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In the last ten years, lung cancer and chronic pulmonary diseases have become prominent respiratory diseases that require significant attention. This increase in prominence underscores their widespread impact on public health and the urgent need for better understanding, detection, and management strategies. Accurate identification of lung cancer and Chronic Obstructive Pulmonary Disease (COPD) is crucial for preserving human life. Accurate differentiation between the two disorders and the administration of the necessary treatment are very important. This study focuses on effectively discriminating between two of the deadliest chest diseases using chest X-ray images. Recurrent neural networks help to classify diseases accurately by improving feature extraction from radiographs. The proposed algorithm performs more effectively when analyzing chest X-ray image datasets showing alterations in a patient's chest, including the development of tiny lobes or thicker capillaries in the respiratory system among other details, compared to standard lung imaging. [ABSTRACT FROM AUTHOR]