68 research outputs found
Visual and Camera Sensors
This book includes 13 papers published in Special Issue ("Visual and Camera Sensors") of the journal Sensors. The goal of this Special Issue was to invite high-quality, state-of-the-art research papers dealing with challenging issues in visual and camera sensors
Palmprint Gender Classification Using Deep Learning Methods
Gender identification is an important technique that can improve the performance of authentication systems by reducing searching space and speeding up the matching process. Several biometric traits have been used to ascertain human gender. Among them, the human palmprint possesses several discriminating features such as principal-lines, wrinkles, ridges, and minutiae features and that offer cues for gender identification. The goal of this work is to develop novel deep-learning techniques to determine gender from palmprint images. PolyU and CASIA palmprint databases with 90,000 and 5502 images respectively were used for training and testing purposes in this research. After ROI extraction and data augmentation were performed, various convolutional and deep learning-based classification approaches were empirically designed, optimized, and tested. Results of gender classification as high as 94.87% were achieved on the PolyU palmprint database and 90.70% accuracy on the CASIA palmprint database. Optimal performance was achieved by combining two different pre-trained and fine-tuned deep CNNs (VGGNet and DenseNet) through score level average fusion. In addition, Gradient-weighted Class Activation Mapping (Grad-CAM) was also implemented to ascertain which specific regions of the palmprint are most discriminative for gender classification
Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinsonâs Disease Affected by COVIDâ19: A Narrative Review
Background and Motivation: Parkinsonâs disease (PD) is one of the most serious, non-curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to predict cardiovascular/stroke risk in PD patients. The presence of COVIDâ19 causes the ML systems to be-come severely nonâlinear and poses challenges in cardiovascular/stroke risk stratification. Further, due to comorbidity, sample size constraints, and poor scientific and clinical validation techniques, there have been no wellâexplained ML paradigms. Deep neural networks are powerful learning machines that generalize nonâlinear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVIDâ19 framework. Method: The PRISMA search strategy was used for the selection of 292 studies closely associated with the effect of PD on CVD risk in the COVIDâ19 framework. We study the hypothesis that PD in the presence of COVIDâ19 can cause more harm to the heart and brain than in nonâ COVIDâ19 conditions. COVIDâ19 lung damage severity can be used as a covariate during DL training model designs. We, therefore, propose a DL model for the estimation of, (i) COVIDâ19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVIDâ19 lesions, office and laboratory arterial atherosclerotic imageâbased biomarkers, and medicine usage for the PD patients for the design of DL pointâbased models for CVD/stroke risk stratification. Results: We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVIDâ 19 environment and this was also verified. DL architectures like long shortâterm memory (LSTM), and recurrent neural network (RNN) were studied for CVD/stroke risk stratification showing powerful designs. Lastly, we examined the artificial intelligence bias and provided recommendations for early detection of CVD/stroke in PD patients in the presence of COVIDâ19. Conclusion: The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVIDâ19. © 2022 by the authors. Licensee MDPI, Basel, Switzerland
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