Lung Disease Detection

Pneumonia, lung cancer and chronic obstructive pulmonary disease (COPD) cause many deaths worldwide, and patients do better when these illnesses are found early. Today a diagnosis rests mainly on a doctor reading chest X-rays and computed tomography (CT) scans, together with pulmonary function tests. This takes specialist time, depends on the reader's own judgement, and often leads to delay. Artificial intelligence (AI) and machine learning (ML) can speed the process up. Convolutional Neural Networks (CNNs), a type of deep learning, study radiological images and pick out small abnormal patterns that a human reader may overlook. Data from wearable sensors, digital stethoscopes and multi-omics biomarkers can be added to build multimodal diagnostic systems. Automation of this kind lowers the number of missed cases, makes clinical work smoother and lets treatment begin sooner. Joining computing methods with medical practice helps close gaps in care, particularly where resources are scarce, and can improve survival and long-term health. Key Words: Lung disease detection, Deep learning, Convolutional neural network, Chest X-ray, Pneumonia, Grad-CAM.