학술논문

An Analysis of Pneumonia Prediction Approach Using Deep Learning
Document Type
Conference
Source
2023 International Conference on Disruptive Technologies (ICDT) Disruptive Technologies (ICDT), 2023 International Conference on. :681-684 May, 2023
Subject
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
General Topics for Engineers
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Deep learning
Measurement
Ultrasonic imaging
Microorganisms
Pulmonary diseases
Transfer learning
Lung
CNN
Deep Learning
Feature extraction
Image Processing
Pneumonia
Language
Abstract
A bacterial infection in the lungs' alveoli frequently results in pneumonia, an infectious disease. Pus occurs in infected lung tissue as it gets irritated. To ascertain whether a patient has pneumonia, experts perform physical examinations and diagnose their patients using a chest X-ray, ultrasound, or lung biopsy. A patient's mortality may result from a misdiagnosis, ineffective treatment, or disregard for the disease. The development of deep learning aids specialists in their decision-making when it comes to diagnosing pneumonia patients. The study uses six CNN models to predict and identify a patient with and without the condition using an X-ray image of their chest. It combines versatile and affordable deep learning approaches.