Smart access development for classifying lung disease with chest x-ray images using deep learning.

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Pulmonology

Background:

• COVID-19 symptoms often mimic other lung illnesses like pneumonia, complicating diagnosis.
• Accurate and timely diagnosis is crucial for effective patient management during the pandemic.
• Chest X-rays offer initial insights but require expert interpretation, which can be time-consuming.

Purpose of the Study:

• To develop an automated system for classifying COVID-19 and pneumonia using Convolutional Neural Networks (CNNs).
• To leverage chest X-ray imaging for improved diagnostic accuracy and speed.
• To create a user-friendly interface for predicting lung conditions.

Main Methods:

• Utilized a Convolutional Neural Network (CNN) architecture implemented in TensorFlow and Keras.
• Trained the CNN model on chest X-ray images to differentiate between COVID-19, pneumonia, and normal lungs.
• Deployed the optimized CNN model within the Django framework for a web-based application.

Main Results:

• The CNN model demonstrated effective classification capabilities for identifying COVID-19 and pneumonia from chest X-rays.
• The deployed system provided a functional user interface for disease prediction.
• Achieved promising accuracy in distinguishing between different lung conditions.

Conclusions:

• AI-powered analysis of chest X-rays, using CNNs, shows significant potential in aiding the diagnosis of COVID-19 and pneumonia.
• The developed system can assist medical professionals in faster and more accurate disease identification.
• Further research and validation are warranted to integrate such AI tools into clinical workflows.