Memory Dysfunction Detection: An MRI Image- Based Approach

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Memory Dysfunction Detection: An MRI Image- Based Approach

Authors : Pallavi Meshram, Purva Tangadpalliwar, Yogen Dhurve, Nilesh Korde

DOI : 10.46335/IJIES.2024.9.5.12

Abstract : Using magnetic resonance, anatomical structures of the brain have been studied. imaging (MRI), which has helped to analyze various neurological diseases and define pathological areas. In order to implement preventive measures, early detection of memory dysfunction is essential. Memory Dysfunction is the most common chronic disease in the people belonging to old age, with a high rate of affection. Deep learning has proved to be a big success in the analysis of healthcare imagery over recent years. Using segmented MRI scans, diseases related to brain can be classified more precisely due to detailed tissue architecture studies. Many, intricate segmentation paradigms have been introduced for diagnosing Memory Dysfunction. Since deep Learning algorithms are capable of delivering efficient results when collecting a lot of data, and have attracted interest. Use to segment the brain's structure and classify memory disorders. Therefore, the techniques of deep learning are currently favored over machine learning techniques. We're going to talk about the concepts of convolutional neural network. In order to detect memory dysfunction, it may be used to study brain anatomy. Their results on open datasets, and their latest techniques Consideration is given to the benefits of brain MRI segmentation for the categorization of memory disorders.