PDF(Pubmed)
Abstract：
Medicine is one of the fields where the advancement of computer science is making significant progress. Some diseases require an immediate diagnosis in order to improve patient outcomes. The usage of computers in medicine improves precision and accelerates data processing and diagnosis. In order to categorize biological images, hybrid machine learning, a combination of various deep learning approaches, was utilized, and a meta-heuristic algorithm was provided in this research. In addition, two different medical datasets were introduced, one covering the magnetic resonance imaging (MRI) of brain tumors and the other dealing with chest X-rays (CXRs) of COVID-19. These datasets were introduced to the combination network that contained deep learning techniques, which were based on a convolutional neural network (CNN) or autoencoder, to extract features and combine them with the next step of the meta-heuristic algorithm in order to select optimal features using the particle swarm optimization (PSO) algorithm. This combination sought to reduce the dimensionality of the datasets while maintaining the original performance of the data. This is considered an innovative method and ensures highly accurate classification results across various medical datasets. Several classifiers were employed to predict the diseases. The COVID-19 dataset found that the highest accuracy was 99.76% using the combination of CNN-PSO-SVM. In comparison, the brain tumor dataset obtained 99.51% accuracy, the highest accuracy derived using the combination method of autoencoder-PSO-KNN.
摘要：
医学是计算机科学进步取得重大进展的领域之一。一些疾病需要立即诊断以改善患者预后。计算机在医学中的使用提高了精度并加速了数据处理和诊断。为了对生物图像进行分类，混合机器学习，各种深度学习方法的组合，被利用，并在本研究中提供了元启发式算法。此外,引入了两个不同的医疗数据集，一个涉及脑肿瘤的磁共振成像（MRI），另一个涉及COVID-19的胸部X射线（CXRs）。这些数据集被引入到包含深度学习技术的组合网络中，它们基于卷积神经网络(CNN)或自动编码器，提取特征，并与下一步的元启发式算法相结合，以便使用粒子群优化（PSO）算法选择最优特征。这种组合试图降低数据集的维度，同时保持数据的原始性能。这被认为是一种创新的方法，可确保各种医疗数据集的高度准确的分类结果。采用几种分类器来预测疾病。COVID-19数据集发现，使用CNN-PSO-SVM组合的最高准确率为99.76%。相比之下,脑肿瘤数据集获得99.51%的准确率，使用自动编码器-PSO-KNN组合方法得出的最高精度。
