Equivalent Relationship of Feedforward Neural Networks and Real-Time Face Detection System

Background: Valvular heart disease is a serious disease leading to mortality and increasing medical care cost. The aortic valve is the most common valve affected by this disease. Doctors rely on echocardiogram for diagnosing and evaluating valvular heart disease. However, the images from echocardiogram are poor in comparison to Computerized Tomography and Magnetic Resonance Imaging scan. This study proposes the development of Convolutional Neural Networks (CNN) that can function optimally during a live echocardiographic examination for detection of the aortic valve. An automated detection system in an echocardiogram will improve the accuracy of medical diagnosis and can provide further medical analysis from the resulting detection. Methods: Two detection architectures, Single Shot Multibox Detector (SSD) and Faster Regional based Convolutional Neural Network (R-CNN) with various feature extractors were trained on echocardiography images from 33 patients. Thereafter, the models were tested on 10 echocardiography videos. Results: Faster R-CNN Inception v2 had shown the highest accuracy (98.6%) followed closely by SSD Mobilenet v2. In terms of speed, SSD Mobilenet v2 resulted in a loss of 46.81% in framesper- second (fps) during real-time detection but managed to perform better than the other neural network models. Additionally, SSD Mobilenet v2 used the least amount of Graphic Processing Unit (GPU) but the Central Processing Unit (CPU) usage was relatively similar throughout all models. Conclusion: Our findings provide a foundation for implementing a convolutional detection system to echocardiography for medical purposes.

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A Novel Real-Time Face Detection System Using Modified Affine Transformation and Haar Cascades

Advances in Intelligent Systems and Computing - Recent Findings in Intelligent Computing Techniques ◽

10.1007/978-981-10-8639-7_20 ◽

2018 ◽

pp. 193-204 ◽

Cited By ~ 1

Author(s):

Rohit Sharma ◽

T. S. Ashwin ◽

Ram Mohana Reddy Guddeti

Keyword(s):

Real Time ◽

Face Detection ◽

Affine Transformation ◽

Detection System

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Real Time Object Detection Canbe Embedded on Low Powered Devices

10.31219/osf.io/t3gdy ◽

2019 ◽

Author(s):

Jimut Bahan Pal

Keyword(s):

Neural Networks ◽

Object Detection ◽

Open Source ◽

Real Time ◽

Detection System ◽

Computing Power ◽

Convolution Neural Networks ◽

Real Challenge ◽

New Algorithms

It has been a real challenge for computers with low computing power and memory to detect objects in real time. After the invention of Convolution Neural Networks (CNN) it is easy for computers to detect images and recognize them. There are several technologies and models which can detect objects in real time, but most of them require high end technologies in terms of GPUs and TPUs. Though, recently many new algorithms and models have been proposed, which runs on low resources. In this paper we studied MobileNets to detect objects using webcam to successfully build a real time objectdetection system. We observed the pre trained model of the famous MS COCO dataset to achieve our purpose. Moreover, we applied Google’s open source TensorFlow as our back end. This real time object detection system may help in future to solve various complex vision problems.

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Robust Real-Time Detection of Laparoscopic Instruments in Robot Surgery Using Convolutional Neural Networks with Motion Vector Prediction

Applied Sciences ◽

10.3390/app9142865 ◽

2019 ◽

Vol 9 (14) ◽

pp. 2865 ◽

Cited By ~ 4

Author(s):

Kyungmin Jo ◽

Yuna Choi ◽

Jaesoon Choi ◽

Jong Woo Chung

Keyword(s):

Neural Networks ◽

Real Time ◽

Convolutional Neural Networks ◽

Detection System ◽

Motion Vector ◽

Detection Algorithm ◽

Surgical Instruments ◽

The Mean ◽

Real Time Detection ◽

Motion Vector Prediction

More than half of post-operative complications can be prevented, and operation performances can be improved based on the feedback gathered from operations or notifications of the risks during operations in real time. However, existing surgical analysis methods are limited, because they involve time-consuming processes and subjective opinions. Therefore, the detection of surgical instruments is necessary for (a) conducting objective analyses, or (b) providing risk notifications associated with a surgical procedure in real time. We propose a new real-time detection algorithm for detection of surgical instruments using convolutional neural networks (CNNs). This algorithm is based on an object detection system YOLO9000 and ensures continuity of detection of the surgical tools in successive imaging frames based on motion vector prediction. This method exhibits a constant performance irrespective of a surgical instrument class, while the mean average precision (mAP) of all the tools is 84.7, with a speed of 38 frames per second (FPS).

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