Nature-inspired algorithms play a vital role in various applications, namely image processing, engineering, industrialized designs and business. Generally, these algorithms are inspired by the nature which is helpful in segmenting the brain internal regions, namely cerebrospinal fluid, grey matter HC, white matter, ventricle and so on. Segmentation of hippocampus (HC) is a very hectic process due to its anatomical structure of the brain. This work has been recommended for different optimization techniques such as lion optimization algorithm (LOA), genetic algorithm, BAT algorithm, particle swarm optimization and artificial bee colony optimization to segment HC region from the brain subregions. The comparison of these optimization methods has been evaluated, and it showed better performance in LOA due to its individualities of escaping from local optima. From the obtained results, it is witnessed that the LOA has ability to segment HC region with high accuracy of 95%. The LOA method showed the best classification accuracy compared to all other methods. Finally, the mini-mental state examination score validation has been attempted to reach the clinical targets as HC region is a major hallmarks for diagnosing AD. The overall process of the proposed work demonstrates the abnormalities in the brain natural history which provides the reliable and accurate indication to the clinician about AD progression.

受自然启发的算法在图像处理，工程，工业化设计和商业等各种应用中起着至关重要的作用。通常，这些算法是受自然界启发的，这有助于分割大脑内部区域，即脑脊液，灰质HC，白质，心室等。由于其大脑的解剖结构，海马（HC）的分割是一个非常繁忙的过程。已推荐这项工作用于不同的优化技术，例如狮子优化算法（LOA），遗传算法，BAT算法，粒子群优化和人工蜂群优化，以从大脑子区域中分割HC区域。比较了这些优化方法的比较，由于它具有避免局部最优的独特性，因此在LOA中表现出更好的性能。从获得的结果可以看出，LOA具有以95％的高精度分割HC区的能力。与所有其他方法相比，LOA方法显示出最佳的分类精度。最后，由于HC区域是诊断AD的主要标志，因此已尝试通过小精神状态检查评分验证来达到临床目标。拟议工作的整个过程证明了大脑自然史的异常，这为临床医生提供了关于AD进展的可靠且准确的指示。与所有其他方法相比，LOA方法显示出最佳的分类精度。最后，由于HC区域是诊断AD的主要标志，因此已尝试通过小精神状态检查评分验证来达到临床目标。拟议工作的整个过程证明了大脑自然史的异常，这为临床医生提供了关于AD进展的可靠且准确的指示。与所有其他方法相比，LOA方法显示出最佳的分类精度。最后，由于HC区域是诊断AD的主要标志，因此已尝试通过小精神状态检查评分验证来达到临床目标。拟议工作的整个过程证明了大脑自然史的异常，这为临床医生提供了关于AD进展的可靠且准确的指示。