Lung Diseases Detection Using Various Deep Learning Algorithms.

Area of Science:

• Medical Imaging Analysis
• Artificial Intelligence in Healthcare
• Computational Pathology

Background:

• Early and accurate detection of lung diseases like pneumonia, tuberculosis, and lung cancer is crucial for effective patient treatment.
• Traditional diagnostic methods can be time-consuming and may face limitations with complex imaging data.
• Deep learning, particularly Convolutional Neural Networks (CNNs), shows significant promise in analyzing biomedical image datasets.

Purpose of the Study:

• To develop and validate a deep learning framework for detecting and classifying multiple lung diseases from X-ray and CT scan images.
• To implement and compare the performance of Sequential, Functional, and Transfer deep learning models for lung disease classification.
• To establish a novel approach for disease detection that potentially outperforms existing methods.

Main Methods:

• Implementation of three deep learning models: Sequential, Functional, and Transfer learning.
• Training of models on open-source datasets of X-ray and CT scan images.
• Validation and performance comparison of the implemented models against existing methods using metrics like accuracy, F1 score, and recall.

Main Results:

• The sequential model achieved high performance for pneumonia (F1 score 98.55%, accuracy 98.43%) and tuberculosis (F1 score 97.99%, accuracy 99.4%).
• The functional model demonstrated superior performance for lung cancer detection with 99.9% accuracy and 99.89% specificity.
• The proposed models offer high accuracy and efficiency, with the functional model requiring fewer computational resources.

Conclusions:

• The developed deep learning framework provides an effective and accurate method for classifying lung diseases from medical images.
• The sequential and functional models represent a significant advancement in automated lung disease detection, offering improved diagnostic capabilities.
• This research paves the way for faster, more cost-effective, and accurate diagnosis of critical lung conditions, benefiting patient care.