Advancing Pneumonia Detection and Recommendation Systems Through Machine Learning Approaches

Pneumonia remains a critical health issue, requiring timely and accurate diagnosis. This study introduces a machine learning approach enhanced by a fuzzy non-maximum suppression (FNMS) layer, which refines pneumonia detection in chest X-ray images by reducing overlaps and improving focus on affected areas. Alongside, a personalized recommendation system provides tailored treatment advice, follow-up plans, and preventive care suggestions, ensuring a patient-centred approach. Our model combines advanced algorithms with practical insights, achieving reliable results across varied datasets. This work bridges technology and compassionate healthcare, offering a promising step toward more accurate and humanized pneumonia management.