Alzheimer disease classification using tawny flamingo based deep convolutional neural networks via federated learning

Smart health-care is the recent technology, which ensures the early diagnosis and prevention to the patients in the remote area. Particularly, for the serious illness, like cardiac problems, brain abnormalities require immediate attention. Moreover, owing to the commencement of the smart systems, the data from distributed sources is bulky, which imposes the complexity to handle and degrades the diagnosis accuracy. Hence, this research proposes an innovative distributed learning based classification model named federated learning dependent Tawny flamingo-based deep CNN classifier for the disease classification, which handles the clinical data from the distributed sources and ensures the disease classification with good accuracy. In this research, a tawny flamingo-based deep CNN is proposed to detect the Alzheimer's disease, where the parameter of the classifier is tuned by the tawny flamingo algorithm. The tawny flamingo optimization reveals the maximal accuracy of 98.252% for K-fold and 97.995% for training percentage dependent analysis.