scholarly journals Efficient structural pseudoinverse learning-based hierarchical representation learning for skin lesion classification

2021 ◽  
Author(s):  
Xiaodan Deng ◽  
Qian Yin ◽  
Ping Guo
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Learning ◽  
Skin Lesion ◽  
Gabor Filter ◽  
Representation Learning ◽  
Data Set ◽  
Slow Convergence ◽  
Hierarchical Representation ◽  
Lesion Classification

AbstractThe success of deep learning in skin lesion classification mainly depends on the ultra-deep neural network and the significantly large training data set. Deep learning training is usually time-consuming, and large datasets with labels are hard to obtain, especially skin lesion images. Although pre-training and data augmentation can alleviate these issues, there are still some problems: (1) the data domain is not consistent, resulting in the slow convergence; and (2) low robustness to confusing skin lesions. To solve these problems, we propose an efficient structural pseudoinverse learning-based hierarchical representation learning method. Preliminary feature extraction, shallow network feature extraction and deep learning feature extraction are carried out respectively before the classification of skin lesion images. Gabor filter and pre-trained deep convolutional neural network are used for preliminary feature extraction. The structural pseudoinverse learning (S-PIL) algorithm is used to extract the shallow features. Then, S-PIL preliminarily identifies the skin lesion images that are difficult to be classified to form a new training set for deep learning feature extraction. Through the hierarchical representation learning, we analyze the features of skin lesion images layer by layer to improve the final classification. Our method not only avoid the slow convergence caused by inconsistency of data domain but also enhances the training of confusing examples. Without using additional data, our approach outperforms existing methods in the ISIC 2017 and ISIC 2018 datasets.

scholarly journals Towards Mapping Images to Text Using Deep-Learning Architectures

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1606
Author(s):  
Daniela Onita ◽  
Adriana Birlutiu ◽  
Liviu P. Dinu
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Learning ◽  
Image Features ◽  
The Other ◽  
Data Sets ◽  
Network Architectures ◽  
Learning Approaches ◽  
Data Set ◽  
Pixel Value

Images and text represent types of content that are used together for conveying a message. The process of mapping images to text can provide very useful information and can be included in many applications from the medical domain, applications for blind people, social networking, etc. In this paper, we investigate an approach for mapping images to text using a Kernel Ridge Regression model. We considered two types of features: simple RGB pixel-value features and image features extracted with deep-learning approaches. We investigated several neural network architectures for image feature extraction: VGG16, Inception V3, ResNet50, Xception. The experimental evaluation was performed on three data sets from different domains. The texts associated with images represent objective descriptions for two of the three data sets and subjective descriptions for the other data set. The experimental results show that the more complex deep-learning approaches that were used for feature extraction perform better than simple RGB pixel-value approaches. Moreover, the ResNet50 network architecture performs best in comparison to the other three deep network architectures considered for extracting image features. The model error obtained using the ResNet50 network is less by approx. 0.30 than other neural network architectures. We extracted natural language descriptors of images and we made a comparison between original and generated descriptive words. Furthermore, we investigated if there is a difference in performance between the type of text associated with the images: subjective or objective. The proposed model generated more similar descriptions to the original ones for the data set containing objective descriptions whose vocabulary is simpler, bigger and clearer.

scholarly journals Skin Lesion Classification Using Deep Learning Methods

2020 ◽  
Vol 15 (2) ◽  
pp. 180-194
Author(s):  
E.Yu. Shchetinin ◽  
A.V. Demidova ◽  
D.S. Kulyabov ◽  
L.A. Sevastyanov
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Skin Lesion ◽  
Deep Neural Network ◽  
Computer Experiments ◽  
Test Sample ◽  
Skin Lesions ◽  
Learning Methods ◽  
Data Set ◽  
Noise Contamination

In this paper, we propose an approach to solving the problem of recognizing skin lesions, namely melanoma, based on the analysis of dermoscopic images using deep learning methods. For this purpose, the architecture of a deep convolutional neural network was developed, which was applied to the processing of dermoscopic images of various skin lesions contained in the HAM10000 data set. The data under study were preprocessed to eliminate noise, contamination, and change the size and format of images. In addition, since the disease classes are unbalanced, a number of transformations were performed to balance them. The data obtained in this way were divided into two classes: Melanoma and Benign. Computer experiments using the built deep neural network based on the data obtained in this way have shown that the proposed approach provides 94% accuracy on the test sample, which exceeds similar results obtained by other algorithms.

scholarly journals Research and implementation of NN based on TensorFlow

Connectivity ◽  
2020 ◽  
Vol 148 (6) ◽  
Author(s):  
B. V. Shefkin ◽  
◽  
I. V. Krasyuk ◽  
V. O. Khomenchuk ◽  
K. P. Storchak ◽  
...  
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Classical Model ◽  
Test Accuracy ◽  
Data Set ◽  
The Neural Network

TensorFlow is Google’s open-source machine learning and deep learning framework, which is convenient and flexible to build the current mainstream deep learning model. Convolutional neural network is a classical model of deep learning, the advantage lies in its powerful feature extraction capabilities of convolutional blocks. A neural network in the simplest case is a mathematical model consisting of several layers of elements that perform parallel calculations. Initially, such an architecture was created by analogy with the small computing elements of the human brain — neurons. The minimal computing elements of an artificial neural network are also called neurons. Neural networks typically consist of three or more layers: an input layer, a hidden layer (or layers), and an output layer. An important feature of the neural network is its ability to learn by example, this is called learning with a teacher. The neural network is trained on a large number of examples consisting of input-output pairs (corresponding to each other input and output). In object recognition problems, such a pair will be the input image and the corresponding label — the name of the object. Neural network learning is an iterative process that reduces the deviation of the network output from a given «teacher response» — a label that corresponds to a given image. This process consists of steps called epochs of learning (they are usually calculated in thousands), each of which is the adjustment of the «weights» of the neural network — the parameters of the hidden layers of the network. Upon completion of the learning process, the quality of the neural network is usually good enough to perform the task for which it was trained, although the optimal set of parameters that perfectly recognizes all the images, it is often impossible to choose. Based on the TensorFlow platform, a convolutional neural network model with two-convolution-layers was built. The model was trained and tested with the MNIST data set. The test accuracy rate could reach 99,15%, and compared with the rate of 98,69% with only one-convolution-layer model, which shows that the two-convolution-layers convolutional neural network model has a better ability of feature extraction and classification decision-making.

