scholarly journals 460 The role of data augmentation on the performance of automated lesion classification in the presence of imaging artifacts: An evaluation of the 2019 ISIC Challenge

2021 ◽  
Vol 141 (5) ◽  
pp. S80
Author(s):  
P.P. Mehta ◽  
J. Sellitti ◽  
J. Weber ◽  
Y. Oh ◽  
K. Kose ◽  
...  
Keyword(s):  
Data Augmentation ◽  
Imaging Artifacts ◽  
Lesion Classification

scholarly journals Data Augmentation-Assisted Makeup-Invariant Face Recognition

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Muhammad Sajid ◽  
Nouman Ali ◽  
Saadat Hanif Dar ◽  
Naeem Iqbal Ratyal ◽  
Asif Raza Butt ◽  
...  
Keyword(s):  
Face Recognition ◽  
Data Augmentation ◽  
Recognition Performance ◽  
Recognition Task ◽  
Face Images ◽  
Face Features ◽  
Face Datasets ◽  
Exponential Rates ◽  
Interpersonal Situations

Recently, face datasets containing celebrities photos with facial makeup are growing at exponential rates, making their recognition very challenging. Existing face recognition methods rely on feature extraction and reference reranking to improve the performance. However face images with facial makeup carry inherent ambiguity due to artificial colors, shading, contouring, and varying skin tones, making recognition task more difficult. The problem becomes more confound as the makeup alters the bilateral size and symmetry of the certain face components such as eyes and lips affecting the distinctiveness of faces. The ambiguity becomes even worse when different days bring different facial makeup for celebrities owing to the context of interpersonal situations and current societal makeup trends. To cope with these artificial effects, we propose to use a deep convolutional neural network (dCNN) using augmented face dataset to extract discriminative features from face images containing synthetic makeup variations. The augmented dataset containing original face images and those with synthetic make up variations allows dCNN to learn face features in a variety of facial makeup. We also evaluate the role of partial and full makeup in face images to improve the recognition performance. The experimental results on two challenging face datasets show that the proposed approach can compete with the state of the art.

scholarly journals Use of Artificial Intelligence as a Predictor of the Response to Treatment in Alopecia Areata

Iproceedings ◽  
10.2196/35433 ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. e35433
Author(s):  
Fernando Alarcón-Soldevilla ◽  
Francisco José Hernández-Gómez ◽  
Juan Antonio García-Carmona ◽  
Celia Campoy Carreño ◽  
Ramon Grimalt ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Alopecia Areata ◽  
Predictive Value ◽  
Data Augmentation ◽  
Conflicts Of Interest ◽  
The United States ◽  
Response To Treatment ◽  
Disease Evolution ◽  
Dermatological Disease

Background Artificial intelligence (AI) has emerged in dermatology with some studies focusing on skin disorders such as skin cancer, atopic dermatitis, psoriasis, and onychomycosis. Alopecia areata (AA) is a dermatological disease whose prevalence is 0.7%-3% in the United States, and is characterized by oval areas of nonscarring hair loss of the scalp or body without evident clinical variables to predict its response to the treatment. Nonetheless, some studies suggest a predictive value of trichoscopic features in the evaluation of treatment responses. Assuming that black dots, broken hairs, exclamation marks, and tapered hairs are markers of negative predictive value of the treatment response, while yellow dots are markers of no response to treatment according to recent studies, the absence of these trichoscopic features could indicate favorable disease evolution without treatment or even predict its response. Nonetheless, no studies have reportedly evaluated the role of AI in AA on the basis of trichoscopic features. Objective This study aimed to develop an AI algorithm to predict, using trichoscopic images, those patients diagnosed with AA with a better disease evolution. Methods In total, 80 trichoscopic images were included and classified in those with or without features of negative prognosis. Using a data augmentation technique, they were multiplied to 179 images to train an AI algorithm, as previously carried out with dermoscopic images of skin tumors with a favorable response. Subsequently, 82 new images of AA were presented to the algorithm, and the algorithm classified these patients as responders and non-responders; this process was reviewed by an expert trichologist observer and presented a concordance higher than 90% with the algorithm identifying structures described previously. Evolution of the cases was followed up to truly determine their response to treatment and, therefore, to assess the predictive value of the algorithm. Results In total, 32 of 40 (80%) images of patients predicted as nonresponders scarcely showed response to the treatment, while 34 of 42 (81%) images of those predicted as responders showed a favorable response to the treatment. Conclusions The development of an AI algorithm or tool could be useful to predict AA evolution and its response to treatment. However, further research is needed, including larger sample images or trained algorithms, by using images previously classified in accordance with the disease evolution and not with trichoscopic features. Conflicts of Interest None declared.

scholarly journals A Review on Finding Efficient Approach to Detect Customer Emotion Analysis using Deep Learning Analysis

2021 ◽  
Vol 3 (2) ◽  
pp. 95-113
Author(s):  
Kottilingam Kottursamy
Keyword(s):  
Deep Learning ◽  
Facial Expression Recognition ◽  
Data Augmentation ◽  
Expression Recognition ◽  
Generalization Error ◽  
Augmentation Techniques ◽  
Parallel Feature ◽  
Improved Accuracy ◽  
Learning Analysis

The role of facial expression recognition in social science and human-computer interaction has received a lot of attention. Deep learning advancements have resulted in advances in this field, which go beyond human-level accuracy. This article discusses various common deep learning algorithms for emotion recognition, all while utilising the eXnet library for achieving improved accuracy. Memory and computation, on the other hand, have yet to be overcome. Overfitting is an issue with large models. One solution to this challenge is to reduce the generalization error. We employ a novel Convolutional Neural Network (CNN) named eXnet to construct a new CNN model utilising parallel feature extraction. The most recent eXnet (Expression Net) model improves on the previous model's inaccuracy while having many fewer parameters. Data augmentation techniques that have been in use for decades are being utilized with the generalized eXnet. It employs effective ways to reduce overfitting while maintaining overall size under control.

