Disease detection on medical images using light-weight Convolutional Neural Networks for resource constrained platforms

Monitoring environmental conditions is an important application of cyber-physical systems. Typically, the monitoring is to perceive surrounding environments with battery-powered, tiny devices deployed in the field. While deep learning-based methods, especially the convolutional neural networks (CNNs), are promising approaches to enriching the functionalities offered by the tiny devices, they demand more computation and memory resources, which makes these methods difficult to be adopted on such devices. In this article, we develop a software framework, RAP , that permits the construction of the CNN designs by aggregating the existing, lightweight CNN layers, which are able to fit in the limited memory (e.g., several KBs of SRAM) on the resource-constrained devices satisfying application-specific timing constrains. RAP leverages the Python-based neural network framework Chainer to build the CNNs by mounting the C/C++ implementations of the lightweight layers, trains the built CNN models as the ordinary model-training procedure in Chainer, and generates the C version codes of the trained models. The generated programs are compiled into target machine executables for the on-device inferences. With the vigorous development of lightweight CNNs, such as binarized neural networks with binary weights and activations, RAP facilitates the model building process for the resource-constrained devices by allowing them to alter, debug, and evaluate the CNN designs over the C/C++ implementation of the lightweight CNN layers. We have prototyped the RAP framework and built two environmental monitoring applications for protecting endangered species using image- and acoustic-based monitoring methods. Our results show that the built model consumes less than 0.5 KB of SRAM for buffering the runtime data required by the model inference while achieving up to 93% of accuracy for the acoustic monitoring with less than one second of inference time on the TI 16-bit microcontroller platform.

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Plant Leaf Disease Detection using Convolutional Neural Networks

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2020.32205 ◽

2020 ◽

Vol 8 (11) ◽

pp. 494-499

Author(s):

Siddhesh B Gharpankar

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Disease Detection ◽

Plant Leaf ◽

Leaf Disease

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Convolutional Neural Networks-based plant disease detection implemented on low-power consumption device

10.1109/ropec53248.2021.9668160 ◽

2021 ◽

Author(s):

Eduardo A. Huerta Mora ◽

Victor Gonzalez-Huitron ◽

A.E. Rodriguez-Mata ◽

Hector Rodriguez Rangel

Keyword(s):

Neural Networks ◽

Power Consumption ◽

Low Power ◽

Convolutional Neural Networks ◽

Plant Disease ◽

Low Power Consumption ◽

Disease Detection

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Investigation of Convolutional Neural Networks in the Tasks of Medical Images Analysis and Classification of Breast Tumors

10.1109/cisp-bmei53629.2021.9624326 ◽

2021 ◽

Author(s):

Yuriy Zaychenko ◽

Helen Zaichenko ◽

Galib Hamidov

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Medical Images ◽

Breast Tumors

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Plant Disease Detection Using Convolutional Neural Networks and Remedy Recommendation System

Advances in Intelligent Systems and Computing - Emerging Technologies in Data Mining and Information Security ◽

10.1007/978-981-15-9927-9_57 ◽

2021 ◽

pp. 587-595

Author(s):

P. A. Keerthana ◽

B. Babu Nayana ◽

R. Neeraja Krishna ◽

S. Sheba ◽

P. S. Deepthi

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Plant Disease ◽

Recommendation System ◽

Disease Detection

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Plant Disease Detection Using Deep Learning (Convolutional Neural Networks)

10.1007/978-3-030-84760-9_54 ◽

2021 ◽

pp. 635-649

Author(s):

Nidhi Prashar ◽

A. L. Sangal

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Plant Disease ◽

Disease Detection

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Slim and Efficient Neural Network Design for Resource-Constrained SAR Target Recognition

Remote Sensing ◽

10.3390/rs10101618 ◽

2018 ◽

Vol 10 (10) ◽

pp. 1618 ◽

Cited By ~ 14

Author(s):

Hongyi Chen ◽

Fan Zhang ◽

Bo Tang ◽

Qiang Yin ◽

Xian Sun

Keyword(s):