Wavelet-based deep learning for skin lesion classification

2020 ◽  
Vol 14 (4) ◽  
pp. 720-726 ◽  
Author(s):  
Sertan Serte ◽  
Hasan Demirel
Keyword(s):  
Deep Learning ◽  
Skin Lesion ◽  
Lesion Classification

scholarly journals RISC-V Virtual Platform-Based Convolutional Neural Network Accelerator Implemented in SystemC

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1514
Author(s):  
Seung-Ho Lim ◽  
WoonSik William Suh ◽  
Jin-Young Kim ◽  
Sang-Young Cho
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
System Level ◽  
Neural Network Models ◽  
Data Set ◽  
Embedded Device ◽  
Virtual Platform

The optimization for hardware processor and system for performing deep learning operations such as Convolutional Neural Networks (CNN) in resource limited embedded devices are recent active research area. In order to perform an optimized deep neural network model using the limited computational unit and memory of an embedded device, it is necessary to quickly apply various configurations of hardware modules to various deep neural network models and find the optimal combination. The Electronic System Level (ESL) Simulator based on SystemC is very useful for rapid hardware modeling and verification. In this paper, we designed and implemented a Deep Learning Accelerator (DLA) that performs Deep Neural Network (DNN) operation based on the RISC-V Virtual Platform implemented in SystemC in order to enable rapid and diverse analysis of deep learning operations in an embedded device based on the RISC-V processor, which is a recently emerging embedded processor. The developed RISC-V based DLA prototype can analyze the hardware requirements according to the CNN data set through the configuration of the CNN DLA architecture, and it is possible to run RISC-V compiled software on the platform, can perform a real neural network model like Darknet. We performed the Darknet CNN model on the developed DLA prototype, and confirmed that computational overhead and inference errors can be analyzed with the DLA prototype developed by analyzing the DLA architecture for various data sets.

scholarly journals Deep Learning Assisted Neonatal Cry Classification via Support Vector Machine Models

2021 ◽  
Vol 9 ◽  
Author(s):  
Ashwini K ◽  
P. M. Durai Raj Vincent ◽  
Kathiravan Srinivasan ◽  
Chuan-Yu Chang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Support Vector ◽  
Svm Classifier ◽  
Infant Cry ◽  
Learning Techniques

Neonatal infants communicate with us through cries. The infant cry signals have distinct patterns depending on the purpose of the cries. Preprocessing, feature extraction, and feature selection need expert attention and take much effort in audio signals in recent days. In deep learning techniques, it automatically extracts and selects the most important features. For this, it requires an enormous amount of data for effective classification. This work mainly discriminates the neonatal cries into pain, hunger, and sleepiness. The neonatal cry auditory signals are transformed into a spectrogram image by utilizing the short-time Fourier transform (STFT) technique. The deep convolutional neural network (DCNN) technique takes the spectrogram images for input. The features are obtained from the convolutional neural network and are passed to the support vector machine (SVM) classifier. Machine learning technique classifies neonatal cries. This work combines the advantages of machine learning and deep learning techniques to get the best results even with a moderate number of data samples. The experimental result shows that CNN-based feature extraction and SVM classifier provides promising results. While comparing the SVM-based kernel techniques, namely radial basis function (RBF), linear and polynomial, it is found that SVM-RBF provides the highest accuracy of kernel-based infant cry classification system provides 88.89% accuracy.

Automatic Segmentation and Classification of COVID-19 CT Image Using Deep Learning and Multi-Scale Recurrent Neural Network Based Classifier

2021 ◽  
Vol 11 (10) ◽  
pp. 2618-2625
Author(s):  
R. T. Subhalakshmi ◽  
S. Appavu Alias Balamurugan ◽  
S. Sasikala
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Superior Performance ◽  
Multi Scale ◽  
Proposed Model ◽  
Class Labels

In recent times, the COVID-19 epidemic turn out to be increased in an extreme manner, by the accessibility of an inadequate amount of rapid testing kits. Consequently, it is essential to develop the automated techniques for Covid-19 detection to recognize the existence of disease from the radiological images. The most ordinary symptoms of COVID-19 are sore throat, fever, and dry cough. Symptoms are able to progress to a rigorous type of pneumonia with serious impediment. As medical imaging is not recommended currently in Canada for crucial COVID-19 diagnosis, systems of computer-aided diagnosis might aid in early COVID-19 abnormalities detection and help out to observe the disease progression, reduce mortality rates potentially. In this approach, a deep learning based design for feature extraction and classification is employed for automatic COVID-19 diagnosis from computed tomography (CT) images. The proposed model operates on three main processes based pre-processing, feature extraction, and classification. The proposed design incorporates the fusion of deep features using GoogLe Net models. Finally, Multi-scale Recurrent Neural network (RNN) based classifier is applied for identifying and classifying the test CT images into distinct class labels. The experimental validation of the proposed model takes place using open-source COVID-CT dataset, which comprises a total of 760 CT images. The experimental outcome defined the superior performance with the maximum sensitivity, specificity, and accuracy.

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