A Fast and Light Weight Deep Convolution Neural Network Model for Cancer Disease Identification in Human Lung(s)

Author(s):  
Siva Skandha Sanagala ◽  
Suneet Kr. Gupta ◽  
Vijaya Kumar Koppula ◽  
Mohit Agarwal

Offline Signature recognition plays an important role in Forensic issues. In this paper, we explore Signature Identification and Verification using features extracted from pretrained Convolution Neural Network model (Alex Net). All the experiments are performed on signatures from three dataset (SigComp2011) (Dutch, Chinese), SigWiComp2013 (Japanese) and SigWIcomp2015 (Italian). The result shows that features extracted from pretrained Deep Convolution neural network and SVM as classifier show better results than that of Decision Tree. The accuracy of more than 96% for Japanese, Italian, Dutch and Chinese Signatures is obtained with Deep Convolution neural network and SVM as classifier.


2017 ◽  
Vol 107 ◽  
pp. 715-720 ◽  
Author(s):  
Xingcheng Luo ◽  
Ruihan Shen ◽  
Jian Hu ◽  
Jianhua Deng ◽  
Linji Hu ◽  
...  

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Li Ma ◽  
Xueliang Guo ◽  
Shuke Zhao ◽  
Doudou Yin ◽  
Yiyi Fu ◽  
...  

The growth of strawberry will be stressed by biological or abiotic factors, which will cause a great threat to the yield and quality of strawberry, in which various strawberry diseased. However, the traditional identification methods have high misjudgment rate and poor real-time performance. In today's era of increasing demand for strawberry yield and quality, it is obvious that the traditional strawberry disease identification methods mainly rely on personal experience and naked eye observation and cannot meet the needs of people for strawberry disease identification and control. Therefore, it is necessary to find a more effective method to identify strawberry diseases efficiently and provide corresponding disease description and control methods. In this paper, based on the deep convolution neural network technology, the recognition of strawberry common diseases was studied, as well as a new method based on deep convolution neural network (DCNN) strawberry disease recognition algorithm, through the normal training of strawberry image feature representation in different scenes, and then through the application of transfer learning method, the strawberry disease image features are added to the training set, and finally the features are classified and recognized to achieve the goal of disease recognition. Moreover, attention mechanism and central damage function are introduced into the classical convolutional neural network to solve the problem that the information loss of key feature areas in the existing classification methods of convolutional neural network affects the classification effect, and further improves the accuracy of convolutional neural network in image classification.


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