基于混合CNN-LSTM模型的肺癌分割与分类

摘要

癌症是一种致命疾病，由一系列遗传紊乱与多种代谢异常共同引起。肺癌与结肠癌被认为是导致人群死亡与病残的主要原因。在选择最佳治疗方案时，对这类肿瘤的诊断通常是最重要的考量。本研究的主要目标是对肺癌及其严重程度进行分类，并识别恶性肺结节；所提出的方法还对肺癌的分期进行分类，以便识别肺结节。本文用卷积神经网络（CNN）检测肺结节，实现结节的准确分割与分类。所提出的方法分为两部分：首先区分正常与异常，其次对肺癌的不同分期进行分类；在分类阶段提取基于纹理与灰度的特征。与嵌套长短期记忆网络（LSTM）+CNN等其他方法相比，混合CNN-LSTM在准确率（99.35%）、特异度（99.30%）、灵敏度（99.32%）与F1分数（99.29%）方面均取得更优的结果。

Abstract

A collection of genetic disorders and various types of abnormalities in the metabolism lead to cancer, a fatal disease. Lung and colon cancer are found to be main causes of death and infirmity in people. When choosing the best course of treatment, the diagnosis of these tumors is usually the most important consideration. This study's main objectives are to classify lung cancer and its severity, as well as to recognize malignant lung nodules. The suggested approach additionally classifies the stages of lung cancer in order to recognize lung nodules. The convolutional neural network (CNN) is used to detect lung nodules, identifying a nodule which is accurately segmented and classified. The suggested method is separated into dual parts: primarily, it classifies normal and abnormal behavior, and the subsequent one classifies the different stages of lung cancer. Texture and intensity-based features are extracted during the classification stage. When compared to other methods such as nested long short-term memory (LSTM)+CNN, the hybrid CNN-LSTM obtains superior outcomes in terms of accuracy (99.35%), specificity (99.30%), sensitivity (99.32%), and F1-score (99.29%).