Studying gene expression profile in a single cancer cell is important because multiple genes are associated with cancer development. Quantum dots (QDs) have been utilized as biological probes for imaging and detection. QDs display specific optical and electrical properties that depend on their size that can be applied for imaging and sensing applications. In this study, simultaneous imaging of the cancer biomarkers, tenascin-C and nucleolin, was performed using two types of aptamer-conjugated QDs. The simultaneous imaging of these two different cancer markers in three cancer cell lines was reliable and cell line-specific. Current requirements for cancer imaging technologies include the need for simple preparation methods and the ability to detect multiple cancer biomarkers and evaluate their intracellular localizations. The method employed in this study is a feasible solution to these requirements.
Convolutional neural networks (CNNs) have been recently applied to tackle a variety of computer vision problems. However, because of its high computational cost, careful considerations are required to design cost-effective CNNs. In this paper, we propose a CNN inspired by MobileNet for fire detection in surveillance systems. In the proposed network, color features emphasized by the channel multiplier are extracted through depthwise separable convolution, and squeeze and excitation modules further increase the representation of the channel-wise convolution. Custom Swish is used as an activation function to limit exceedingly high weights from the effects of the channel multiplier. Our proposed network achieves 95.44% accuracy for fire detection, which is higher than those achieved other existing networks. Furthermore, the number of parameters used is 38.50% fewer than that of MobileNetV2, the smallest among other networks. We believe that using the proposed CNN, CNN-based surveillance systems could be implemented in lightweight devices without using expensive dedicated processors. INDEX TERMS Fire detection, deep learning, convolutional neural networks, image classification.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.