The exciting field of stretchable electronics (SE) promises numerous novel applications, particularly in-body and medical diagnostics devices. However, future advanced SE miniature devices will require high-density, extremely stretchable interconnects with micron-scale footprints, which calls for proven standardized (complementary metal-oxide semiconductor (CMOS)-type) process recipes using bulk integrated circuit (IC) microfabrication tools and fine-pitch photolithography patterning. Here, we address this combined challenge of microfabrication with extreme stretchability for high-density SE devices by introducing CMOS-enabled, free-standing, miniaturized interconnect structures that fully exploit their 3D kinematic freedom through an interplay of buckling, torsion, and bending to maximize stretchability. Integration with standard CMOS-type batch processing is assured by utilizing the Flex-to-Rigid (F2R) post-processing technology to make the back-end-of-line interconnect structures free-standing, thus enabling the routine microfabrication of highly-stretchable interconnects. The performance and reproducibility of these free-standing structures is promising: an elastic stretch beyond 2000% and ultimate (plastic) stretch beyond 3000%, with <0.3% resistance change, and >10 million cycles at 1000% stretch with <1% resistance change. This generic technology provides a new route to exciting highly-stretchable miniature devices.
White blood cells (WBCs) play a main role in identifying the health condition and disease characteristics of a normal person. An automated classification system is capable of recognizing white blood cells that may help doctors to diagnose several diseases like malaria, anemia, leukemia, etc. Automated blood cell analysis allows fast and accurate outcomes and often involves broad data without performance negotiation. The state-of-the-art systems use a lot of different stages (feature extraction, segmentation, pre-processing, etc.) to provide the automated blood cell analysis using blood smear images which is a lengthy process. To overcome these problems, this paper presents an efficient peripheral blood cell image recognition and classification using a combination of the salp swarm algorithm and the cat swarm optimization (SSPSO) algorithm-based optimized convolutional neural networks (SSPSO-CNN) method. This paper uses the CNN approach to classify five peripheral blood cells such as eosinophil, basophil, lymphocytes, monocytes, and neutrophils without any human intervention. The other objective of this paper is to propose an improved version of salp swarm optimizer (SSO) using particle swarm optimization (PSO) to attain competitive classification performance over the database of the blood cell images. In this paper, the CNN uses VGG19 architecture for training purposes. The accuracy of the classification achieved with VGG19 models is 98%. The proposed model based on the CNN approach optimized by SSPSO achieves high classification accuracy and provides automatic peripheral blood cell classification. This method establishes the fine-tuning process to develop a classifier trained using 10 674 images obtained from medical practice. The proposed method augmented the performance in terms of high precision and [Formula: see text]1-score and obtained an overall classification accuracy of 99%.
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