Millions of people have died as a result of several epidemic lung diseases that have spread around the world, including COVID-19, lung opacity, and pneumonia. because these disorders exhibit small changes in Chest X-Ray (CXR) images, medical specialists have had difficulty in accurately diagnosing them. This research proposes a computer-aided lung disease detection technique based on CXR images to assist medical professionals. To meet the demand for practical and user-friendly diagnostic instruments, this research introduces new, enhanced deep-learning models designed explicitly for multi-class diagnosis of lung diseases, including normal, viral pneumonia, tuberculosis, lung opacity, fibrosis, and COVID-19 pneumonia, using CXR images. The four major phases of the proposed research are pre-processing, feature extraction, classification, and segmentation. First, the pre-processing phase employs a gaussian filtering approach to reduce input noise, thereby enhancing the image quality. Then, in the feature extraction stage, a custom Convolutional Neural Network (CNN) model is utilized to extract useful features and mitigate complexity problems. After feature extraction, the multiple classes of lung diseases are effectively classified by using the Enhanced ResNet50V2 (EResNet50V2) model. The region of infection in the chest radiographs is accurately identified and delineated using the Enhanced DeepLabV3+(EDeepLabV3+) model. The efficacy of the proposed approach is evaluated through extensive experiments against existing state-of-the-art methods. The research’s results demonstrated exceptional accuracy, with a 99.47% success rate. Throughout the six-class classification system, the average performance metrics for F1- score, recall, precision, Area Under the Curve (AUC), Intersection Over Union (IOU), and Dice Similarity Coefficient (DSC) were 0.9933, 0.9933, 0.9983, 0.9905, 0.9754, and 0.9867. This research advances the field of medical imaging and lays the foundation for developing deep learning-based systems for diagnosing lung diseases in the future.

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