Classification of Lung Conditions Using Image Processing and Deep Learning

The classification of lung conditions using image processing and deep learning has gained significant attention due to its potential in aiding early diagnosis and improving healthcare outcomes. This study focuses on classifying lung diseases, specifically COVID-19, pneumonia (CAP), and non-pneumonia (NP), using 3D medical imaging. The process begins with the acquisition of 3D lung images, followed by 3D image visualization techniques to enhance the anatomical structures for analysis. These images are then pre-processed to reduce noise and improve the quality of data for the model. We utilize deep learning, specifically AlexNet with transfer6 learning, to classify the lung conditions. The model is trained using a dataset of Labeled 3D images, and transfer learning allows for leveraging pretrained weights, optimizing training efficiency, and improving classification accuracy. The proposed system is capable of distinguishing between COVID-19, pneumonia (CAP), and non-pneumonia (NP) conditions with high accuracy. Testing results show that the AlexNet model, fine-tuned for this task, provides robust performance in classifying the lung conditions, demonstrating the effectiveness of deep learning techniques in medical image analysis. This approach offers a promising tool for assisting radiologists in diagnosing and differentiating between various lung diseases, enhancing diagnostic precision and healthcare delivery.

Keywords: Lung 3D Dataset, Pre-Processing, Deep learning, CNN, Classification, Accuracy.