Convolutional Neural Network Based Alzheimer's Disease Detection Using OIASIS Dataset

Alzheimer's disease, an intricate neurological disorder, culminates in the degeneration of brain cells, a process intricately linked with the emergence of dementia-a primary consequence of this ailment. The ramifications of Alzheimer's disease reverberate through cognitive functions, particularly manifesting as an individual's struggle to retain recent memories, subsequently eroding their capacity to execute routine activities proficiently. This research paper delves into the application of Convolutional Neural Networks (CNNs) as a potent tool for the early-stage detection of Alzheimer's disease. Characterized by its debilitating effects on memory and cognitive functions, Alzheimer's disease occupies a significant position within the realm of neurodegenerative disorders. The gradual loss of brain cells, the fundamental building blocks of cognition, propels individuals toward the precipice of dementia-a multifaceted condition profoundly influencing cognitive abilities. This intricate interplay between Alzheimer's and dementia translates into a cascading effect on an individual's capacity to recollect recent events, further leading to a gradual erosion of their capability to perform seemingly mundane day-to-day tasks. Addressing this critical juncture, this research endeavors to unravel the potential of Convolutional Neural Networks (CNNs) in their capacity to diagnose Alzheimer's disease at its nascent stages. CNNs, a subset of artificial neural networks renowned for their prowess in image recognition and pattern detection, stand poised as an innovative technological avenue for early detection. Through the extraction of intricate patterns and nuanced features from medical imaging data, CNNs offer the promise of identifying subtle indicators of Alzheimer's disease even before noticeable symptoms emerge.