Deep Learning Approach for Lung Disease Detection by Image Processing

In this research, the detection of lung diseases is targeted for ILD (interstitial lung disease) emphysema consolidation and fibrosis by image processing approaches. High resolution CT scans of the lung are analyzed using a combination of several techniques that include deep learning models such as convolutional neural networks (CNNs), image segmentation, feature extraction and classification methods. CNNs improve the accuracy of a variety of manual image feature extraction methods for disease pattern recognition. In this study, transfer learning with pre-trained models is used to improve the classification of lung disease patterns in medical images. An impressive usage of U-Net for multi-scale feature extraction can refine the detection even more. This project demonstrates the potential for integrating these technologies into clinical settings, aiding radiologists in early diagnosis and improving patient outcomes.