scholarly journals Deep Learning for Diagnostic Binary Classification of Multiple-Lesion Skin Diseases

2020 ◽  
Vol 7 ◽  
Author(s):  
Kenneth Thomsen ◽  
Anja Liljedahl Christensen ◽  
Lars Iversen ◽  
Hans Bredsted Lomholt ◽  
Ole Winther
Keyword(s):  
Deep Learning ◽  
Skin Diseases ◽  
Binary Classification ◽  
Multiple Lesion ◽  
Lesion Skin

Classification of large-scale image database of various skin diseases using deep learning

2021 ◽  
Author(s):  
Masaya Tanaka ◽  
Atsushi Saito ◽  
Kosuke Shido ◽  
Yasuhiro Fujisawa ◽  
Kenshi Yamasaki ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Skin Diseases ◽  
Image Database

Deep Learning-Based Binary Classification of ADHD Using Resting State MR Images

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Vikas Khullar ◽  
Karuna Salgotra ◽  
Harjit Pal Singh ◽  
Davinder Pal Sharma
Keyword(s):  
Deep Learning ◽  
Resting State ◽  
Binary Classification ◽  
Mr Images

Detection and Classification of Brain Tumors from MRI Images Using a Deep Convolutional Neural Network Approach

2022 ◽  
Vol 10 (1) ◽  
pp. 0-0
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Brain Tumor ◽  
Convolutional Neural Network ◽  
Binary Classification ◽  
Deep Convolutional Neural Network ◽  
Neural Network Models ◽  
Neural Network Approach ◽  
Cancer Disease

Brain tumor is a severe cancer disease caused by uncontrollable and abnormal partitioning of cells. Timely disease detection and treatment plans lead to the increased life expectancy of patients. Automated detection and classification of brain tumor are a more challenging process which is based on the clinician’s knowledge and experience. For this fact, one of the most practical and important techniques is to use deep learning. Recent progress in the fields of deep learning has helped the clinician’s in medical imaging for medical diagnosis of brain tumor. In this paper, we present a comparison of Deep Convolutional Neural Network models for automatically binary classification query MRI images dataset with the goal of taking precision tools to health professionals based on fined recent versions of DenseNet, Xception, NASNet-A, and VGGNet. The experiments were conducted using an MRI open dataset of 3,762 images. Other performance measures used in the study are the area under precision, recall, and specificity.

scholarly journals Application of neuron networks of deep learning for exposures editing of digital phonograms

2020 ◽  
Vol 65 (4) ◽  
pp. 506-512
Author(s):  
V. I. Solovyov ◽  
O. V. Rybalskiy ◽  
V. V. Zhuravel ◽  
V. K. Zheleznyak
Keyword(s):  
Deep Learning ◽  
Binary Classification ◽  
Maximum Size ◽  
Maximum Efficiency ◽  
Correct Classification ◽  
Digital Audio ◽  
Neuron Networks ◽  
Effective System ◽  
Further Development

Possibility of creation of effective system, which is intended for exposure of tracks of editing in digital phonograms and is built on the basis of neuron networks of the deep learning, is experimentally proven. Sense of experiment consisted in research of ability of the systems on the basis of such networks to expose pauses with tracks of editing. The experimental array of data is created in a voice editor from phonograms written on the different apparatus of the digital audio recording (at frequency of discretisation 44,1 kHz). A preselection of pauses was produced from it, having duration from 100 мs to a few seconds. From 1000 selected pauses the array of fragments of pauses is formed in the automatic (computer) mode, from which the arrays of fragments of pauses of different duration are generated by a dimension about 100 000. For forming of array of fragments of pauses with editing, the chosen pauses were divided into casual character parts in arbitrary correlation. Afterwards, the new pauses were created from it with the fixed place of editing. The general array of all fragments of pauses was broken into training and test arrays. The maximum efficiency, achieved on a test array in the process of educating, was determined. In general case this efficiency is determined by the maximum size of probability of correct classification of fragments with editing and fragments without editing. Scientifically reasonable methodology of exposure of signs of editing in digital phonograms is offered on the basis of neuron networks of the deep learning. The conducted experiments showed that the construction of the effective system is possible for the exposure of such tracks. Further development of methodology must be directed to find the ways to increase the probability of correct binary classification of investigated pauses.

scholarly journals Ensemble Deep Learning for Multilabel Binary Classification of User-Generated Content

Algorithms ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 83 ◽  
Author(s):  
Giannis Haralabopoulos ◽  
Ioannis Anagnostopoulos ◽  
Derek McAuley
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
Ensemble Learning ◽  
Affective Computing ◽  
Binary Classification ◽  
User Generated Content ◽  
Emotional Information ◽  
Multiple Classifiers ◽  
Different Levels

