Enhancing Alzheimer’s Disease Detection with CNN-Based Analysis of Landmark-Selected Hippocampus Slices in MRI Images

The diagnosis of Alzheimer ’s disease (AD) presents considerable difficulties owing to its gradual onset and the requirement for advanced imaging techniques to identify initial indicators of neurodegeneration. This research presents an innovative strategy for diagnosing AD through the utilization of a Convolution Neural Network (CNN) to examine specific slices of MRI images, with an emphasis on the hippocampus—a vital brain area impacted by AD. The suggested approach utilizes landmark-based slice selection to improve the precision of AD diagnosis, thereby overcoming the shortcomings of traditional imaging analysis techniques. Because of its well-established susceptibility to shrinkage in AD, the hippocampus is a crucial region of interest for MRI imaging, which is a fundamental component of the clinical evaluation of the illness. Conventional methods frequently include examining vast portions or complete MRI volumes, which can be computationally costly and could weaken the diagnostic emphasis. This work tackles these problems by employing a CNN to assess carefully selected slices that emphasize the hippocampus, with the use of anatomical markers to guarantee that important regions are appropriately shown. A collection of MRI images with tagged hippocampus regions is used to train the CNN model, which enables it to identify unique characteristics linked to alterations in AD to spot minute changes in hippocampus shape that point to Alzheimer's disease in its early stages. The model's capacity to precisely locate and examine the hippocampus area is much enhanced by the application of landmark-based slice selection, which raises the accuracy and dependability of diagnosis.