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Alzheimer's disease (AD) has been studied extensively to better understand the complexities of this disease and to address the numerous unanswered questions about prognosis and diagnosis. To be able to determine and allocate the resources appropriate to the research area, a detailed understanding of the research topic is much needed. Along with the tremendous expansion in the scope of neurodegenerative disease treatment research, the diversity of technologies to help the research continues to expand. Many studies have investigated into how AD affects different brain structures as the disease progresses, using various image processing methods to derive a variety of brain structure steps. To detect AD, structural magnetic resonance imaging (sMRI) is utilized to detect delicate structural variations in the brain. MRI is preferred over other modalities for identifying the structural changes in the brain caused by neurodegenerative diseases and their significance for AD diagnosis and prognosis. Hippocampal atrophy is a significant biomarker for assessing and diagnosing AD. The statistical properties obtained by texture analysis on the MRI based on a biomarker can be used to identify and further evaluate subtle changes in neurodegeneration. To distinguish normal control subjects from AD patients, various Neural Network-based algorithms have been developed. Consequently, this analysis focuses on understanding the recent developments by using an enriched collection of papers available on Scopus, and thus assists in understanding and providing a guided perspective for assigning research resources. The analysis is focusing on various statistical data obtained from Scopus, such as source, document type, affiliations, and so on, to analyze and collate current trends, research activity, and the impact of several notable writers, institutes/organizations, and countries in the respective research domain.