Multi-Modal Medical Imaging Interface for Lung Disease Classification

This paper discusses a novel approach for classifying lung diseases using multi-modal medical imaging techniques. By integrating data from various imaging modalities such as X-ray, CT scan, and MRI, our model aims to improve accuracy and reliability in disease diagnosis. Through advanced Deep learning algorithms and feature extraction methods, we demonstrate the effectiveness of our approach in accurately identifying and classifying different types of lung diseases, ultimately contributing to more precise and efficient patient care. Furthermore, we introduce an innovative enhancement to our approach through the integration of Grad-CAM (Gradient-weighted Class Activation Mapping) technology. This augmentation enables the generation of heatmaps that highlight regions of interest in medical images, providing insights into the severity and localization of abnormalities associated with lung diseases. By incorporating Grad-CAM, our model not only facilitates accurate disease classification but also offers clinicians valuable visual aids for assessing disease severity and planning treatment strategies. Combining the power of Python with the flexibility of PyQt, our application aims to provide a user-friendly interface for various tasks. Through careful design and implementation, we demonstrate the seamless integration of PyQt widgets, layouts, and functionalities to create an intuitive and efficient desktop application. This application serves as a valuable tool for healthcare professionals, enabling them to leverage our advanced classification model and Grad-CAM technology in a user-friendly manner, ultimately enhancing the quality and efficiency of patient care.