Binary Classification of Pulmonary Nodules using Long Short-Term Memory (LSTM)

Smridhi Gupta, Arushi Garg, Vidhi Bishnoi, Nidhi Goel
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引用次数: 1

Abstract

Lung cancer is a prominent reason for deaths all over the globe. A large number of cases have been detected in developed as well as developing nations. It is evident that the probability of survival in the patients is higher only if detected at its nascent stages. Thus, systems employing Computer-Aided Detection (CAD) deliver a faster diagnosis and hence can probably save lives. In the present paper, a classification model for lung nodules that uses Computed Tomography (CT) scans which classifies the given nodule into benign and malignant based on Long Short Term Memory (LSTM) is proposed. The architecture analyzes the images of the nodules extracted from LIDC/ IDRI and Luna-16 datasets. The nodule extraction is executed using the python package pylidc and LSTM is implemented using PyTorch. The highest achieved accuracy using the proposed architecture is 86.98%.
基于长短期记忆的肺结节二分类
肺癌是全球死亡的主要原因之一。在发达国家和发展中国家都发现了大量病例。很明显,只有在早期发现,患者的生存几率才会更高。因此,采用计算机辅助检测(CAD)的系统可以提供更快的诊断,从而可能挽救生命。本文提出了一种基于长短期记忆(LSTM)的肺结节分类模型,该模型利用计算机断层扫描(CT)将给定结节分为良性和恶性。该架构分析了从LIDC/ IDRI和Luna-16数据集提取的结节图像。模块提取使用python包pylidc执行,LSTM使用PyTorch实现。使用所提出的体系结构获得的最高准确率为86.98%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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