An Image Dataset of Advanced Imaging Techniques for Lung Cancer Diagnosis

This dataset consists of chest CT scan images collected from the National Institute of Diseases of the Chest and Hospital (NIDCH) in Dhaka, Bangladesh. It has been curated to support research in medical imaging, with a focus on the classification and early detection of lung-related diseases using artificial intelligence and deep learning methods. The dataset is divided into three diagnostic categories: 1050 images of malignant cases, 416 images of normal (healthy) cases, and 467 images of benign cases. Each image was carefully labeled and verified by Dr. Sabina Akter, MBBS, MD (Radiology & Imaging), Associate Professor and Head of the Department of Radiology and Imaging at NIDCH. The annotations were conducted under clinical supervision to ensure high-quality labeling suitable for academic and scientific use. This dataset provides a valuable resource for training, validating, and benchmarking AI-based models for automated diagnosis, particularly in the context of thoracic diseases and pulmonary abnormalities. It can also serve as a foundation for developing clinical decision support systems intended for deployment in real-world healthcare settings.