Detection of Lung Malignancy Using SqueezeNet-Fc Deep Learning Classification Technique

2022 ◽  
pp. 683-699
Author(s):  
Vinod Kumar ◽  
Brijesh Bakariya
Keyword(s):  
Deep Learning ◽  
Classification Technique ◽  
Lung Malignancy

scholarly journals A SURVEY ON DEEP LEARNING FOR THE DETECTION OF THE INAPPROPRIATE CONTENT PRESENT IN TEXT

2021 ◽  
Vol 6 (4) ◽  
Author(s):  
Shivakumar H Teli ◽  
Dr Kiran V
Keyword(s):  
Deep Learning ◽  
Web Search ◽  
Learning Algorithms ◽  
Textual Information ◽  
Classification Techniques ◽  
Classification Technique ◽  
The Web

certain piece of textual information produced by any user or agent is said to be inappropriate if the expressed intent can cause hate, annoyance to other users or exhibits lack of respect, rudeness, which is disrespectful towards certain individuals or communities who may cause harm to oneself or others. In the present day scenario the different classification techniques are used to filter this kind of annoying text or messages. And browsers this days should be able to filter such kind of searches done in the searching engines which will be done every day. Providing such classification technique to filter such messages or searches which are not appropriate using some of the deep learning algorithms and considering the web search conversations such kind of searches which is found as abusive or which might cause hatred can be eliminated.

scholarly journals Deep learning based root-soil segmentation from X-ray tomography

2016 ◽  
Author(s):  
Clement DOUARRE ◽  
Richard SCHIELEIN ◽  
Carole FRINDEL ◽  
Stefan GERTH ◽  
David ROUSSEAU
Keyword(s):  
Deep Learning ◽  
Plant Science ◽  
Low Contrast ◽  
X Ray ◽  
Classification Technique ◽  
Plant Sciences ◽  
Science Problem ◽  
Classical Image ◽  
First Time ◽  
Segmentation Problem

One of the most challenging computer vision problem in plant sciences is the segmentation of root and soil from X-ray tomography. So far, this has been addressed from classical image analysis methods. In this paper, we address this root/soil segmentation problem from X-ray tomography using a new deep learning classification technique. The robustness of this technique, tested for the first time on this plant science problem, is established with root/soil presenting a very low contrast in X-ray tomography. We also demonstrate the possibility to segment efficiently root from soil while learning on purely synthetic soil and root.

Adaptive Lung Diseases Images Classification Technique Based on Deep Learning

2021 ◽  
pp. 803-814
Author(s):  
Nguyen Huu The ◽  
Nguyen Thi Hong Nhung ◽  
Nguyen Thanh Binh
Keyword(s):  
Deep Learning ◽  
Lung Diseases ◽  
Classification Technique

Comparative Analysis and Proposal of Deep Learning Based Colorectal Cancer Polyps Classification Technique

2020 ◽  
Vol 17 (5) ◽  
pp. 2354-2362
Author(s):  
Sushama Tanwar ◽  
S. Vijayalakshmi
Keyword(s):  
Neural Network ◽  
Colorectal Cancer ◽  
Deep Learning ◽  
Comparative Analysis ◽  
Medical Image ◽  
Automated Analysis ◽  
Convolutional Network ◽  
Classification Technique ◽  
Work Done ◽  
Proper Analysis

The information hidden in an image is worth more than a thousand words. Proper analysis of a medical image can help in timely detection and diagnose of a disease which increases the rate of survival of cancer patients. Analysis of images manually is subjective and time consuming. On the other hand, automated analysis of a medical image has a lot of challenges due to the architecture and colors of the medical images. This paper, gives a survey on detection, classification and diagnosis of colorectal cancer and proposes a deep learning based techniques to differentiate between healthy tissues and cancerous polyps in histology images. It also compares the accuracy of three different classification frameworks namely Convolutional Neural Network (CNN), Fully Convolutional Network (FCN) and Recurrent Neural Network (RNN). It also presents the overview of the work done in this field. It first discusses basic deep learning methods and then the known techniques used for detection, classification and diagnosis of colorectal cancer followed by the comparative analysis of all the surveyed paper. Finally, it talks about the conclusion, challenges and the future scope of the progress in this field.

Deep Learning

2009 ◽  
Author(s):  
Stellan Ohlsson
Keyword(s):  
Deep Learning
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