Large-Scale Text Classification Using Scope-Based Convolutional Neural Network: A Deep Learning Approach

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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A Novel Deep Learning Approach of Convolutional Neural Network and Random Forest Classifier for Fine-grained Sentiment Classification

International Journal on Electrical Engineering and Informatics ◽

10.15676/ijeei.2021.13.2.13 ◽

2021 ◽

Vol 13 (2) ◽

pp. 465-476

Author(s):

Siji George C. G. ◽

◽

B Sumathi ◽

Keyword(s):

Neural Network ◽

Deep Learning ◽

Random Forest ◽

Convolutional Neural Network ◽

Random Forest Classifier ◽

Sentiment Classification ◽

Learning Approach ◽

Fine Grained

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Deep Learning Approach to Parkinson’s Disease Detection Using Voice Recordings and Convolutional Neural Network Dedicated to Image Classification

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) ◽

10.1109/embc.2019.8856972 ◽

2019 ◽

Cited By ~ 2

Author(s):

Marek Wodzinski ◽

Andrzej Skalski ◽

Daria Hemmerling ◽

Juan Rafael Orozco-Arroyave ◽

Elmar Noth

Keyword(s):

Neural Network ◽

Parkinson’S Disease ◽

Parkinson's Disease ◽

Deep Learning ◽

Convolutional Neural Network ◽

Image Classification ◽

Disease Detection ◽

Learning Approach

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Real-time automatic surgical phase recognition in laparoscopic sigmoidectomy using the convolutional neural network-based deep learning approach

Surgical Endoscopy ◽

10.1007/s00464-019-07281-0 ◽

2019 ◽

Vol 34 (11) ◽

pp. 4924-4931 ◽

Cited By ~ 10

Author(s):

Daichi Kitaguchi ◽

Nobuyoshi Takeshita ◽

Hiroki Matsuzaki ◽

Hiroaki Takano ◽

Yohei Owada ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Real Time ◽

Learning Approach ◽

Laparoscopic Sigmoidectomy ◽

Phase Recognition

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Conceptual Understanding of Convolutional Neural Network- A Deep Learning Approach

Procedia Computer Science ◽

10.1016/j.procs.2018.05.069 ◽

2018 ◽

Vol 132 ◽

pp. 679-688 ◽

Cited By ~ 39

Author(s):

Sakshi Indolia ◽

Anil Kumar Goswami ◽

S.P. Mishra ◽

Pooja Asopa

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Conceptual Understanding ◽

Learning Approach

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A Bi-Directional LSTM-CNN Model with Attention for Aspect-Level Text Classification

Future Internet ◽

10.3390/fi10120116 ◽

2018 ◽

Vol 10 (12) ◽

pp. 116 ◽

Cited By ~ 9

Author(s):

Yonghua Zhu ◽

Xun Gao ◽

Weilin Zhang ◽

Shenkai Liu ◽

Yuanyuan Zhang

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Text Classification ◽

Business Value ◽

The Internet ◽

Information Analysis ◽

Target Information ◽

Learning Methods ◽

Daily Lives

The prevalence that people share their opinions on the products and services in their daily lives on the Internet has generated a large quantity of comment data, which contain great business value. As for comment sentences, they often contain several comment aspects and the sentiment on these aspects are different, which makes it meaningless to give an overall sentiment polarity of the sentence. In this paper, we introduce Attention-based Aspect-level Recurrent Convolutional Neural Network (AARCNN) to analyze the remarks at aspect-level. The model integrates attention mechanism and target information analysis, which enables the model to concentrate on the important parts of the sentence and to make full use of the target information. The model uses bidirectional LSTM (Bi-LSTM) to build the memory of the sentence, and then CNN is applied to extracting attention from memory to get the attentive sentence representation. The model uses aspect embedding to analyze the target information of the representation and finally the model outputs the sentiment polarity through a softmax layer. The model was tested on multi-language datasets, and demonstrated that it has better performance than conventional deep learning methods.

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A Deep Learning Approach For the Detection and Classification of Interstitial Lung Diseases Using Convolutional Neural Network

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v5i11.7982 ◽

2017 ◽

Vol 5 (11) ◽

pp. 79-82

Author(s):

ruthy P S ◽

◽

Sanoj S R

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Lung Diseases ◽

Interstitial Lung Diseases ◽

Learning Approach

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A Deep Learning Approach of Recognizing Natural Disasters on Images using Convolutional Neural Network and Transfer Learning

10.1145/3487923.3487927 ◽

2021 ◽

Author(s):

Daryl B. Valdez ◽

Rey Anthony G. Godmalin

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Natural Disasters ◽

Transfer Learning ◽

Learning Approach

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Identifying Emotion on Indonesian Tweets using Convolutional Neural Networks

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ◽

10.29207/resti.v5i3.3137 ◽

2021 ◽

Vol 5 (3) ◽

pp. 584-593

Author(s):

Naufal Hilmiaji ◽

Kemas Muslim Lhaksmana ◽

Mahendra Dwifebri Purbolaksono

Keyword(s):

Neural Network ◽

Neural Networks ◽

Performance Evaluation ◽

Deep Learning ◽

Convolutional Neural Network ◽

Convolutional Neural Networks ◽

Text Classification ◽

Classification Model ◽

Learning Methods ◽

Expected Performance

especially with the advancement of deep learning methods for text classification. Despite some effort to identify emotion on Indonesian tweets, its performance evaluation results have not achieved acceptable numbers. To solve this problem, this paper implements a classification model using a convolutional neural network (CNN), which has demonstrated expected performance in text classification. To easily compare with the previous research, this classification is performed on the same dataset, which consists of 4,403 tweets in Indonesian that were labeled using five different emotion classes: anger, fear, joy, love, and sadness. The performance evaluation results achieve the precision, recall, and F1-score at respectively 90.1%, 90.3%, and 90.2%, while the highest accuracy achieves 89.8%. These results outperform previous research that classifies the same classification on the same dataset.

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The Application of Deep Learning and Image Processing Technology in Laser Positioning

Applied Sciences ◽

10.3390/app8091542 ◽

2018 ◽

Vol 8 (9) ◽

pp. 1542 ◽

Cited By ~ 7

Author(s):

Chern-Sheng Lin ◽

Yu-Chia Huang ◽

Shih-Hua Chen ◽

Yu-Liang Hsu ◽

Yu-Chen Lin

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Template Matching ◽

Large Scale ◽

Laser Spot ◽

Precise Position ◽

Spot Area ◽

Target Spot ◽

Spot Image

In this study, machine vision technology was used to precisely position the highest energy of the laser spot to facilitate the subsequent joining of product workpieces in a laser welding machine. The displacement stage could place workpieces into the superposition area and allow the parts to be joined. With deep learning and a convolutional neural network training program, the system could enhance the accuracy of the positioning and enhance the efficiency of the machine work. A bi-analytic deep learning localization method was proposed in this study. A camera was used for real-time monitoring. The first step was to use a convolutional neural network to perform a large-scale preliminary search and locate the laser light spot region. The second step was to increase the optical magnification of the camera, re-image the spot area, and then use template matching to perform high-precision repositioning. According to the aspect ratio of the search result area, the integrity parameters of the target spot were determined. The centroid calculation was performed in the complete laser spot. If the target was an incomplete laser spot, the operation of invariant moments would be performed. Based on the result, the precise position of the highest energy of the laser spot could be obtained from the incomplete laser spot image. The amount of displacement could be calculated by overlapping the highest energy of the laser spot and the center of the image.

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