Sentiment analysis usually refers to the analysis of human-generated content via a polarity filter. Affective computing deals with the exact emotions conveyed through information. Emotional information most frequently cannot be accurately described by a single emotion class. Multilabel classifiers can categorize human-generated content in multiple emotional classes. Ensemble learning can improve the statistical, computational and representation aspects of such classifiers. We present a baseline stacked ensemble and propose a weighted ensemble. Our proposed weighted ensemble can use multiple classifiers to improve classification results without hyperparameter tuning or data overfitting. We evaluate our ensemble models with two datasets. The first dataset is from Semeval2018-Task 1 and contains almost 7000 Tweets, labeled with 11 sentiment classes. The second dataset is the Toxic Comment Dataset with more than 150,000 comments, labeled with six different levels of abuse or harassment. Our results suggest that ensemble learning improves classification results by 1.5 % to 5.4 % .

scholarly journals An Accuracy vs. Complexity Comparison of Deep Learning Architectures for the Detection of COVID-19 Disease

Computation ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 3
Author(s):  
Sima Sarv Ahrabi ◽  
Michele Scarpiniti ◽  
Enzo Baccarelli ◽  
Alireza Momenzadeh
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Medical Research ◽  
Binary Classification ◽  
Lung Infection ◽  
X Rays ◽  
Training Process ◽  
Learning Architectures

In parallel with the vast medical research on clinical treatment of COVID-19, an important action to have the disease completely under control is to carefully monitor the patients. What the detection of COVID-19 relies on most is the viral tests, however, the study of X-rays is helpful due to the ease of availability. There are various studies that employ Deep Learning (DL) paradigms, aiming at reinforcing the radiography-based recognition of lung infection by COVID-19. In this regard, we make a comparison of the noteworthy approaches devoted to the binary classification of infected images by using DL techniques, then we also propose a variant of a convolutional neural network (CNN) with optimized parameters, which performs very well on a recent dataset of COVID-19. The proposed model’s effectiveness is demonstrated to be of considerable importance due to its uncomplicated design, in contrast to other presented models. In our approach, we randomly put several images of the utilized dataset aside as a hold out set; the model detects most of the COVID-19 X-rays correctly, with an excellent overall accuracy of 99.8%. In addition, the significance of the results obtained by testing different datasets of diverse characteristics (which, more specifically, are not used in the training process) demonstrates the effectiveness of the proposed approach in terms of an accuracy up to 93%.

scholarly journals Assessment of Facial Homogeneity with Regard to Genealogical Aspects Based on Deep Learning Approach

2021 ◽  
Vol 12 (3) ◽  
pp. 1550-1556
Author(s):  
Ravi Kumar Y B Et.al
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Binary Classification ◽  
Learning Algorithms ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Facial Features ◽  
Learning Approach

The current research work encompasses the assessment of similarity based facial features of images with erected method so as to determines the genealogical similarity. It is based on the principle of grouping the closer features, as compared to those which are away from the predefined threshold for a better ascertainment of the extracted features. The system developed is trained using deep learning-oriented architecture incorporating these closer features for a binary classification of the subjects considered into genealogic non-genealogic. The genealogic set of data is further used to calculate the percentage of similarity with erected methods. The present work considered XX datasets from XXXX source for the assessment of facial similarities. The results portrayed an accuracy of 96.3% for genealogic data, the salient among them being those of father-daughter (98.1%), father-son(98.3%), mother-daughter(96.6%), mother-son(96.1%) genealogy in case of the datasets from “kinface W-I”. Extending this work onto “kinface W-II” set of data, the results were promising with father-daughter(98.5%), father-son(96.7%), mother-daughter(93.4%) and mother-son(98.9%) genealogy. Such an approach could be further extended to larger database so as to assess the genealogical similarity with the aid of machine-learning algorithms.

scholarly journals Deep Learning Application for Effective Classification of Different Types of Psoriasis

2022 ◽  
Vol 2022 ◽  
pp. 1-12
Author(s):  
Syeda Fatima Aijaz ◽  
Saad Jawaid Khan ◽  
Fahad Azim ◽  
Choudhary Sobhan Shakeel ◽  
Umer Hassan
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Skin Diseases ◽  
Normal Skin ◽  
Learning Algorithms ◽  
Learning Techniques ◽  
Different Types ◽  
Input Sample

Psoriasis is a chronic inflammatory skin disorder mediated by the immune response that affects a large number of people. According to latest worldwide statistics, 125 million individuals are suffering from psoriasis. Deep learning techniques have demonstrated success in the prediction of skin diseases and can also lead to the classification of different types of psoriasis. Hence, we propose a deep learning-based application for effective classification of five types of psoriasis namely, plaque, guttate, inverse, pustular, and erythrodermic as well as the prediction of normal skin. We used 172 images of normal skin from the BFL NTU dataset and 301 images of psoriasis from the Dermnet dataset. The input sample images underwent image preprocessing including data augmentation, enhancement, and segmentation which was followed by color, texture, and shape feature extraction. Two deep learning algorithms of convolutional neural network (CNN) and long short-term memory (LSTM) were applied with the classification models being trained with 80% of the images. The reported accuracies of CNN and LSTM are 84.2% and 72.3%, respectively. A paired sample T-test exhibited significant differences between the accuracies generated by the two deep learning algorithms with a p < 0.001 . The accuracies reported from this study demonstrate potential of this deep learning application to be applied to other areas of dermatology for better prediction.

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