Integrating AI and IoT for Smarter Healthcare Facilities and Hospital Management

Leukemia is a blood cancer, defined by the abnormal addition of white blood cells and can be fatal if not detected early. Classical diagnostic procedures rely on manually interpreting microscopic blood smear images, which can be error-prone and time-consuming for humans. Modern developments in deep learning, particularly Convolutional Neural Networks, have allowed for the highly accurate automated diagnosis and categorization of leukemia from microscopic pictures. This paper presents a deep CNN model trained on microscopic blood smear pictures to differentiate between normal and leukemia-affected cells. This model evaluated the diagnostic accuracy, sensitivity, and specificity, which means that the model could be a non-invasive and reliable diagnosis tool. The research work recommended CNN approach uses extensive feature extraction layers to spot small morphological changes in blood cells, which signifies leukemia. The Internet of Things (IoT) offers critical real-time data that could provide accurate predictions when built with AI. The model accurately detected Leukemia types by picture augmentation and hyperparameter fine-tuning. Integrating models based on deep learning in clinical processes can improve early detection, decrease diagnostic errors, and help hematologists make faster and more accurate decisions.