Abstract: Medical area focused on automating skin cancer detection after the pandemic era of "Monkey Pox". Previous works proposed ANN mechanisms to classify the type of skin cancer. However, all those models implement layers of ANN with standard estimator components like hidden layers implemented using the ReLu activation function, several neurons are generally a power of two and others, but these values are not always perfect. Few researchers implemented optimization techniques for tuning the estimators of A.I. algorithms, but all those mechanisms require more resources and don't guarantee the best values for each estimator. The proposed method analyzes all the essential estimators of every possible neural network layer. Then it applies a modified version of Bayesian optimization because it avoids the disadvantages of Grid and Random optimization techniques. It picks the best estimator by using the conditional probability of naive Bayesian for every combination.

Rajashekar Deva and G .Narsimha, “Erythemato-Squamous Disease Detection using Best Optimized Estimators of ANN” International Journal of Advanced Computer Science and Applications(IJACSA), 14(2), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140274

@article{Deva2023,
title = {Erythemato-Squamous Disease Detection using Best Optimized Estimators of ANN},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140274},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140274},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {2},
author = {Rajashekar Deva and G .Narsimha}
}