Shafi Syed Mohammed, Chinnappan Sathiya Kumar
Vellore Institute of Technology, Vellore, Tamil Nadu, India.
PeerJ Comput Sci. 2024 Dec 13;10:e2444. doi: 10.7717/peerj-cs.2444. eCollection 2024.
According to the World Health Organization (WHO) report, lung disorders are the third leading cause of mortality worldwide. Approximately three million individuals are affected with various types of lung disorders annually. This issue alarms us to take control measures related to early diagnostics, accurate treatment procedures, . The precise identification through the assessment of medical images is crucial for pulmonary disease diagnosis. Also, it remains a formidable challenge due to the diverse and unpredictable nature of pathological lung appearances and shapes. Therefore, the efficient lung disease segmentation and classification model is essential. By taking this initiative, a novel lung disease segmentation with a hybrid LinkNet-Modified LSTM (L-MLSTM) model is proposed in this research article. The proposed model utilizes four essential and fundamental steps for its implementation. The first step is pre-processing, where the input lung images are pre-processed using median filtering. Consequently, an improved Transformer-based convolutional neural network (CNN) model (ITCNN) is proposed to segment the affected region in the segmentation process. After segmentation, essential features such as texture, shape, color, and deep features are retrieved. Specifically, texture features are extracted using modified Local Gradient Increasing Pattern (LGIP) and Multi-texton analysis. Then, the classification step utilizes a hybrid model, the L-MLSTM model. This work leverages two datasets such as the COVID-19 normal pneumonia-CT images dataset (Dataset 1) and the Chest CT scan images dataset (Dataset 2). The dataset is crucial for training and evaluating the model, providing a comprehensive basis for robust and generalizable results. The L-MLSTM model outperforms several existing models, including HDE-NN, DBN, LSTM, LINKNET, SVM, Bi-GRU, RNN, CNN, and VGG19 + CNN, with accuracies of 89% and 95% at learning percentages of 70 and 90, respectively, for datasets 1 and 2. The improved accuracy achieved by the L-MLSTM model highlights its capability to better handle the complexity and variability in lung images. This hybrid approach enhances the model's ability to distinguish between different types of lung diseases and reduces diagnostic errors compared to existing methods.
根据世界卫生组织（WHO）的报告，肺部疾病是全球第三大死因。每年约有300万人受到各种类型肺部疾病的影响。这个问题警示我们要采取与早期诊断、准确治疗程序相关的控制措施。通过医学图像评估进行精确识别对于肺部疾病诊断至关重要。此外，由于病理性肺部外观和形状的多样性和不可预测性，这仍然是一项艰巨的挑战。因此，高效的肺部疾病分割和分类模型至关重要。通过采取这一举措，本文提出了一种基于混合LinkNet-改进长短期记忆网络（L-MLSTM）模型的新型肺部疾病分割方法。所提出的模型在实现过程中利用了四个基本步骤。第一步是预处理，使用中值滤波对输入的肺部图像进行预处理。随后，在分割过程中提出了一种改进的基于Transformer的卷积神经网络（CNN）模型（ITCNN）来分割受影响区域。分割后，检索纹理、形状、颜色等基本特征以及深度特征。具体而言，使用改进的局部梯度增强模式（LGIP）和多纹理分析提取纹理特征。然后，分类步骤使用混合模型L-MLSTM模型。这项工作利用了两个数据集，即COVID-19正常肺炎CT图像数据集（数据集1）和胸部CT扫描图像数据集（数据集2）。该数据集对于训练和评估模型至关重要，为获得稳健且可推广的结果提供了全面的基础。L-MLSTM模型优于几个现有模型，包括HDE-NN、深度信念网络（DBN）、长短期记忆网络（LSTM）、LinkNet、支持向量机（SVM）、双向门控循环单元（Bi-GRU）、循环神经网络（RNN）、卷积神经网络（CNN）和VGG19+CNN，对于数据集1和数据集2，在学习率分别为70%和90%时，准确率分别为89%和95%。L-MLSTM模型实现的更高准确率凸显了其更好地处理肺部图像复杂性和变异性的能力。与现有方法相比，这种混合方法增强了模型区分不同类型肺部疾病的能力并减少了诊断错误。
