abstract

The rapid advancement of medical imaging technologies has propelled the development of automated systems for the identification and classification of lung diseases. This study presents the design and implementation of an innovative image retrieval system utilizing artificial neural networks (ANNs)to enhance the accuracy and efficiency of diagnosing lung diseases. The proposed system focuses on addressing the challenges associated with the accurate recognition and classification of lung diseases from medical images, such as X-rays and CT scans. Leveraging the capabilities of ANNs, specifically convolution neural networks (CNNs), the system aims to capture intricate patterns and features within images that are often imperceptible to human observers. This enables the system to learn discriminative representations of normal lung anatomy and various disease manifestations. The design of the system involves multiple stages. Initially, a robust dataset of annotated lung images is curated, encompassing a diverse range of lung diseases and their corresponding healthy states. Subsequently, a pre-processing pipeline is implemented to standardize the images, ensuring consistent quality and facilitating feature extraction. The CNN architecture is then meticulously constructed, with attention to layer configurations, activation functions, and optimization algorithms to facilitate effective learning and classification. The system also incorporates image retrieval techniques, enabling the efficient searching and retrieval of relevant medical images from the database based on query inputs. This retrieval functionality assists medical practitioner sin accessing similar cases for comparative analysis and reference, ultimately supporting accurate diagnosis and treatment planning. To evaluate the system’s performance, comprehensive experiments are conducted using benchmark data sets, and performance metrics such as accuracy, precision, recall, and F1-score are measured. The experimental results demonstrate the system’s capability to distinguish between various lung diseases and healthy states with a high degree of accuracy and reliability. The proposed system exhibits substantial potential in revolutionizing lung disease diagnosis by assisting medical professionals in making informed decisions and improving patient outcomes.