Optimization of Multilayer Perceptron Hyperparameter in Classifying Pneumonia Disease Through X-Ray Images with Speeded-Up Robust Features Extraction Method

Abstract

Pneumonia is an illness that affects practically everyone, from children to the elderly. Pneumonia is an infectious disease caused by viruses, bacteria, or fungi that affect the lungs. It is quite difficult to recognize someone who has pneumonia. This is because pneumonia has multiple levels of classification, and so the symptoms experienced may vary. The multilayer perceptron approach will be used in this study to categorize Pneumonia and determine the level of accuracy, which will contribute to scientific development. The Multilayer Perceptron is employed as the classification method with hyperparameter learning rate and momentum, while SURF is used to extract the features in this classification. Based on the experiments that have been carried out, in general, the learning rate value is not very influential in the learning process, both at the momentum values of 0.1, 0.3, 0.5, 0.7, and 0.9. The best desirable accuracy value for momentum 0.1 is at a learning rate of 0.05. The best desirable accuracy value for momentum 0.3 is at a learning rate of 0.09. The most desirable accuracy value for momentum 0.3 is at a learning rate of 0.05 and 0.07. At a learning rate of 0.03 the highest ideal accuracy value is obtained. The best desirable accuracy value for momentum 0.9 is at a learning rate of 0.09. this research should be redone using the number of hidden layers and nodes in each hidden layer. The addition of a hidden layer, as well as variations in the number of nodes in the hidden layer, will affect computation time and yield more optimal accuracy results.

Compares the effectiveness of the bagging method in classifying spices using the histogram of oriented gradient feature extraction technique

“…The F2-score is a weighted harmonic mean of recall and precision, with a best value of 1 and worst value of 0. Lastly, the Jaccard index, or Jaccard similarity coefficient, measures the similarity between two sets of labels by dividing the size of their intersection by the size of their union [26], [27].…”