Medical vision: web and mobile medical image retrieval system based on google cloud vision

Putra, I Ketut Gede Darma; Asra, Dewa Made Sri; Hardijaya, I Gusti Ngurah Dwiva; Prabawa, I Gede Galang Surya; Widiatmika, I Made Aris Satia

doi:10.11591/ijece.v10i6.pp5974-5984

Cited by 2 publications

(1 citation statement)

References 34 publications

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“…It was discovered that Google Cloud Vision and Tesseract OCR achieved accuracies of 84.43% and 47.02%, respectively. Similarly, Google Cloud Vision was proposed to build a vision system for people with no medical expertise [11]. The experimental results revealed a promising performance of the proposed technology as it can provide pertinent information about the given image.…”

Section: Introductionmentioning

confidence: 99%

Cloud-Based Architecture for YOLOv3 Object Detector using gRPC and Protobuf

Jamzuri

Mandala

Analia

et al. 2022

Jurnal Teknik Elektro

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The deep learning-based object detector accuracy has surpassed conventional detection methods. Although implementation is still limited to hardware capabilities, this problem can be overcome by combining edge devices with cloud computing. The recent study of cloud-based object detector architecture is generally based on representational state transfer (RESTful web services), which uses a pooling system method for data exchange. As a result, this system leads to a low detection speed and cannot support real-time data streaming. Therefore, this study aims to enhance the detection speed in cloud-based object recognition systems using gRPC and Protobuf to support real-time detection. The proposed architecture was deployed on the Virtual Machine Instance (VMI) equipped with a Graphics Processing Unit (GPU). The gRPC server and YOLOv3 deep learning object detector were executed on the cloud server to handle detection requests from edge devices. Furthermore, the captured images from the edge devices were encoded into Protobuf format to reduce the message size delivered to the cloud server. The results showed that the proposed architecture improved detection speed performance on the client-side in the range of 0.27 FPS to 1.72 FPS compared to the state-of-the-art method. It was also observed that it could support multiple edge devices connection with slight performance degradation in the range of 1.78 FPS to 1.83 FPS, depending on the network interface used.

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Section: Introductionmentioning

confidence: 99%