A New Image Classification Approach via Improved MobileNet Models with Local Receptive Field Expansion in Shallow Layers

Wang, Wei; Hu, Yiyang; Zou, Ting; Li, Hongmei; Wang, Jin; Wang, Xin

doi:10.1155/2020/8817849

Cited by 49 publications

(45 citation statements)

References 24 publications

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“…As opposed to MobileNet V2 [ 63 ], MobileNet [ 4 ] is a CNN-based model that is extensively used to classify images. The main advantage of using the MobileNet architecture is that the model needs comparatively less computational effort than the conventional CNN model that makes it suitable for working over mobile devices and the computers that work over lower computational capabilities [ 64 , 65 , 66 ]. The MobileNet model is a simplified structure that incorporates a convolution layer that can be used in distinguishing the detail that relies on two manageable features that switch among the parameter’s accuracy and latency effectively.…”

Section: Methodsmentioning

confidence: 99%

Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

Srinivasu

SivaSai

Ijaz

et al. 2021

Sensors

449

169

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Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2× lesser computations than the conventional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region’s image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity.

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Section: Methodsmentioning

confidence: 99%

Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

Srinivasu

SivaSai

Ijaz

et al. 2021

Sensors

449

169

View full text Add to dashboard Cite

show abstract

“…To show the efficiency of the proposed method, some comparison experiments are conducted, where some other state-of-the-art deep learning-based methods are tested on the same data set used in Section III-B. These state-of-the-art deep learning-based methods include MobileNet (with 14 Conv, 13 DW, 1 Pool, 1 FC and 1 Softmax layers) [45], ResNet101 (with 101 layers) [46], AlexNet (with 8 layers) [47], EfficientNet (with 16 MBConv, 2 Conv, 1 Pool, and 1 FC layers) [48], and Inception V1 (with 22 layers, see Fig. 7 for details) [49].…”

Section: Comparison Experimentsmentioning

confidence: 99%

An Improved Deep Network-Based Scene Classification Method for Self-Driving Cars

Shen

Chen

et al. 2022

IEEE Trans. Instrum. Meas.

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Self-driving car is a hot research topic in the field of intelligent transportation system, which can greatly alleviate traffic jams and improve travel efficiency. Scene classification is one of the key technologies of self-driving cars, which can provide the basis for decision-making in self-driving cars. In recent years, deep learning-based solutions have achieved good results in the problem of scene classification. However, some problems should be further studied in the scene classification methods, such as how to deal with the similarities among different categories and the differences among the same category. To deal with these problems, an improved deep network-based scene classification method is proposed in this paper. In the proposed method, an improved Faster RCNN (Regions with CNN features) network is used to extract the features of representative objects in the scene to obtain local features, where a new residual attention block is added to the Faster RCNN network to highlight local semantics related to driving scenarios. In addition, an improved Inception module is used to extract global features, where a mixed Leaky ReLU and ELU function is presented, to reduce the possible redundancy of the convolution kernel and enhance the robustness. Then the local features and the global features are fused to realize the scene classification.Finally, a private data set is built from the public data sets for the specialized application of scene classification in the self-driving field and the proposed method is tested on the proposed data set. The experimental results show that the accuracy of the proposed method can reach 94.76%, which is higher than the state-of-the-art methods.

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“…The pooling layer, so called subsampling layer, is behindhand the convolutional layer. It executes down sampling function, with certain value as output in a specific sub region [22]. Simultaneously, the flexibility of the network to the modifications of image translation and rotation is raised.…”

Section: Mobilenet Based Spatial Feature Extractionmentioning

confidence: 99%

“…Stimulated by hunting performance of grey wolves, the top 3 optimum students are chosen, as given by Eq. (22).…”

Section: Teacher Phase Imentioning

confidence: 99%

Vision Based Real Time Monitoring System for Elderly Fall Event Detection Using Deep Learning

Priya¹

2022

Computer Systems Science and Engineering

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Human fall detection plays a vital part in the design of sensor based alarming system, aid physical therapists not only to lessen after fall effect and also to save human life. Accurate and timely identification can offer quick medical services to the injured people and prevent from serious consequences. Several vision-based approaches have been developed by the placement of cameras in diverse everyday environments. At present times, deep learning (DL) models particularly convolutional neural networks (CNNs) have gained much importance in the fall detection tasks. With this motivation, this paper presents a new vision based elderly fall event detection using deep learning (VEFED-DL) model. The proposed VEFED-DL model involves different stages of operations namely preprocessing, feature extraction, classification, and parameter optimization. Primarily, the digital video camera is used to capture the RGB color images and the video is extracted into a set of frames. For improving the image quality and eliminate noise, the frames are processed in three levels namely resizing, augmentation, and min-max based normalization. Besides, MobileNet model is applied as a feature extractor to derive the spatial features that exist in the preprocessed frames. In addition, the extracted spatial features are then fed into the gated recurrent unit (GRU) to extract the temporal dependencies of the human movements. Finally, a group teaching optimization algorithm (GTOA) with stacked autoencoder (SAE) is used as a binary classification model to determine the existence of fall or non-fall events. The GTOA is employed for the parameter optimization of the SAE model in such a way that the detection performance can be enhanced. In order to assess the fall detection performance of the presented VEFED-DL model, a set of simulations take place on the UR fall detection dataset and multiple cameras fall dataset. The experimental outcomes highlighted the superior performance of the presented method over the recent methods.

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A New Image Classification Approach via Improved MobileNet Models with Local Receptive Field Expansion in Shallow Layers

Cited by 49 publications

References 24 publications

Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

An Improved Deep Network-Based Scene Classification Method for Self-Driving Cars

Vision Based Real Time Monitoring System for Elderly Fall Event Detection Using Deep Learning

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