Deep Convolutional Neural Network-Based Automatic Classification of Neonatal Hip Ultrasound Images: A Novel Data Augmentation Approach with Speckle Noise Reduction

2020 ◽  
Vol 46 (3) ◽  
pp. 735-749
Author(s):  
Aysun Sezer ◽  
Hasan Basri Sezer
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Noise Reduction ◽  
Data Augmentation ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Neonatal Hip ◽  
Hip Ultrasound ◽  
Speckle Noise Reduction

Speckle Noise Reduction of Holograms Based on Spectral Convolutional Neural Network

2020 ◽  
Vol 40 (5) ◽  
pp. 0509001
Author(s):  
周文静 Zhou Wenjing ◽  
邹帅 Zou Shuai ◽  
何登科 He Dengke ◽  
HuJinglu Hu Jinglu ◽  
于瀛洁 Yu Yingjie
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Noise Reduction ◽  
Speckle Noise ◽  
Speckle Noise Reduction

Tomato pest classification using deep convolutional neural network with transfer learning, fine tuning and scratch learning

2021 ◽  
pp. 1-10
Author(s):  
Gayatri Pattnaik ◽  
Vimal K. Shrivastava ◽  
K. Parvathi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Data Augmentation ◽  
State Of The Art ◽  
Deep Convolutional Neural Network ◽  
Fine Tuning ◽  
Tomato Plants ◽  
Random Weights

Pests are major threat to economic growth of a country. Application of pesticide is the easiest way to control the pest infection. However, excessive utilization of pesticide is hazardous to environment. The recent advances in deep learning have paved the way for early detection and improved classification of pest in tomato plants which will benefit the farmers. This paper presents a comprehensive analysis of 11 state-of-the-art deep convolutional neural network (CNN) models with three configurations: transfers learning, fine-tuning and scratch learning. The training in transfer learning and fine tuning initiates from pre-trained weights whereas random weights are used in case of scratch learning. In addition, the concept of data augmentation has been explored to improve the performance. Our dataset consists of 859 tomato pest images from 10 categories. The results demonstrate that the highest classification accuracy of 94.87% has been achieved in the transfer learning approach by DenseNet201 model with data augmentation.

scholarly journals Speckle Noise Reduction in Ultrasound Images for Improving the Metrological Evaluation of Biomedical Applications: An Overview

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 15983-15999 ◽  
Author(s):  
Carlos A. Duarte-Salazar ◽  
Andres Eduardo Castro-Ospina ◽  
Miguel A. Becerra ◽  
Edilson Delgado-Trejos
Keyword(s):  
Noise Reduction ◽  
Biomedical Applications ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Speckle Noise Reduction ◽  
Metrological Evaluation
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