Lung diseases classification
Pro Research Analysisby 
Searched over 200M research papers
Deep Learning and Machine Learning for Lung Disease Classification
Recent research shows that deep learning and machine learning methods are highly effective for classifying lung diseases using medical images such as chest X-rays and CT scans. Convolutional Neural Networks (CNNs) are the most widely used models, often combined with image enhancement techniques like contrast-limited adaptive histogram equalization (CLAHE), discrete wavelet transform (DWT), and gamma correction to improve feature extraction and classification accuracy Chutia2024Chutia2024Shamrat2023. Transfer learning with pre-trained models such as DenseNet, MobileNetV2, EfficientNet, and Xception has also been shown to boost performance, especially when datasets are limited Hong2021Shamrat2023Santhoosh2024.
Multi-Class and Multi-Label Lung Disease Classification
Many studies have moved beyond binary classification to address the challenge of multi-class lung disease identification. Models have been developed to distinguish between normal lungs and various diseases, including pneumonia, tuberculosis, COVID-19, fibrosis, lung opacity, emphysema, atelectasis, and more Hong2021Sarkar2023Shamrat2023+2 MORE. These models achieve high accuracy, with some reporting over 96% accuracy in multi-class scenarios, demonstrating their robustness in real-world diagnostic settings Chutia2024Rehman2024Sarkar2023+2 MORE.
Feature Extraction and Attention Mechanisms
Advanced feature extraction methods, such as analyzing human tissue densities in Hounsfield Units from CT images, have been proposed to improve classification performance and enable real-time applications . Attention mechanisms, including channel attention modules and hybrid pooling, are integrated into deep learning models to focus on the most relevant image regions, further enhancing diagnostic accuracy and interpretability Chutia2024Choi2023.
Explainable AI and Model Interpretability
To support clinical adoption, explainable AI (XAI) techniques like SHAP and Grad-CAM are increasingly used. These methods provide visual explanations of model predictions, helping radiologists and clinicians understand the decision-making process and increasing trust in AI-assisted diagnosis Chutia2024Sarkar2023Shamrat2023+1 MORE.
Audio-Based Classification Approaches
In addition to imaging, some studies use respiratory sound recordings and spectrogram features for lung disease classification. Deep learning models, enhanced with attention modules, have shown high accuracy in distinguishing between normal and various abnormal lung sounds, supporting early diagnosis through smart stethoscopes Rehman2024Choi2023.
Performance and Clinical Impact
Across studies, the best-performing models achieve accuracy rates between 95% and 99%, with high precision, recall, and F1-scores, often surpassing traditional diagnostic methods and previous state-of-the-art models Chutia2024Rehman2024Chutia2024+3 MORE. These advancements promise faster, more consistent, and more accurate lung disease diagnosis, supporting clinicians in early detection and treatment planning.
Conclusion
Deep learning and machine learning techniques, especially CNNs with advanced preprocessing, attention mechanisms, and explainable AI, have significantly improved the classification of lung diseases from medical images and audio data. These models offer high accuracy, speed, and interpretability, making them valuable tools for supporting early and reliable lung disease diagnosis in clinical practice Chutia2024Rehman2024Chutia2024+7 MORE.
Sources and full results
Most relevant research papers on this topic