Internet of Medical Things (IoMT) Enabled Skin Lesion Detection and Classification Using Optimal Segmentation and Restricted Boltzmann Machines

2020 ◽  
pp. 195-209
Author(s):  
A. Peter Soosai Anandaraj ◽  
V. Gomathy ◽  
A. Amali Angel Punitha ◽  
D. Abitha Kumari ◽  
S. Sheeba Rani ◽  
...  
Keyword(s):  
Skin Lesion ◽  
Lesion Detection ◽  
Restricted Boltzmann Machines ◽  
Boltzmann Machines ◽  
Internet Of Medical Things ◽  
Optimal Segmentation

scholarly journals Analysis on Noisy Boltzmann Machines and Noisy Restricted Boltzmann Machines

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Wenhao Lu ◽  
Chi-Sing Leung ◽  
John Sum
Keyword(s):  
Restricted Boltzmann Machines ◽  
Boltzmann Machines

scholarly journals Adaptive hyperparameter updating for training restricted Boltzmann machines on quantum annealers

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Guanglei Xu ◽  
William S. Oates
Keyword(s):  
Neural Network ◽  
Maximum Likelihood ◽  
Image Reconstruction ◽  
Image Recognition ◽  
Shannon Entropy ◽  
Reconstruction Error ◽  
Likelihood Method ◽  
Restricted Boltzmann Machines ◽  
Boltzmann Machines ◽  
D Wave

AbstractRestricted Boltzmann Machines (RBMs) have been proposed for developing neural networks for a variety of unsupervised machine learning applications such as image recognition, drug discovery, and materials design. The Boltzmann probability distribution is used as a model to identify network parameters by optimizing the likelihood of predicting an output given hidden states trained on available data. Training such networks often requires sampling over a large probability space that must be approximated during gradient based optimization. Quantum annealing has been proposed as a means to search this space more efficiently which has been experimentally investigated on D-Wave hardware. D-Wave implementation requires selection of an effective inverse temperature or hyperparameter ($$\beta $$ β ) within the Boltzmann distribution which can strongly influence optimization. Here, we show how this parameter can be estimated as a hyperparameter applied to D-Wave hardware during neural network training by maximizing the likelihood or minimizing the Shannon entropy. We find both methods improve training RBMs based upon D-Wave hardware experimental validation on an image recognition problem. Neural network image reconstruction errors are evaluated using Bayesian uncertainty analysis which illustrate more than an order magnitude lower image reconstruction error using the maximum likelihood over manually optimizing the hyperparameter. The maximum likelihood method is also shown to out-perform minimizing the Shannon entropy for image reconstruction.

Multi-Class Skin Lesion Detection and Classification via Teledermatology

2021 ◽  
pp. 1-1
Author(s):  
Muhammad Attique Khan ◽  
Khan Muhammad ◽  
Muhammad Sharif ◽  
Tallha Akram ◽  
Victor Hugo C. de Albuquerque
Keyword(s):  
Skin Lesion ◽  
Lesion Detection

scholarly journals Predicting drug-target interactions using restricted Boltzmann machines

2013 ◽  
Vol 29 (13) ◽  
pp. i126-i134 ◽  
Author(s):  
Yuhao Wang ◽  
Jianyang Zeng
Keyword(s):  
Drug Target ◽  
Restricted Boltzmann Machines ◽  
Boltzmann Machines
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