Deep Convolutional Neural Networks for Chest Diseases Detection

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Computer-Aided Diagnosis

Background:

• Chest diseases pose significant health risks, necessitating accurate and timely diagnosis.
• Conventional diagnostic methods for chest pathologies can be time-consuming and require expert interpretation.
• Automated analysis of chest X-rays offers a potential solution for improving diagnostic efficiency.

Purpose of the Study:

• To evaluate the feasibility of classifying chest pathologies using chest X-rays via conventional and deep learning approaches.
• To present and analyze the performance of convolutional neural networks (CNNs) for chest disease diagnosis.
• To compare CNNs with backpropagation neural networks (BPNNs) and competitive neural networks (CpNNs).

Main Methods:

• Development and implementation of convolutional neural networks (CNNs) for chest X-ray analysis.
• Construction of backpropagation neural networks (BPNNs) using supervised learning.
• Development of competitive neural networks (CpNNs) utilizing unsupervised learning.
• Training and testing all networks on a standardized chest X-ray database.

Main Results:

• Comparative analysis of CNN, BPNN, and CpNN performance metrics including accuracy, error rate, and training time.
• Demonstration of the effectiveness of CNNs in classifying chest pathologies.
• Discussion of the strengths and weaknesses of each network architecture for diagnostic tasks.

Conclusions:

• Convolutional neural networks (CNNs) present a feasible and effective approach for the automated diagnosis of chest diseases from X-ray images.
• The study provides a comparative performance analysis, highlighting the advantages of deep learning methods in this domain.
• Findings suggest that CNNs can contribute to more efficient and accurate chest disease detection.