Assessment Using Dataset 2.

Thoracic diseases, including pneumonia, tuberculosis, lung cancer, and others, pose significant health risks and require timely and accurate diagnosis to ensure proper treatment. Thus, in this research, a model for thorax disease classification using Chest X-rays is proposed by considering deep learning model. The input is pre-processed by resizing, normalizing pixel values, and applying data augmentation to address the issue of imbalanced datasets and improve model generalization. Significant features are extracted from the images using an Enhanced Auto-Encoder (EnAE) model, which combines a stacked auto-encoder architecture with an attention module to enhance feature representation and classification accuracy. To further improve feature selection, we utilize the Chaotic Whale Optimization (ChWO) Algorithm, which optimally selects the most relevant attributes from the extracted features. Finally, the disease classification is performed using the novel Improved Swin Transformer (IMSTrans) model, which is designed to efficiently process high-dimensional medical image data and achieve superior classification performance. The proposed EnAE + ChWO+IMSTrans model for thorax disease classification was evaluated using extensive Chest X-ray datasets and the Lung Disease Dataset. The proposed method demonstrates enhanced Accuracy, Precision, Recall, F-Score, MCC and MAE of 0.964, 0.977, 0.9845, 0.964, 0.9647, and 0.184 respectively indicating the reliable and efficient solution for thorax disease classification.胸腔疾病（包括肺炎、肺结核、肺癌等）会带来严重的健康风险，唯有及时且精准的诊断才能保障患者获得恰当的治疗。为此，本研究提出一种基于深度学习的胸部X线胸腔疾病分类模型。针对数据集类别不平衡问题并提升模型泛化能力，首先对输入数据进行尺寸调整、像素值归一化以及数据增强等预处理操作。本研究采用增强型自编码器（Enhanced Auto-Encoder, EnAE）模型从胸部X线影像中提取有效特征，该模型结合堆叠自编码器架构与注意力模块，以强化特征表征能力并提升分类精度。为进一步优化特征选择环节，本研究引入混沌鲸鱼优化（Chaotic Whale Optimization, ChWO）算法，从提取的特征中最优筛选出最具相关性的属性子集。最后，本研究采用新型改进型Swin Transformer（IMSTrans）模型完成疾病分类任务，该模型专为高效处理高维医学影像数据设计，可实现更优异的分类性能。本研究提出的EnAE+ChWO+IMSTrans胸腔疾病分类模型，通过大规模胸部X线数据集与肺部疾病数据集进行了全面的性能评估。实验结果显示，该方法的准确率、精确率、召回率、F-Score、马修斯相关系数（Matthews Correlation Coefficient, MCC）以及平均绝对误差（Mean Absolute Error, MAE）分别达到0.964、0.977、0.9845、0.964、0.9647与0.184，证实其为一种可靠且高效的胸腔疾病分类解决方案。