In healthcare, a multitude of data is collected from medical sensors and devices, such as X-ray machines, magnetic resonance imaging, computed tomography (CT), and so on, that can be analyzed by artificial intelligence methods for early diagnosis of diseases. Recently, the outbreak of the COVID-19 disease caused many deaths. Computer vision researchers support medical doctors by employing deep learning techniques on medical images to diagnose COVID-19 patients. Various methods were proposed for COVID-19 case classification. A new automated technique is proposed using parallel fusion and optimization of deep learning models. The proposed technique starts with a contrast enhancement using a combination of top-hat and Wiener filters. Two pre-trained deep learning models (AlexNet and VGG16) are employed and fine-tuned according to target classes (COVID-19 and healthy). Features are extracted and fused using a parallel fusion approach—parallel positive correlation. Optimal features are selected using the entropy-controlled firefly optimization method. The selected features are classified using machine learning classifiers such as multiclass support vector machine (MC-SVM). Experiments were carried out using the Radiopaedia database and achieved an accuracy of 98%. Moreover, a detailed analysis is conducted and shows the improved performance of the proposed scheme.

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Three morphologies of martensite in dual-phase microstructure of 0.2% C steel were obtained by different heat treatment cycles. These morphologies consisting of grain boundary growth, scattered laths, and bulk form of martensite have their distinct patterns of distribution in the matrix (ferrite). In tensile testing martensite particles with these distributions behaved differently. A reasonable work hardening was gained initially during plastic deformation of the specimens. The control on ductility was found to depend on the alignment of martensite particles along the tensile axes. The increased surface area contact of martensite particles with ferrite, in grain boundary growth and scattered lath morphologies, facilitated stress transfer from ductile to hard phase. The ductility in the later part of deformation was dependent on the density of microvoids in the necked region. The microvoids are formed mostly by de-cohesion of martensite particles at the interface. The fracture of martensite particles is less prominent in the process of microvoid formation which predicts high strength of martensite.

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