomes. The Decision Tree (DT) model achieved an accuracy of 86.8%, a sensitivity of 81.6%, and a specificity of 90.3%. The Support Vector Machine (SVM) model achieved an accuracy of 90.9%, a sensitivity of 95.7%, and a specificity of 86.0%. The K-nearest neighbor (KNN) model achieved an accuracy of 90.8%, a sensitivity of 91.7%, and a specificity of 89.1%.
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