An artificial intelligent diagnostic system on mobile Android terminals for cholelithiasis by lightweight convolutional neural network

Artificial intelligence (AI) tools have been applied to diagnose or predict disease risk from medical images with recent data disclosure actions, but few of them are designed for mobile terminals due to the limited computational power and storage capacity of mobile devices. In this work, a novel AI diagnostic system is proposed for cholelithiasis recognition on mobile devices with Android platform. To this aim, a data set of CT images of cholelithiasis is firstly collected from The Third Hospital of Shandong Province, China, and then we technically use histogram equalization to preprocess these CT images. As results, a lightweight convolutional neural network is obtained in a constructive way to extract cholelith features and recognize gallstones. In terms of implementation, we compile Java and C++ to adapt to the application of deep learning algorithm on mobile devices with Android platform. Noted that, the training task is completed offline on PC, but cholelithiasis recognition tasks are performed on mobile terminals. We evaluate and compare the performance of our MobileNetV2 with MobileNetV1, Single Shot Detector (SSD), YOLOv2 and original SSD (with VGG-16) as feature extractors for object detection. It is achieved that our MobileNetV2 achieve similar accuracy rate, about 91% with the other four methods, but the number of parameters used is reduced from 36.1M (SSD 300, SSD512), 50.7M (Yolov2) and 5.1M (MobileNetV1) to 4.3M (MobileNetV2). The complete process on testing mobile devices, including Virtual machine, Xiaomi 7 and Htc One M8 can be controlled within 4 seconds in recognizing cholelithiasis as well as the degree of the disease.

[1]  S. Kanmani,et al.  Self-adaptive agent-based tutoring system , 2016 .

[2]  Salvatore Vitabile,et al.  An edge-driven 3D region growing approach for upper airways morphology and volume evaluation in patients with Pierre Robin sequence , 2016 .

[3]  Xun Wang,et al.  Small Universal Bacteria and Plasmid Computing Systems , 2018, Molecules.

[4]  Song Han,et al.  Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding , 2015, ICLR.

[5]  Volker Schmid,et al.  Pattern Recognition and Signal Analysis in Medical Imaging , 2003 .

[6]  Hai Van Pham,et al.  A knowledge-based consultancy system using ICT Newhouse indicators with reasoning techniques for consultants in e-learning , 2016 .

[7]  Ronald L. Hoffman.  Intelligent Medicine : A Guide to Optimizing Health and Preventing Illness for the Baby-Boomer Generation , 1997 .

[8]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[9]  Pan Zheng,et al.  Multi-Objective Evolutionary Algorithm Based on Decomposition for Energy-aware Scheduling in Heterogeneous Computing Systems , 2017, J. Univers. Comput. Sci..

[10]  Alfonso Rodríguez-Patón,et al.  A Parallel Image Skeletonizing Method Using Spiking Neural P Systems with Weights , 2018, Neural Processing Letters.

[11]  Gordon Fischer,et al.  The Linux Kernel Primer: A Top-Down Approach for x86 and PowerPC Architectures , 2005 .

[12]  Volker Schmid,et al.  Chapter 5 – Genetic Algorithms , 2014 .

[13]  Bram van Ginneken,et al.  A survey on deep learning in medical image analysis , 2017, Medical Image Anal..

[14]  Yeong-Taeg Kim,et al.  Contrast enhancement using brightness preserving bi-histogram equalization , 1997 .

[15]  Xun Wang,et al.  Design of logic gates using spiking neural P systems with homogeneous neurons and astrocytes-like control , 2016, Inf. Sci..

[16]  Pan Zheng,et al.  Spiking Neural P Systems With Learning Functions , 2019, IEEE Transactions on NanoBioscience.

[17]  Wei Liu,et al.  SSD: Single Shot MultiBox Detector , 2015, ECCV.

[18]  Dawei Li,et al.  DeepRebirth: Accelerating Deep Neural Network Execution on Mobile Devices , 2017, AAAI.

[19]  Xiangxiang Zeng,et al.  Spiking Neural P Systems With Colored Spikes , 2018, IEEE Transactions on Cognitive and Developmental Systems.

[20]  Forrest N. Iandola,et al.  SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size , 2016, ArXiv.

[21]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[22]  Alfonso Rodríguez-Patón,et al.  A Parallel Bioinspired Framework for Numerical Calculations Using Enzymatic P System With an Enzymatic Environment , 2018, IEEE Access.

[23]  Johan Lundin,et al.  Medical mobile technologies – what is needed for a sustainable and scalable implementation on a global scale? , 2017, Global health action.

[24]  Xiangyu Zhang,et al.  ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[25]  Xun Wang,et al.  A programming triangular DNA origami for doxorubicin loading and delivering to target ovarian cancer cells , 2017 .

[26]  Xingyi Zhang,et al.  Spiking Neural P Systems With White Hole Neurons , 2016, IEEE Transactions on NanoBioscience.

[27]  Baochang Zhang,et al.  Local Derivative Pattern Versus Local Binary Pattern: Face Recognition With High-Order Local Pattern Descriptor , 2010, IEEE Transactions on Image Processing.

[28]  Sérgio Matos,et al.  Pattern recognition for cache management in distributed medical imaging environments , 2016, International Journal of Computer Assisted Radiology and Surgery.

[29]  Harald Essig,et al.  Model-based segmentation in orbital volume measurement with cone beam computed tomography and evaluation against current concepts , 2015, International Journal of Computer Assisted Radiology and Surgery.

[30]  Ali Farhadi,et al.  YOLO9000: Better, Faster, Stronger , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[31]  Bo Chen,et al.  MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.

[32]  AI diagnostics need attention , 2018, Nature.

[33]  Jih-Sheng Shen,et al.  Reconfigurable Multi-core Architecture -- A Plausible Solution to the Von Neumann Performance Bottleneck , 2013, 2013 IEEE 7th International Symposium on Embedded Multicore Socs.

[34]  François Chollet,et al.  Xception: Deep Learning with Depthwise Separable Convolutions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[35]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[36]  Xiangxiang Zeng,et al.  A Parallel Workflow Pattern Modeling Using Spiking Neural P Systems With Colored Spikes , 2018, IEEE Transactions on NanoBioscience.