Implementation of intelligent model for pneumonia detection

Abstract

The advancement of technology in the field of artificial intelligence and neural networks allows us to improve speed and efficiency in the diagnosis of various types of problems. In the last few years, the rise in the field of convolutional neural networks has been particularly noticeable, showing promising results in problems related to image processing and computer vision. Given that humans have limited ability to detect patterns in individual images, accurate diagnosis can be a problem for even medical professionals. In order to minimize the number of errors and unintended consequences, computer programs based on neural networks and deep learning principles are increasingly used as assistant tools in medicine. The aim of this study was to develop a model of an intelligent system that receives x-ray image of the lungs as an input parameter and, based on the processed image, returns the possibility of pneumonia as an output. The implementation of this functionality was implemented through transfer learning methodology based on already defined convolution neural network architectures.

Perspective of Creating a Low-Cost Medical Assistant Robot Based on the WafflePI4 Platform with a Palm Vein Pattern Scanner

“…In this work, we employ a Convolutional Neural Network (CNN) as a robust methodology for the identification and recognition of user's venous patterns. A Convolutional Neural Network is a specialized architecture within the domain of neural networks, extensively employed by the processing and analysis of images and videos [17][18][19]. Its design is tailored to excel in pattern recognition tasks, showcasing inherit properties that enhance its effectiveness in discerning intricate patterns within visual data.…”

Comparison and Validation of Deep Learning Models for the Diagnosis of Pneumonia

“…Correa et al from Tulane University School of Public Health and Tropical Medicine presented a method for automatic classification of pneumonia using ultrasound imaging of the lungs and pattern recognition in 2018 [ 8 ]. Knok et al from Polytechnic of Međimurje used an already defined convolution neural network architecture to develop a model of an intelligent system that receives X-ray image of the lung as an input parameter and based on the processed image returned the possibility of pneumonia as an output in 2019 [ 9 ]. Rajaraman et al proposed a CNN-based decision support system to detect pneumonia in pediatric CXRs, and it effectively learned from a sparse collection of complex data with reduced bias and improved generalization in 2018 [ 10 ].…”