COVID-19 pandemic is increasing in an exponential rate, with restricted accessibility of rapid test kits. So, the design and implementation of COVID-19 testing kits remain an open research problem. Several findings attained using radio-imaging approaches recommend that the images comprise important data related to coronaviruses. The application of recently developed artificial intelligence (AI) techniques, integrated with radiological imaging, is helpful in the precise diagnosis and classification of the disease. In this view, the current research paper presents a novel fusion model hand-crafted with deep learning features called FM-HCF-DLF model for diagnosis and classification of COVID-19. The proposed FM-HCF-DLF model comprises three major processes, namely Gaussian filtering-based preprocessing, FM for feature extraction and classification. FM model incorporates the fusion of handcrafted features with the help of local binary patterns (LBP) and deep learning (DL) features and it also utilizes convolutional neural network (CNN)-based Inception v3 technique. To further improve the performance of Inception v3 model, the learning rate scheduler using Adam optimizer is applied. At last, multilayer perceptron (MLP) is employed to carry out the classification process. The proposed FM-HCF-DLF model was experimentally validated using chest X-ray dataset. The experimental outcomes inferred that the proposed model yielded superior performance with maximum sensitivity of 93.61%, specificity of 94.56%, precision of 94.85%, accuracy of 94.08%, F score of 93.2% and kappa value of 93.5%.

COVID-19 大流行正以指数速度增长，快速检测试剂盒的可及性受到限制。因此，COVID-19 检测试剂盒的设计和实施仍然是一个开放的研究问题。使用放射成像方法获得的几项发现表明，这些图像包含与冠状病毒相关的重要数据。最近开发的人工智能 (AI) 技术与放射成像相结合的应用有助于疾病的精确诊断和分类。鉴于此，当前的研究论文提出了一种新的融合模型，该模型具有手工制作的深度学习特征，称为 FM-HCF-DLF 模型，用于 COVID-19 的诊断和分类。所提出的 FM-HCF-DLF 模型包括三个主要过程，即基于高斯滤波的预处理、用于特征提取和分类的 FM。FM 模型在局部二进制模式 (LBP) 和深度学习 (DL) 特征的帮助下融合了手工制作的特征，它还利用了基于卷积神经网络 (CNN) 的 Inception v3 技术。为了进一步提高 Inception v3 模型的性能，应用了使用 Adam 优化器的学习率调度器。最后，采用多层感知器（MLP）进行分类过程。使用胸部 X 射线数据集对所提出的 FM-HCF-DLF 模型进行了实验验证。实验结果推断，所提出的模型产生了优越的性能，最大灵敏度为 93.61%，特异性为 94.56%，精度为 94.85%，准确度为 94.08%，应用了使用 Adam 优化器的学习率调度器。最后，采用多层感知器（MLP）进行分类过程。使用胸部 X 射线数据集对所提出的 FM-HCF-DLF 模型进行了实验验证。实验结果推断，所提出的模型产生了优越的性能，最大灵敏度为 93.61%，特异性为 94.56%，精度为 94.85%，准确度为 94.08%，应用了使用 Adam 优化器的学习率调度器。最后，采用多层感知器（MLP）进行分类过程。使用胸部 X 射线数据集对所提出的 FM-HCF-DLF 模型进行了实验验证。实验结果推断，所提出的模型产生了优越的性能，最大灵敏度为 93.61%，特异性为 94.56%，精度为 94.85%，准确度为 94.08%，F分数为 93.2%，kappa 值为 93.5%。