Enhancing Clinical Diagnosis With Convolutional Neural Networks: Developing High-Accuracy Deep Learning Models for...

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Computational Pathology

Background:

• Computational technology and artificial intelligence (AI) are increasingly used to improve diagnostic accuracy and efficiency in medicine.
• AI as an adjunctive tool can augment clinical decision-making, enhance patient care quality, and reduce healthcare costs.

Purpose of the Study:

• To develop a deep learning model using convolution neural networks (CNNs) for differentiating normal chest X-rays from those indicating pneumonia, tuberculosis, cardiomegaly, and COVID-19.

Main Methods:

• Utilized a dataset of 12,109 chest X-rays (3,063 normal, 3,098 pneumonia, 2,920 COVID-19, 2,214 cardiomegaly, 554 tuberculosis) from Kaggle for training and validation.
• Implemented CNNs to train a deep learning model to recognize disease-specific patterns in chest X-rays.

Main Results:

• The developed deep learning model demonstrated a high accuracy rate, with 98.34% of detections being correct (implying 1.66% incorrect detections).
• The model's ability to recognize patterns suggests potential for timely disease identification.

Conclusions:

• The study highlights the significant potential of machine learning algorithms in disease detection using chest X-rays.
• Further research should focus on using diverse, standardized image datasets and assessing performance across a wider range of conditions to enhance model reliability.