Deep Learning and Multi-Sensor Fusion for Glioma Classification Using Multistream 2D Convolutional Networks

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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Deep convolutional networks for human sketches by means of the evolutionary deep learning

2017 Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems (IFSA-SCIS) ◽

10.1109/ifsa-scis.2017.8023302 ◽

2017 ◽

Cited By ~ 6

Author(s):

Saya Fujino ◽

Naoki Mori ◽

Keinosuke Matsumoto

Keyword(s):

Deep Learning ◽

Convolutional Networks

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A Deep Learning Approach to Sensor Fusion Inference at the Edge

10.23919/date51398.2021.9474178 ◽

2021 ◽

Author(s):

T. Becnel ◽

P-E. Gaillardon

Keyword(s):

Deep Learning ◽

Sensor Fusion ◽

Learning Approach

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Going Deep into Remote Sensing Spatial Feature Learning

10.5753/sibgrapi.est.2020.12990 ◽

2020 ◽

Author(s):

Keiller Nogueira ◽

William Robson Schwartz ◽

Jefersson Alex Dos Santos

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Feature Learning ◽

Aerial Images ◽

Morphological Operations ◽

Pixel Classification ◽

Specific Data ◽

Convolutional Networks ◽

Spatial Features ◽

New Strategy

A lot of information may be extracted from the Earth’s surface through aerial images. This information may assist in myriad applications, such as urban planning, crop and forest management, disaster relief, etc. However, the process of distilling this information is strongly based on efficiently encoding the spatial features, a challenging task. Facing this, Deep Learning is able to learn specific data-driven features. This PhD thesis1 introduces deep learning into the remote sensing domain. Specifically, we tackled two main tasks, scene and pixel classification, using Deep Learning to encode spatial features over high-resolution remote sensing images. First, we proposed an architecture and analyze different strategies to exploit Convolutional Networks for image classification. Second, we introduced a network and proposed a new strategy to better exploit multi-context information in order to improve pixelwise classification. Finally, we proposed a new network based on morphological operations towards better learning of some relevant visual features.

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Deep learning techniques‐based perfection of multi‐sensor fusion oriented human‐robot interaction system for identification of dense organisms

Cognitive Computation and Systems ◽

10.1049/ccs2.12010 ◽

2021 ◽

Author(s):

Haiju Li ◽

Chuntang Zhang ◽

Jingwen Bo ◽

Zhongjun Ding

Keyword(s):

Deep Learning ◽

Sensor Fusion ◽

Human Robot Interaction ◽

Robot Interaction ◽

Interaction System ◽

Learning Techniques

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COVIDSAVIOR: A Novel Sensor-Fusion and Deep Learning Based Framework for Virus Outbreaks

Frontiers in Public Health ◽

10.3389/fpubh.2021.797808 ◽

2021 ◽

Vol 9 ◽

Author(s):

Sharnil Pandya ◽

Anirban Sur ◽

Nitin Solke

Keyword(s):

Deep Learning ◽

Body Temperature ◽

Real Time ◽

Sensor Fusion ◽

Face Mask ◽

Temperature Threshold ◽

Mask Condition ◽

Essential Information ◽

The Individual ◽

Scanning Mechanism

The presented deep learning and sensor-fusion based assistive technology (Smart Facemask and Thermal scanning kiosk) will protect the individual using auto face-mask detection and auto thermal scanning to detect the current body temperature. Furthermore, the presented system also facilitates a variety of notifications, such as an alarm, if an individual is not wearing a mask and detects thermal temperature beyond the standard body temperature threshold, such as 98.6°F (37°C). Design/methodology/approach—The presented deep Learning and sensor-fusion-based approach can also detect an individual in with or without mask situations and provide appropriate notification to the security personnel by raising the alarm. Moreover, the smart tunnel is also equipped with a thermal sensing unit embedded with a camera, which can detect the real-time body temperature of an individual concerning the prescribed body temperature limits as prescribed by WHO reports. Findings—The investigation results validate the performance evaluation of the presented smart face-mask and thermal scanning mechanism. The presented system can also detect an outsider entering the building with or without mask condition and be aware of the security control room by raising appropriate alarms. Furthermore, the presented smart epidemic tunnel is embedded with an intelligent algorithm that can perform real-time thermal scanning of an individual and store essential information in a cloud platform, such as Google firebase. Thus, the proposed system favors society by saving time and helps in lowering the spread of coronavirus.

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Ship Classification in High-Resolution SAR Images Using Deep Learning of Small Datasets

Sensors ◽

10.3390/s18092929 ◽

2018 ◽

Vol 18 (9) ◽

pp. 2929 ◽

Cited By ~ 14

Author(s):

Yuanyuan Wang ◽

Chao Wang ◽

Hong Zhang

Keyword(s):

Deep Learning ◽

High Resolution ◽

Classification Accuracy ◽

Experimental Results ◽

Fine Tuning ◽

Classification Model ◽

Great Success ◽

Sar Images ◽

Convolutional Networks ◽

Ship Classification

With the capability to automatically learn discriminative features, deep learning has experienced great success in natural images but has rarely been explored for ship classification in high-resolution SAR images due to the training bottleneck caused by the small datasets. In this paper, convolutional neural networks (CNNs) are applied to ship classification by using SAR images with the small datasets. First, ship chips are constructed from high-resolution SAR images and split into training and validation datasets. Second, a ship classification model is constructed based on very deep convolutional networks (VGG). Then, VGG is pretrained via ImageNet, and fine tuning is utilized to train our model. Six scenes of COSMO-SkyMed images are used to evaluate our proposed model with regard to the classification accuracy. The experimental results reveal that (1) our proposed ship classification model trained by fine tuning achieves more than 95% average classification accuracy, even with 5-cross validation; (2) compared with other models, the ship classification model based on VGG16 achieves at least 2% higher accuracies for classification. These experimental results reveal the effectiveness of our proposed method.

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