Artificial Intelligence Medical Ultrasound Equipment: Application of Breast Lesions Detection

2020 ◽  
Vol 42 (4-5) ◽  
pp. 191-202 ◽  
Author(s):  
Xuesheng Zhang ◽  
Xiaona Lin ◽  
Zihao Zhang ◽  
Licong Dong ◽  
Xinlong Sun ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Real Time ◽  
High Precision ◽  
Practical Significance ◽  
Breast Lesions ◽  
Medical Ultrasound ◽  
Network Sensitivity ◽  
Computational Capability ◽  
Knowledge Distillation

Breast cancer ranks first among cancers affecting women’s health. Our work aims to realize the intelligence of the medical ultrasound equipment with limited computational capability, which is used for the assistant detection of breast lesions. We embed the high-computational deep learning algorithm into the medical ultrasound equipment with limited computational capability by two techniques: (1) lightweight neural network: considering the limited computational capability of ultrasound equipment, a lightweight neural network is designed, which greatly reduces the amount of calculation. And we use the technique of knowledge distillation to train the low-precision network helped with the high-precision network; (2) asynchronous calculations: consider four frames of ultrasound images as a group; the image of the first frame of each group is used as the input of the network, and the result is respectively fused with the images of the fourth to seventh frames. An amount of computation of 30 GFLO/frame is required for the proposed lightweight neural network, about 1/6 of that of the large high-precision network. After trained from scratch using the knowledge distillation technique, the detection performance of the lightweight neural network (sensitivity = 89.25%, specificity = 96.33%, the average precision [AP] = 0.85) is close to that of the high-precision network (sensitivity = 98.3%, specificity = 88.33%, AP = 0.91). By asynchronous calculation, we achieve real-time automatic detection of 24 fps (frames per second) on the ultrasound equipment. Our work proposes a method to realize the intelligence of the low-computation-power ultrasonic equipment, and successfully achieves the real-time assistant detection of breast lesions. The significance of the study is as follows: (1) The proposed method is of practical significance in assisting doctors to detect breast lesions; (2) our method provides some practical and theoretical support for the development and engineering of intelligent equipment based on artificial intelligence algorithms.

scholarly journals Energy-efficient memcapacitor devices for neuromorphic computing

2021 ◽  
Author(s):  
Kai-Uwe Demasius ◽  
Aron Kirschen ◽  
Stuart Parkin
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Energy Efficiency ◽  
High Precision ◽  
Energy Efficient ◽  
Lateral Size ◽  
Data Intensive Computing ◽  
Data Intensive ◽  
Specialized Hardware

AbstractData-intensive computing operations, such as training neural networks, are essential for applications in artificial intelligence but are energy intensive. One solution is to develop specialized hardware onto which neural networks can be directly mapped, and arrays of memristive devices can, for example, be trained to enable parallel multiply–accumulate operations. Here we show that memcapacitive devices that exploit the principle of charge shielding can offer a highly energy-efficient approach for implementing parallel multiply–accumulate operations. We fabricate a crossbar array of 156 microscale memcapacitor devices and use it to train a neural network that could distinguish the letters ‘M’, ‘P’ and ‘I’. Modelling these arrays suggests that this approach could offer an energy efficiency of 29,600 tera-operations per second per watt, while ensuring high precision (6–8 bits). Simulations also show that the devices could potentially be scaled down to a lateral size of around 45 nm.

scholarly journals Wearable Airbag System for Real-Time Bicycle Rider Accident Recognition by Orthogonal Convolutional Neural Network (O-CNN) Model

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1423
Author(s):  
Joo Woo ◽  
So-Hyeon Jo ◽  
Gi-Sig Byun ◽  
Baek-Soon Kwon ◽  
Jae-Hoon Jeong
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Neck Injuries ◽  
Judgment Accuracy ◽  
Time Motion ◽  
Motion Condition ◽  
Bicycle Rider ◽  
Accident Conditions

