학술논문

Adoption of Shallow Neural Networks in Pneumonia Classification
Document Type
Conference
Source
2023 International Conference on Modeling & E-Information Research, Artificial Learning and Digital Applications (ICMERALDA) Modeling & E-Information Research, Artificial Learning and Digital Applications (ICMERALDA), 2023 International Conference on. :307-311 Nov, 2023
Subject
Communication, Networking and Broadcast Technologies
Computing and Processing
Robotics and Control Systems
Signal Processing and Analysis
Bridges
Pneumonia
Error analysis
Learning (artificial intelligence)
Artificial neural networks
Data models
Digital divide
shallow neural network
image classification
pneumonia
Language
Abstract
A CNN contains a several layers each receiving input from the preceding layer. The final layer of the CNN flattens the image into a column and then determines which features most correlate to a particular class. In Kenya, pneumonia accounts for 16% of the total number of deaths in children under the age of five and ranked as the second leading cause of death with this age group. Currently radiologist examine Chest X-rays images under luminous light to check for pneumonia. This study proposes the use of a five-layer shallow neural network for image classification. The model records an accuracy of 91% and AUC scores of 90% with the type I error and type II errors are 14% and 5% respectively on secondary data. On local tests data, the model recorded an ROC of 90% and Type 1 error rate and type II error rate was 11.7 % and 7.4% respectively