MobiHisNet: A Lightweight CNN in Mobile Edge Computing for Histopathological Image Classification

Kumar, Abhinav; Sharma, Anshul; Bharti, Vandana; Singh, Amit Kumar; Singh, Sanjay Kumar; Saxena, Sonal

doi:10.1109/jiot.2021.3119520

Cited by 37 publications

(16 citation statements)

References 33 publications

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“…The Inception-v1 can achieve the test accuracy of 89%, 92%, 94%, and 90%, respectively, at 40×, 100×, 200× and 400× magnification factor classification. The approach of [ 59 ] proposed an efficient and lightweight CNN model for histopathological image classification based on MobileNet. It achieves the test accuracy of 91.42%, 89.93%, 92.70%, and 85.84%, respectively, at 40×, 100×, 200× and 400× magnification factor classification.…”

Section: Resultsmentioning

confidence: 99%

A Parallel Spiking Neural Network Based on Adaptive Lateral Inhibition Mechanism for Objective Recognition

Dong

2022

Computational Intelligence and Neuroscience

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Spiking neural network (SNN) has attracted extensive attention in the field of machine learning because of its biological interpretability and low power consumption. However, the accuracy of pattern recognition cannot completely surpass deep neural networks (DNNs). The main reason is that the inherent nondifferentiability of spiking neurons makes SNN unable to be trained directly by the gradient descent algorithm, and there is also no unified training algorithm for SNN. Inspired by the biological vision system, this paper proposes a parallel convolution SNN structure combined with an adaptive lateral inhibition mechanism. And, a way of dynamically evolving the time constant with the training of SNN is proposed to ensure the diversity of neurons. This paper verifies the effectiveness of the proposed methods on static datasets and neuromorphic datasets and extends it to the recognition of breast tumors. Experimental results show that the SNN has obvious advantages in dynamical datasets. For breast tumors, it is also an edge-based task, because the edge of a medical image contains the most important information in the image. This kind of information can provide great help for the noninvasive and accurate diagnosis of diseases. The Experimental results show that the proposed method is very close to the recognition results of DNNs on static datasets, and its performance on neuromorphic datasets exceeds that of DNNs.

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Section: Resultsmentioning

confidence: 99%

A Parallel Spiking Neural Network Based on Adaptive Lateral Inhibition Mechanism for Objective Recognition

Dong

2022

Computational Intelligence and Neuroscience

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“…So, it is important to find a low-cost solution where models are compressed while maintaining accuracy. Kumar et al [95] proposed MobiHisNet, a lightweight CNN model based on MObileNet, which was applied to histopathological image classification (HIC). It reduced the computational parameters efficiently by applying a range of depth-wise separable convolutions.…”

Section: Application Of Lightweight Modelsmentioning

confidence: 99%

Edge Intelligence with Distributed Processing of DNNs: A Survey

Tang¹,

Cui²,

Qi³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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With the rapid development of deep learning, the size of data sets and deep neural networks (DNNs) models are also booming. As a result, the intolerable long time for models' training or inference with conventional strategies can not meet the satisfaction of modern tasks gradually. Moreover, devices stay idle in the scenario of edge computing (EC), which presents a waste of resources since they can share the pressure of the busy devices but they do not. To address the problem, the strategy leveraging distributed processing has been applied to load computation tasks from a single processor to a group of devices, which results in the acceleration of training or inference of DNN models and promotes the high utilization of devices in edge computing. Compared with existing papers, this paper presents an enlightening and novel review of applying distributed processing with data and model parallelism to improve deep learning tasks in edge computing. Considering the practicalities, commonly used lightweight models in a distributed system are introduced as well. As the key technique, the parallel strategy will be described in detail. Then some typical applications of distributed processing will be analyzed. Finally, the challenges of distributed processing with edge computing will be described.

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“…With the rapid advancement of the Internet of Things (IoT), lightweight research has become significant for resourceconstrained IoT devices. The application of IoT based deep learning in the healthcare service has grown substantially in recent years, resulting in the advancement of diagnostic equipment and opening up new avenues for medical treatment [13].…”

Section: A Backgroundmentioning

confidence: 99%

“…Using a compound coefficient, EfficientNet can scale all dimensions of depth/width/resolution. Kumar et al [13] presented an effective and portable CNN model for histopathology image categorization, and demonstrated its performance on the Raspberry Pi and three mobile devices. MobileViTs [48]…”

Section: B Related Workmentioning

confidence: 99%

FastNet: A Lightweight Convolutional Neural Network for Tumors Fast Identification in Mobile-Computer-Assisted Devices

Xiao

Qin

Chen

et al. 2023

IEEE Internet Things J.

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Histopathology diagnosis is an important standard for breast tumors identifying. However, histopathology image analysis is complex, tedious and error-prone, due to the superresolution image. In recent years, deep learning technology has been successfully applied to histopathology image analysis and made great progress. The well-known deep neural networks usually have tens of million parameters, which consume much memory to deploy the state-of-the-art model. In addition, deep neural networks rely on high-performance hardware resources, which impede the deployment of state-of-the-art model on portable equipment. In this work, a novel framework which consists of a weight accumulation method and a lightweight fast neural network (FastNet) was proposed for tumor fast identification (TFI) in mobile computer-assisted devices. The weight accumulation method was designed to obtain the tissue mask regions of interest and remove the useless background area in histopathology images, which greatly reduces the redundant computation cost. Furthermore, we proposed the lightweight FastNet to improve the computational efficiency on mobile devices. A novel attention loss function was designed and applied in FastNet. The attention loss function pays more attention on the positive samples and the indistinguishable samples, which greatly improves performance. The proposed FastNet was compared with three state-of-the-art methods commonly used for image classification and object detection. Experimental results indicated that FastNet achieves highest recall of 96.94%, highest F1 score of 97.33% and highest accuracy of 97.34%, besides least trainable parameters of 0.22M and smallest floating point operations of 210M FLOPs.

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MobiHisNet: A Lightweight CNN in Mobile Edge Computing for Histopathological Image Classification

Cited by 37 publications

References 33 publications

A Parallel Spiking Neural Network Based on Adaptive Lateral Inhibition Mechanism for Objective Recognition

A Parallel Spiking Neural Network Based on Adaptive Lateral Inhibition Mechanism for Objective Recognition

Edge Intelligence with Distributed Processing of DNNs: A Survey

FastNet: A Lightweight Convolutional Neural Network for Tumors Fast Identification in Mobile-Computer-Assisted Devices

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