Ahoor Ayesha, Arif Fahim, Sajid Muhammad Zaheer, Qureshi Imran, Abbas Fakhar, Jabbar Sohail, Abbas Qaisar
Department of Computer Software Engineering, MCS, National University of Science and Technology, Islamabad 44000, Pakistan.
College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia.
Diagnostics (Basel). 2023 Oct 12;13(20):3195. doi: 10.3390/diagnostics13203195.
The lungs are critical components of the respiratory system because they allow for the exchange of oxygen and carbon dioxide within our bodies. However, a variety of conditions can affect the lungs, resulting in serious health consequences. Lung disease treatment aims to control its severity, which is usually irrevocable. The fundamental objective of this endeavor is to build a consistent and automated approach for establishing the intensity of lung illness. This paper describes MixNet-LD, a unique automated approach aimed at identifying and categorizing the severity of lung illnesses using an upgraded pre-trained MixNet model. One of the first steps in developing the MixNet-LD system was to build a pre-processing strategy that uses Grad-Cam to decrease noise, highlight irregularities, and eventually improve the classification performance of lung illnesses. Data augmentation strategies were used to rectify the dataset's unbalanced distribution of classes and prevent overfitting. Furthermore, dense blocks were used to improve classification outcomes across the four severity categories of lung disorders. In practice, the MixNet-LD model achieves cutting-edge performance while maintaining model size and manageable complexity. The proposed approach was tested using a variety of datasets gathered from credible internet sources as well as a novel private dataset known as Pak-Lungs. A pre-trained model was used on the dataset to obtain important characteristics from lung disease images. The pictures were then categorized into categories such as normal, COVID-19, pneumonia, tuberculosis, and lung cancer using a linear layer of the SVM classifier with a linear activation function. The MixNet-LD system underwent testing in four distinct tests and achieved a remarkable accuracy of 98.5% on the difficult lung disease dataset. The acquired findings and comparisons demonstrate the MixNet-LD system's improved performance and learning capabilities. These findings show that the proposed approach may effectively increase the accuracy of classification models in medicinal image investigations. This research helps to develop new strategies for effective medical image processing in clinical settings.
肺是呼吸系统的关键组成部分，因为它们使我们体内的氧气和二氧化碳得以交换。然而，多种病症会影响肺部，导致严重的健康后果。肺部疾病治疗旨在控制其严重程度，而这种严重程度通常是不可逆转的。这项工作的基本目标是建立一种连贯且自动化的方法来确定肺部疾病的严重程度。本文介绍了MixNet-LD，这是一种独特的自动化方法，旨在使用升级后的预训练MixNet模型来识别和分类肺部疾病的严重程度。开发MixNet-LD系统的首要步骤之一是构建一种预处理策略，该策略使用梯度加权类激活映射（Grad-Cam）来减少噪声、突出异常，最终提高肺部疾病的分类性能。数据增强策略被用于纠正数据集中类别分布不均衡的问题，并防止过拟合。此外，密集块被用于改善肺部疾病四个严重程度类别的分类结果。在实际应用中，MixNet-LD模型在保持模型大小和可管理复杂度的同时，实现了前沿的性能。所提出的方法使用了从可靠互联网来源收集的各种数据集以及一个名为Pak-Lungs的新型私有数据集进行测试。在数据集上使用预训练模型以从肺部疾病图像中获取重要特征。然后使用具有线性激活函数的支持向量机（SVM）分类器的线性层将这些图片分类为正常、新冠肺炎、肺炎、肺结核和肺癌等类别。MixNet-LD系统在四项不同测试中进行了测试，在困难的肺部疾病数据集上达到了98.5%的显著准确率。所获得的结果和比较证明了MixNet-LD系统的改进性能和学习能力。这些结果表明，所提出的方法可以有效地提高医学图像研究中分类模型的准确性。这项研究有助于开发临床环境中有效医学图像处理的新策略。