As demand for bicycles increases, bicycle-related accidents are on the rise. There are many items such as helmets and racing suits for bicycles, but many people do not wear helmets even if they are the most basic safety protection. To protect the rider from accidents, technology is needed to measure the rider’s motion condition in real time, determine whether an accident has occurred, and cope with the accident. This paper describes an artificial intelligence airbag. The artificial intelligence airbag is a system that measures real-time motion conditions of a bicycle rider using a six-axis sensor and judges accidents with artificial intelligence to prevent neck injuries. The MPU 6050 is used to understand changes in the rider’s movement in normal and accident conditions. The angle is determined by using the measured data and artificial intelligence to determine whether an accident happened or not by analyzing acceleration and angle. In this paper, similar methods of artificial intelligence (NN, PNN, CNN, PNN-CNN) to are compared to the orthogonal convolutional neural network (O-CNN) method in terms of the performance of judgment accuracy for accident situations. The artificial neural networks were applied to the airbag system and verified the reliability and judgment in advance.

scholarly journals Artificial Intelligence (AI) Framework for Multi-Modal Learning and Decision Making towards Autonomous and Electric Vehicles

2021 ◽  
Vol 309 ◽  
pp. 01167
Author(s):  
G. Ramesh ◽  
J. Praveen
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Path Planning ◽  
Real Time ◽  
Power Management ◽  
Transfer Learning ◽  
Electric Vehicles ◽  
Recurrent Neural Network ◽  
Autonomous Driving

An electric vehicle with autonomous driving is a possibility provided technology innovations in multi-disciplinary approach. Electric vehicles leverage environmental conditions and are much desired in the contemporary world. Another great possibility is to strive for making the vehicle to drive itself (autonomous driving) provided instructions. When the two are combined, it leads to a different dimension of environmental safety and technology driven driving that has many pros and cons as well. It is still in its infancy and there is much research to be carried out. In this context, this paper is aimed at building an Artificial Intelligence (AI) framework that has dual goal of “monitoring and regulating power usage” and facilitating autonomous driving with technology-driven and real time knowledge required. A methodology is proposed with multiple deep learning methods. For instance, deep learning is used for localization of vehicle, path planning at high level and path planning for low level. Apart from this, there is reinforcement learning and transfer learning to speed up the process of gaining real time business intelligence. To facilitate real time knowledge discovery from given scenarios, both edge and cloud resources are appropriately exploited to benefit the vehicle as driving safety is given paramount importance. There is power management module where modular Recurrent Neural Network is used. Another module known as speed control is used to have real time control over the speed of the vehicle. The usage of AI framework makes the electronic and autonomous vehicles realize unprecedented possibilities in power management and safe autonomous driving. Key words: Artificial Intelligence Autonomous Driving Recurrent Neural Network Transfer Learning

scholarly journals Investigasi Pengaruh Step Training pada Metode Single Shot Multibox Detector untuk Marker dalam Teknologi Augmented Reality

2020 ◽  
Vol 12 (1) ◽  
pp. 1
Author(s):  
Vivian Alfionita Sutama ◽  
Suryo Adhi Wibowo ◽  
Rissa Rahmania
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Augmented Reality ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Transfer Learning ◽  
Learning Rate ◽  
Batch Size ◽  
Single Shot

Nowadays, Artificial Intelligence is one of the most developing technology, especially on Augmented Reality (AR). AR is a technology which connected between real world and virtual in a real time that allows user to interact directly and display it in 3D. AR technology has two methods, that are AR based on marker and AR based on markerless. However, AR based on marker need an object detection system which has high performance as an interaction tools between user and the device. Single shot multibox detector (SSD) is an object detection algorithm that has fast learning computation and good performance. This method is affected by some parameters like number of epoch, learning rate, batch size, step training, etc. However, to create a good system it took a long process such as taking dataset, labelling process, then training and testing models to gain the best performance. In this experiment, we analyze SSD method in AR technology using inception architecture as pre-trained Convolutional neural network (CNN), and then do transfer learning to minimize amount training time. The configuration that used is the number of step training. The result of this experiment gets the best accuracy in 70.17%. Then, the best performance is used as an object detection model for marker’s AR technology.Abstrak Saat ini, Artificial intelligence merupakan teknologi yang sedang berkembang pesat. Salah satunya adalah teknologi Augmented Reality (AR). AR adalah teknologi yang menggabungkan dunia nyata dengan virtual secara real-time dengan interaksi pengguna secara langsung dan menampilkannya dalam bentuk 3D. Teknologi AR ini memiliki dua metode yaitu dengan marker dan markerless. Dalam perkembangannya, AR berbasis marker membutuhkan sistem deteksi objek yang memiliki performa tinggi sebagai alat interaksi antara pengguna dengan perangkatnya. Single shot multibox detector (SSD) merupakan algoritma deteksi objek yang memiliki komputasi pembelajaran dan kinerja yang baik. Metode ini dipengaruhi oleh beberapa parameter seperti jumlah lapisan konvolusi, epoch, learning rate, jumlah batch, step training, dll. Namun, dalam mengimplementasikannya diperlukan proses yang cukup panjang seperti, pengambilan dataset, proses pelabelan, proses pelatihan menggunakan metode SSD, dan melakukan pengujian terhadap beberapa model untuk mencari perfomansi paling baik. Dalam percobaan ini, kami melakukan analisis terhadap metode SSD pada teknologi AR menggunakan arsitektur Inception sebagai pre-trained Convolutional neural network (CNN), kemudian dilakukan transfer learning untuk memperkecil jumlah kelas data pelatihan dan waktu pelatihan data. Konfigurasi yang digunakan berupa jumlah step pada pelatihan. Hasil dari penilitian ini menunjukan akurasi terbaik sebesar 70,17%. Kemudian, perfomansi terbaik digunakan sebagai model deteksi objek untuk marker pada teknologi AR.