scholarly journals Impact of BNT162b2 vaccination and isolation on SARS-CoV-2 transmission in Israeli households: an observational study

2021 ◽  
Author(s):  
Maylis Layan ◽  
Mayan Gilboa ◽  
Tal Gonen ◽  
Miki Goldenfeld ◽  
Lilac Meltzer ◽  
...  
Keyword(s):  
Healthcare Workers ◽  
Data Augmentation ◽  
Medical Center ◽  
Credible Interval ◽  
Control Measures ◽  
Risk Of Infection ◽  
Household Transmission ◽  
Index Cases

Background Massive vaccination rollouts against SARS-CoV-2 infections have facilitated the easing of control measures in countries like Israel. While several studies have characterized the effectiveness of vaccines against severe forms of COVID-19 or SARS-CoV-2 infection, estimates of their impact on transmissibility remain limited. Here, we evaluated the role of vaccination and isolation on SARS-CoV-2 transmission within Israeli households. Methods From December 2020 to April 2021, confirmed cases were identified among healthcare workers of the Sheba Medical Centre and their family members. Households were recruited and followed up with repeated PCR for a minimum of ten days after case confirmation. Symptoms and vaccination information were collected at the end of follow-up. We developed a data augmentation Bayesian framework to ascertain how age, isolation and BNT162b2 vaccination with more than 7 days after the 2nd dose impacted household transmission of SARS-CoV-2. Findings 210 households with 215 index cases were enrolled. 269 out of 687 (39%) household contacts developed a SARS-CoV-2 infection. Of those, 170 (63%) developed symptoms. Children below 12 years old were less susceptible than adults/teenagers (Relative Risk RR=0.50, 95% Credible Interval CI 0.32-0.79). Vaccination reduced the risk of infection among adults/teenagers (RR=0.19, 95% CI 0.07-0.40). Isolation reduced the risk of infection of unvaccinated adult/teenager (RR=0.11, 95% CI 0.05-0.19) and child contacts (RR=0.16, 95% CI 0.07-0.31) compared to unvaccinated adults/teenagers that did not isolate. Infectivity was significantly reduced in vaccinated cases (RR=0.22, 95% CI 0.06-0.70). Interpretation Within households, vaccination reduces both the risk of infection and of transmission if infected. When contacts were not vaccinated, isolation also led to important reductions in the risk of transmission. Vaccinated contacts might reduce their risk of infection if they isolate, although this requires confirmation with additional data. Funding Sheba Medical Center.

scholarly journals Multimodal Multi-tasking for Skin Lesion Classification Using Deep Neural Networks

2021 ◽  
pp. 27-38
Author(s):  
Rafaela Carvalho ◽  
João Pedrosa ◽  
Tudor Nedelcu
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Skin Lesion ◽  
Deep Neural Networks ◽  
Classification Performance ◽  
Additional Information ◽  
Deep Learning Model ◽  
Lesion Classification

AbstractSkin cancer is one of the most common types of cancer and, with its increasing incidence, accurate early diagnosis is crucial to improve prognosis of patients. In the process of visual inspection, dermatologists follow specific dermoscopic algorithms and identify important features to provide a diagnosis. This process can be automated as such characteristics can be extracted by computer vision techniques. Although deep neural networks can extract useful features from digital images for skin lesion classification, performance can be improved by providing additional information. The extracted pseudo-features can be used as input (multimodal) or output (multi-tasking) to train a robust deep learning model. This work investigates the multimodal and multi-tasking techniques for more efficient training, given the single optimization of several related tasks in the latter, and generation of better diagnosis predictions. Additionally, the role of lesion segmentation is also studied. Results show that multi-tasking improves learning of beneficial features which lead to better predictions, and pseudo-features inspired by the ABCD rule provide readily available helpful information about the skin lesion.

Prognostics and Health Management of Wind Energy Infrastructure Systems

2022 ◽  
Author(s):  
Celalettin Yuce ◽  
Ozhan Gecgel ◽  
Oguz Dogan ◽  
Shweta Dabetwar ◽  
Yasar Yanik ◽  
...  
Keyword(s):  
Machine Learning ◽  
Wind Energy ◽  
Data Augmentation ◽  
Health Management ◽  
Prognostics And Health Management ◽  
Quality Of Data ◽  
Energy Infrastructure ◽  
Infrastructure Systems ◽  
The Impact

Abstract The improvements in wind energy infrastructure have been a constant process throughout many decades. There are new advancements in technology that can further contribute towards the Prognostics and Health Management (PHM) in this industry. These advancements are driven by the need to fully explore the impact of uncertainty, quality and quantity of data, physics-based machine learning (PBML), and digital twin (DT). All these aspects need to be taken into consideration to perform an effective PHM of wind energy infrastructure. To address these aspects, four research questions were formulated. What is the role of uncertainty in machine learning (ML) in diagnostics and prognostics? What is the role of data augmentation and quality of data for ML? What is the role of PBML? What is the role of the DT in diagnostics and prognostics? The methodology used was Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA). A total of 143 records, from the last five years, were analyzed. Each of the four questions was answered by discussion of literature, definitions, critical aspects, benefits and challenges, the role of aspect in PHM of wind energy infrastructure systems, and conclusion.

Sign in / Sign up

Export Citation Format

Share Document