An effective Alzheimer's disease segmentation and classification using Deep ResUnet and Efficientnet.

Alzheimer's disease (AD) is a degenerative neurologic condition that results in the deterioration of several brain processes (e.g. memory loss). The most notable physical alteration in AD is the impairment of brain cells. An accurate examination of brain pictures may help to find the disease earlier because early diagnosis is crucial to enhancing patient care and treatment outcomes. Therefore, an easy and error-free system for AD diagnosis has recently received much research attention. Conventional image processing techniques sometimes cannot observe the significant features. As a result, the objective of this research is to develop an accurate and efficient method for identifying AD using magnetic resonance imaging (MRI). To begin with, the brain regions in the MRI images are segmented using a powerful Deep ResUnet-based approach. Then, the global and local features from the segmented images are recovered using a Multi-Scale Attention Siamese Network (MASNet)-based network. After extracting the features, the Slime Mould Algorithm-based feature selection process is conducted. Finally, the stages of AD are categorized using the EfficientNetB7 model. The efficacy of the presented method has been tested using brain MRI scans from the Kaggle dataset and the AD Neuroimaging Initiative (ADNI) dataset, and it achieves 99.31% and 99.38% accuracy, respectively. Finally, the study results show that the suggested method is helpful for accurate AD categorization.