scholarly journals Real-Time Pattern-Recognition of GPR Images with YOLO v3 Implemented by Tensorflow

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6476
Author(s):  
Yuanhong Li ◽  
Zuoxi Zhao ◽  
Yangfan Luo ◽  
Zhi Qiu
Keyword(s):  
Artificial Intelligence ◽  
Pattern Recognition ◽  
Real Time ◽  
High Precision ◽  
Electromagnetic Waves ◽  
Superior Performance ◽  
Experimental Result ◽  
Single Shot ◽  
Time Pattern ◽  
Bounding Boxes

Artificial intelligence (AI) is widely used in pattern recognition and positioning. In most of the geological exploration applications, it needs to locate and identify underground objects according to electromagnetic wave characteristics from the ground-penetrating radar (GPR) images. Currently, a few robust AI approach can detect targets by real-time with high precision or automation for GPR images recognition. This paper proposes an approach that can be used to identify parabolic targets with different sizes and underground soil or concrete structure voids based on you only look once (YOLO) v3. With the TensorFlow 1.13.0 developed by Google, we construct YOLO v3 neural network to realize real-time pattern recognition of GPR images. We propose the specific coding method for the GPR image samples in Yolo V3 to improve the prediction accuracy of bounding boxes. At the same time, K-means algorithm is also applied to select anchor boxes to improve the accuracy of positioning hyperbolic vertex. For some instances electromagnetic-vacillated signals may occur, which refers to multiple parabolic electromagnetic waves formed by strong conductive objects among soils or overlapping waveforms. This paper deals with the vacillating signal similarity intersection over union (IoU) (V-IoU) methods. Experimental result shows that the V-IoU combined with non-maximum suppression (NMS) can accurately frame targets in GPR image and reduce the misidentified boxes as well. Compared with the single shot multi-box detector (SSD), YOLO v2, and Faster-RCNN, the V-IoU YOLO v3 shows its superior performance even when implemented by CPU. It can meet the real-time output requirements by an average 12 fps detected speed. In summary, this paper proposes a simple and high-precision real-time pattern recognition method for GPR imagery, and promoted the application of artificial intelligence or deep learning in the field of the geophysical science.

scholarly journals Fashion Style Generator

2017 ◽  
Author(s):  
Shuhui Jiang ◽  
Yun Fu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Global Optimization ◽  
Real Time ◽  
Synthetic Image ◽  
Feed Forward Neural Network ◽  
Style Transfer ◽  
Artistic Style ◽  
The Arts ◽  
Optimization Stage

In this paper, we focus on a new problem: applying artificial intelligence to automatically generate fashion style images. Given a basic clothing image and a fashion style image (e.g., leopard print), we generate a clothing image with the certain style in real time with a neural fashion style generator. Fashion style generation is related to recent artistic style transfer works, but has its own challenges. The synthetic image should preserve the similar design as the basic clothing, and meanwhile blend the new style pattern on the clothing. Neither existing global nor patch based neural style transfer methods could well solve these challenges. In this paper, we propose an end-to-end feed-forward neural network which consists of a fashion style generator and a discriminator. The global and patch based style and content losses calculated by the discriminator alternatively back-propagate the generator network and optimize it. The global optimization stage preserves the clothing form and design and the local optimization stage preserves the detailed style pattern. Extensive experiments show that our method outperforms the state-of-the-arts.

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