Neural Networks ◽

Real Time ◽

Convolutional Neural Networks ◽

Target Recognition ◽

Practical Implementation ◽

Network Storage ◽

Specific Situation ◽

Resource Constrained ◽

Adaptive Architecture ◽

Deep Cnn

Deep convolutional neural networks (CNN) have been recently applied to synthetic aperture radar (SAR) for automatic target recognition (ATR) and have achieved state-of-the-art results with significantly improved recognition performance. However, the training period of deep CNN is long, and the size of the network is huge, sometimes reaching hundreds of megabytes. These two factors of deep CNN hinders its practical implementation and deployment in real-time SAR platforms that are typically resource-constrained. To address this challenge, this paper presents three strategies of network compression and acceleration to decrease computing and memory resource dependencies while maintaining a competitive accuracy. First, we introduce a new weight-based network pruning and adaptive architecture squeezing method to reduce the network storage and the time of inference and training process, meanwhile maintain a balance between compression ratio and classification accuracy. Then we employ weight quantization and coding to compress the network storage space. Due to the fact that the amount of calculation is mainly reflected in the convolution layer, a fast approach for pruned convolutional layers is proposed to reduce the number of multiplication by exploiting the sparsity in the activation inputs and weights. Experimental results show that the convolutional neural networks for SAR-ATR can be compressed by 40 × without loss of accuracy, and the number of multiplication can be reduced by 15 × . Combining these strategies, we can easily load the network in resource-constrained platforms, speed up the inference process to get the results in real-time or even retrain a more suitable network with new image data in a specific situation.

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BE-FNet: 3D Bounding Box Estimation Feature Pyramid Network for Accurate and Efficient Maxillary Sinus Segmentation

Mathematical Problems in Engineering ◽

10.1155/2020/5689301 ◽

2020 ◽

Vol 2020 ◽

pp. 1-16

Author(s):

Zhuofu Deng ◽

Binbin Wang ◽

Zhiliang Zhu

Keyword(s):

Neural Networks ◽

Maxillary Sinus ◽

Convolutional Neural Networks ◽

Feature Fusion ◽

Medical Images ◽

Medical Image Segmentation ◽

3D Segmentation ◽

Deep Convolutional Neural Networks ◽

Bounding Box ◽

Proposed Model

Maxillary sinus segmentation plays an important role in the choice of therapeutic strategies for nasal disease and treatment monitoring. Difficulties in traditional approaches deal with extremely heterogeneous intensity caused by lesions, abnormal anatomy structures, and blurring boundaries of cavity. 2D and 3D deep convolutional neural networks have grown popular in medical image segmentation due to utilization of large labeled datasets to learn discriminative features. However, for 3D segmentation in medical images, 2D networks are not competent in extracting more significant spacial features, and 3D ones suffer from unbearable burden of computation, which results in great challenges to maxillary sinus segmentation. In this paper, we propose a deep neural network with an end-to-end manner to generalize a fully automatic 3D segmentation. At first, our proposed model serves a symmetrical encoder-decoder architecture for multitask of bounding box estimation and in-region 3D segmentation, which cannot reduce excessive computation requirements but eliminate false positives remarkably, promoting 3D segmentation applied in 3D convolutional neural networks. In addition, an overestimation strategy is presented to avoid overfitting phenomena in conventional multitask networks. Meanwhile, we introduce residual dense blocks to increase the depth of the proposed network and attention excitation mechanism to improve the performance of bounding box estimation, both of which bring little influence to computation cost. Especially, the structure of multilevel feature fusion in the pyramid network strengthens the ability of identification to global and local discriminative features in foreground and background achieving more advanced segmentation results. At last, to address problems of blurring boundary and class imbalance in medical images, a hybrid loss function is designed for multiple tasks. To illustrate the strength of our proposed model, we evaluated it against the state-of-the-art methods. Our model performed better significantly with an average Dice 0.947±0.031, VOE 10.23±5.29, and ASD 2.86±2.11, respectively, which denotes a promising technique with strong robust in practice.

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