The rapid evolution of medical imaging technologies has spurred the development of automated systems to identify and classify lung diseases. This study introduces an innovative image retrieval system designed with artificial neural networks (ANNs) to enhance the accuracy and efficiency of diagnosing these conditions. Specifically targeting challenges in recognizing and categorizing lung diseases from X-rays and CT scans, the system utilizes convolutional neural networks (CNNs) to capture intricate patterns imperceptible to human observers. This enables the system to learn distinctive representations of normal lung anatomy and various disease manifestations.

The system's design proceeds through several stages. Initially, a comprehensive dataset of annotated lung images is curated, encompassing a diverse array of diseases and healthy states. Subsequently, an image preprocessing pipeline standardizes quality and aids in feature extraction. The CNN architecture is meticulously constructed, considering layer configurations, activation functions, and optimization algorithms to facilitate effective learning and classification. Additionally, the system incorporates image retrieval techniques for efficient querying and retrieval of relevant medical images from the database, supporting comparative analysis and aiding accurate diagnosis and treatment planning by medical professionals.

To assess performance, extensive experiments leverage benchmark datasets, evaluating metrics such as accuracy, precision, recall, and F1-score. Results demonstrate the system's capability to distinguish between lung diseases and healthy states accurately and reliably. This proposed system shows significant promise in advancing pulmonary healthcare by automating diagnosis and supporting informed decision-making, ultimately enhancing patient outcomes.