Deep Learning Classification of Biomedical Text using Convolutional Neural Network

Brain tumor is a severe cancer disease caused by uncontrollable and abnormal partitioning of cells. Timely disease detection and treatment plans lead to the increased life expectancy of patients. Automated detection and classification of brain tumor are a more challenging process which is based on the clinician’s knowledge and experience. For this fact, one of the most practical and important techniques is to use deep learning. Recent progress in the ﬁelds of deep learning has helped the clinician’s in medical imaging for medical diagnosis of brain tumor. In this paper, we present a comparison of Deep Convolutional Neural Network models for automatically binary classification query MRI images dataset with the goal of taking precision tools to health professionals based on fined recent versions of DenseNet, Xception, NASNet-A, and VGGNet. The experiments were conducted using an MRI open dataset of 3,762 images. Other performance measures used in the study are the area under precision, recall, and specificity.

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Deep Learning-Based Classification of Weld Surface Defects

Applied Sciences ◽

10.3390/app9163312 ◽

2019 ◽

Vol 9 (16) ◽

pp. 3312 ◽

Cited By ~ 4

Author(s):

Zhu ◽

Ge ◽

Liu

Keyword(s):

Neural Network ◽

Deep Learning ◽

Random Forest ◽

Convolutional Neural Network ◽

Surface Defects ◽

Recognition Method ◽

High Level ◽

Intelligent Recognition ◽

Non Destructive

In order to realize the non-destructive intelligent identification of weld surface defects, an intelligent recognition method based on deep learning is proposed, which is mainly formed by convolutional neural network (CNN) and forest random. First, the high-level features are automatically learned through the CNN. Random forest is trained with extracted high-level features to predict the classification results. Secondly, the weld surface defects images are collected and preprocessed by image enhancement and threshold segmentation. A database of weld surface defects is established using pre-processed images. Finally, comparative experiments are performed on the weld surface defects database. The results show that the accuracy of the method combined with CNN and random forest can reach 0.9875, and it also demonstrates the method is effective and practical.

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Fault Diagnosis of Rotary Machines Using Deep Convolutional Neural Network with Wide Three Axis Vibration Signal Input

Sensors ◽

10.3390/s20144017 ◽

2020 ◽

Vol 20 (14) ◽

pp. 4017 ◽

Cited By ~ 3

Author(s):

Davor Kolar ◽

Dragutin Lisjak ◽

Michał Pająk ◽

Danijel Pavković

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Input Matrix ◽

Accelerometer Signal ◽

Rotary Machinery ◽

Signal Input

Fault diagnosis is considered as an essential task in rotary machinery as possibility of an early detection and diagnosis of the faulty condition can save both time and money. This work presents developed and novel technique for deep-learning-based data-driven fault diagnosis for rotary machinery. The proposed technique input raw three axes accelerometer signal as high definition 1D image into deep learning layers which automatically extract signal features, enabling high classification accuracy. Unlike the researches carried out by other researchers, accelerometer data matrix with dimensions 6400 × 1 × 3 is used as input for convolutional neural network training. Since convolutional neural networks can recognize patterns across input matrix, it is expected that wide input matrix containing vibration data should yield good classification performance. Using convolutional neural networks (CNN) trained model, classification in one of the four classes can be performed. Additionally, number of kernels of CNN is optimized using grid search, as preliminary studies show that alternating number of kernels impacts classification results. This study accomplished the effective classification of different rotary machinery states using convolutional artificial neural network for classification of raw three axis accelerometer signal input.

