scholarly journals Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Eduardo Ribeiro ◽  
Andreas Uhl ◽  
Georg Wimmer ◽  
Michael Häfner
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Colonic Polyps ◽  
Colonic Polyp ◽  
Image Databases ◽  
Automated Classification ◽  
Network Layers ◽  
Endoscopic Image ◽  
Fully Connected

Recently, Deep Learning, especially through Convolutional Neural Networks (CNNs) has been widely used to enable the extraction of highly representative features. This is done among the network layers by filtering, selecting, and using these features in the last fully connected layers for pattern classification. However, CNN training for automated endoscopic image classification still provides a challenge due to the lack of large and publicly available annotated databases. In this work we explore Deep Learning for the automated classification of colonic polyps using different configurations for training CNNs from scratch (or full training) and distinct architectures of pretrained CNNs tested on 8-HD-endoscopic image databases acquired using different modalities. We compare our results with some commonly used features for colonic polyp classification and the good results suggest that features learned by CNNs trained from scratch and the “off-the-shelf” CNNs features can be highly relevant for automated classification of colonic polyps. Moreover, we also show that the combination of classical features and “off-the-shelf” CNNs features can be a good approach to further improve the results.

scholarly journals Text Summarization and Classification of Clinical Discharge Summaries using Deep Learning

2020 ◽  
Author(s):  
Alizar Marchawala ◽  
Preetkumar Patel ◽  
Khushal Paresh Thaker ◽  
Hardik Gunjal ◽  
Abhishek nagrecha ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Diagnostic Procedure ◽  
Patient Discharge ◽  
Text Summarization ◽  
Automated Classification ◽  
Complex Features ◽  
Discharge Summaries

<p>This paper implements the automated classification of patient discharge notes into standard disease labels which includes the name of the diagnostic procedure required. In this approach, we use Convolutional Neural Networks to classify and represent complex features from the medical discharge summaries using the MT sample dataset. We make use of GloVE to have a pretrained model learn from it.<b></b></p>

scholarly journals Text Summarization and Classification of Clinical Discharge Summaries using Deep Learning

2020 ◽  
Author(s):  
Alizar Marchawala ◽  
Preetkumar Patel ◽  
Khushal Paresh Thaker ◽  
Hardik Gunjal ◽  
Abhishek nagrecha ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Diagnostic Procedure ◽  
Patient Discharge ◽  
Text Summarization ◽  
Automated Classification ◽  
Complex Features ◽  
Discharge Summaries

<p>This paper implements the automated classification of patient discharge notes into standard disease labels which includes the name of the diagnostic procedure required. In this approach, we use Convolutional Neural Networks to classify and represent complex features from the medical discharge summaries using the MT sample dataset. We make use of GloVE to have a pretrained model learn from it.<b></b></p>

scholarly journals An Automated Classification of Mammals and Reptiles Animal Classes Using Deep Learning

2020 ◽  
pp. 2361-2370
Author(s):  
Elham Mohammed Thabit A. ALSAADI ◽  
Nidhal K. El Abbadi
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
State Of The Art ◽  
Target Object ◽  
Automated Classification ◽  
High Learning ◽  
Deep Convolutional Neural Networks ◽  
Learning Capacity ◽  
Typical Object

Detection and classification of animals is a major challenge that is facing the researchers. There are five classes of vertebrate animals, namely the Mammals, Amphibians, Reptiles, Birds, and Fish, and each type includes many thousands of different animals. In this paper, we propose a new model based on the training of deep convolutional neural networks (CNN) to detect and classify two classes of vertebrate animals (Mammals and Reptiles). Deep CNNs are the state of the art in image recognition and are known for their high learning capacity, accuracy, and robustness to typical object recognition challenges. The dataset of this system contains 6000 images, including 4800 images for training. The proposed algorithm was tested by using 1200 images. The accuracy of the system’s prediction for the target object was 97.5%.

scholarly journals Implementation of a deep learning model for automated classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) in real time

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Song-Quan Ong ◽  
Hamdan Ahmad ◽  
Gomesh Nair ◽  
Pradeep Isawasan ◽  
Abdul Hafiz Ab Majid
Keyword(s):  
Deep Learning ◽  
Aedes Aegypti ◽  
Real Time ◽  
Aedes Albopictus ◽  
Automated Classification ◽  
Expert Performance ◽  
Deep Convolutional Neural Networks ◽  
Significant Difference ◽  
Set Up

AbstractClassification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) by humans remains challenging. We proposed a highly accessible method to develop a deep learning (DL) model and implement the model for mosquito image classification by using hardware that could regulate the development process. In particular, we constructed a dataset with 4120 images of Aedes mosquitoes that were older than 12 days old and had common morphological features that disappeared, and we illustrated how to set up supervised deep convolutional neural networks (DCNNs) with hyperparameter adjustment. The model application was first conducted by deploying the model externally in real time on three different generations of mosquitoes, and the accuracy was compared with human expert performance. Our results showed that both the learning rate and epochs significantly affected the accuracy, and the best-performing hyperparameters achieved an accuracy of more than 98% at classifying mosquitoes, which showed no significant difference from human-level performance. We demonstrated the feasibility of the method to construct a model with the DCNN when deployed externally on mosquitoes in real time.

P3-014: DEEP LEARNING IN AUTOMATED CLASSIFICATION OF ADVERSE EVENTS IN CLINICAL STUDIES OF ALZHEIMER'S DISEASE

2006 ◽  
Vol 14 (7S_Part_19) ◽  
pp. P1067-P1068
Author(s):  
Pradeep Anand Ravindranath ◽  
Rema Raman ◽  
Tiffany W. Chow ◽  
Michael S. Rafii ◽  
Paul S. Aisen ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Adverse Events ◽  
Clinical Studies ◽  
Automated Classification
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