Skin melanoma classification using ROI and data augmentation with deep convolutional neural networks

2020 ◽  
Vol 79 (33-34) ◽  
pp. 24029-24055
Author(s):  
Khalid M. Hosny ◽  
Mohamed A. Kassem ◽  
Mohamed M. Foaud
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
Skin Melanoma ◽  
Deep Convolutional Neural Networks ◽  
Melanoma Classification

scholarly journals Understanding unconventional preprocessors in deep convolutional neural networks for face identification

2019 ◽  
Vol 1 (11) ◽  
Author(s):  
Chollette C. Olisah ◽  
Lyndon Smith
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Network Architecture ◽  
Data Augmentation ◽  
Color Space ◽  
Back Propagation ◽  
Face Identification ◽  
Deep Convolutional Neural Networks ◽  
Softmax Classifier ◽  
Deep Network

Abstract Deep convolutional neural networks have achieved huge successes in application domains like object and face recognition. The performance gain is attributed to different facets of the network architecture such as: depth of the convolutional layers, activation function, pooling, batch normalization, forward and back propagation and many more. However, very little emphasis is made on the preprocessor’s module of the network. Therefore, in this paper, the network’s preprocessing module is varied across different preprocessing approaches while keeping constant other facets of the deep network architecture, to investigate the contribution preprocessing makes to the network. Commonly used preprocessors are the data augmentation and normalization and are termed conventional preprocessors. Others are termed the unconventional preprocessors, they are: color space converters; grey-level resolution preprocessors; full-based and plane-based image quantization, Gaussian blur, illumination normalization and insensitive feature preprocessors. To achieve fixed network parameters, CNNs with transfer learning is employed. The aim is to transfer knowledge from the high-level feature vectors of the Inception-V3 network to offline preprocessed LFW target data; and features is trained using the SoftMax classifier for face identification. The experiments show that the discriminative capability of the deep networks can be improved by preprocessing RGB data with some of the unconventional preprocessors before feeding it to the CNNs. However, for best performance, the right setup of preprocessed data with augmentation and/or normalization is required. Summarily, preprocessing data before it is fed to the deep network is found to increase the homogeneity of neighborhood pixels even at reduced bit depth which serves for better storage efficiency.

scholarly journals Towards highly accurate coral texture images classification using deep convolutional neural networks and data augmentation

2019 ◽  
Vol 118 ◽  
pp. 315-328 ◽  
Author(s):  
Anabel Gómez-Ríos ◽  
Siham Tabik ◽  
Julián Luengo ◽  
ASM Shihavuddin ◽  
Bartosz Krawczyk ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
Deep Convolutional Neural Networks

Early and Late Level Fusion of Deep Convolutional Neural Networks for Visual Concept Recognition

2016 ◽  
Vol 10 (03) ◽  
pp. 379-397 ◽  
Author(s):  
Hilal Ergun ◽  
Yusuf Caglar Akyuz ◽  
Mustafa Sert ◽  
Jianquan Liu
Keyword(s):  
Neural Networks ◽  
Best Practices ◽  
Convolutional Neural Networks ◽  
Network Architecture ◽  
Data Augmentation ◽  
State Of The Art ◽  
Great Promise ◽  
Visual Concept ◽  
Deep Convolutional Neural Networks ◽  
Concept Recognition

Visual concept recognition is an active research field in the last decade. Related to this attention, deep learning architectures are showing great promise in various computer vision domains including image classification, object detection, event detection and action recognition in videos. In this study, we investigate various aspects of convolutional neural networks for visual concept recognition. We analyze recent studies and different network architectures both in terms of running time and accuracy. In our proposed visual concept recognition system, we first discuss various important properties of popular convolutional network architecture under consideration. Then we describe our method for feature extraction at different levels of abstraction. We present extensive empirical information along with best practices for big data practitioners. Using these best practices we propose efficient fusion mechanisms both for single and multiple network models. We present state-of-the-art results on benchmark datasets while keeping computational costs at low level. Our results show that these state-of-the-art results can be reached without using extensive data augmentation techniques.

scholarly journals Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
M. Kekic ◽  
◽  
C. Adams ◽  
K. Woodruff ◽  
J. Renner ◽  
...  
Keyword(s):  
Neural Networks ◽  
Beta Decay ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
Neutrinoless Double Beta Decay ◽  
High Energy ◽  
Double Beta Decay ◽  
Deep Convolutional Neural Networks ◽  
Calibration Source ◽  
Background Rejection

Abstract Convolutional neural networks (CNNs) are widely used state-of-the-art computer vision tools that are becoming increasingly popular in high-energy physics. In this paper, we attempt to understand the potential of CNNs for event classification in the NEXT experiment, which will search for neutrinoless double-beta decay in 136Xe. To do so, we demonstrate the usage of CNNs for the identification of electron-positron pair production events, which exhibit a topology similar to that of a neutrinoless double-beta decay event. These events were produced in the NEXT-White high-pressure xenon TPC using 2.6 MeV gamma rays from a 228Th calibration source. We train a network on Monte Carlo-simulated events and show that, by applying on-the-fly data augmentation, the network can be made robust against differences between simulation and data. The use of CNNs offers significant improvement in signal efficiency and background rejection when compared to previous non-CNN-based analyses.

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