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Deep Learning-Based Classification of Korean Basal Cell Carcinoma Using Convolutional Neural Network

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2019.2560 ◽

2019 ◽

Vol 9 (1) ◽

pp. 195-201

Author(s):

Hyun Jung Chung ◽

Yoon Jae Kim ◽

Howon Song ◽

Sung Ku Ahn ◽

Hyunggun Kim ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Basal Cell Carcinoma ◽

Convolutional Neural Network ◽

Cell Carcinoma ◽

Basal Cell

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Classification of C2C12 cells at differentiation by convolutional neural network of deep learning using phase contrast images

Human Cell ◽

10.1007/s13577-017-0191-9 ◽

2017 ◽

Vol 31 (1) ◽

pp. 87-93 ◽

Cited By ~ 15

Author(s):

Hirohiko Niioka ◽

Satoshi Asatani ◽

Aina Yoshimura ◽

Hironori Ohigashi ◽

Seiichi Tagawa ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Phase Contrast ◽

C2c12 Cells

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Identification and classification of dental implant systems using various deep learning‐based convolutional neural network architectures

Clinical Oral Implants Research ◽

10.1111/clr.175_13509 ◽

2019 ◽

Vol 30 (S19) ◽

pp. 217-217

Author(s):

Lee Jae‐Hong

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Dental Implant ◽

Network Architectures ◽

Neural Network Architectures

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Hybrid Cluster Segmentation and deep learning Convolutional Neural Network classification of Remote Sensing Data

2020 Fourth International Conference on Inventive Systems and Control (ICISC) ◽

10.1109/icisc47916.2020.9171155 ◽

2020 ◽

Author(s):

Jagannath K Jadhav ◽

Kshitijarun Bidari ◽

Amruta P Sonavale ◽

R.P. Singh

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Deep Learning ◽

Convolutional Neural Network ◽

Remote Sensing Data ◽

Hybrid Cluster ◽

Sensing Data ◽

Neural Network Classification

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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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Classification of EgyptSat-1 Images Using Deep Learning Methods

International Journal of Sensors Wireless Communications and Control ◽

10.2174/2210327909666190207153858 ◽

2020 ◽

Vol 10 (1) ◽

pp. 37-46 ◽

Cited By ~ 3

Author(s):

Hatem Keshk ◽

Xu-Cheng Yin

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Deep Learning ◽

Convolutional Neural Network ◽

High Performance ◽

Satellite Images ◽

Satellite Image ◽

Aerial Photographs ◽

Average Accuracy

Background: Deep Learning (DL) neural network methods have become a hotspot subject of research in the remote sensing field. Classification of aerial satellite images depends on spectral content, which is a challenging topic in remote sensing. Objective: With the aim to accomplish a high performance and accuracy of Egyptsat-1 satellite image classification, the use of the Convolutional Neural Network (CNN) is raised in this paper because CNN is considered a leading deep learning method. CNN is developed to classify aerial photographs into land cover classes such as urban, vegetation, desert, water bodies, soil, roads, etc. In our work, a comparison between MAXIMUM Likelihood (ML) which represents the traditional supervised classification methods and CNN method is conducted. Conclusion: This research finds that CNN outperforms ML by 9%. The convolutional neural network has better classification result, which reached 92.25% as its average accuracy. Also, the experiments showed that the convolutional neural network is the most satisfactory and effective classification method applied to classify Egyptsat-1 satellite images.

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Efficient Imbalanced Multimedia Concept Retrieval by Deep Learning on Spark Clusters

Deep Learning and Neural Networks ◽

10.4018/978-1-7998-0414-7.ch017 ◽

2020 ◽

pp. 274-294

Author(s):

Yilin Yan ◽

Min Chen ◽

Saad Sadiq ◽

Mei-Ling Shyu

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Imbalanced Data ◽

Network Models ◽

Multimedia Data ◽

Neural Network Models ◽

Minority Class ◽

Imbalanced Data Classification

The classification of imbalanced datasets has recently attracted significant attention due to its implications in several real-world use cases. The classifiers developed on datasets with skewed distributions tend to favor the majority classes and are biased against the minority class. Despite extensive research interests, imbalanced data classification remains a challenge in data mining research, especially for multimedia data. Our attempt to overcome this hurdle is to develop a convolutional neural network (CNN) based deep learning solution integrated with a bootstrapping technique. Considering that convolutional neural networks are very computationally expensive coupled with big training datasets, we propose to extract features from pre-trained convolutional neural network models and feed those features to another full connected neutral network. Spark implementation shows promising performance of our model in handling big datasets with respect to feasibility and scalability.

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