Nisha M, Kannan T, Sivasankari K
Department of Computer Science and Engineering, Akshaya College of Engineering and Technology, Coimbatore, Tamil Nadu, India.
Department of Mechanical Engineering, Amrita College of Engineering and Technology, Nagercoil, Tamil Nadu, India.
Math Biosci Eng. 2024 Dec 11;21(12):7830-7853. doi: 10.3934/mbe.2024344.
The hippocampus is a small, yet intricate seahorse-shaped tiny structure located deep within the brain's medial temporal lobe. It is a crucial component of the limbic system, which is responsible for regulating emotions, memory, and spatial navigation. This research focuses on automatic hippocampus segmentation from Magnetic Resonance (MR) images of a human head with high accuracy and fewer false positive and false negative rates. This segmentation technique is significantly faster than the manual segmentation methods used in clinics. Unlike the existing approaches such as UNet and Convolutional Neural Networks (CNN), the proposed algorithm generates an image that is similar to a real image by learning the distribution much more quickly by the semi-supervised iterative learning algorithm of the Deep Neuro-Fuzzy (DNF) technique. To assess its effectiveness, the proposed segmentation technique was evaluated on a large dataset of 18,900 images from Kaggle, and the results were compared with those of existing methods. Based on the analysis of results reported in the experimental section, the proposed scheme in the Semi-Supervised Deep Neuro-Fuzzy Iterative Learning System (SS-DNFIL) achieved a 0.97 Dice coefficient, a 0.93 Jaccard coefficient, a 0.95 sensitivity (true positive rate), a 0.97 specificity (true negative rate), a false positive value of 0.09 and a 0.08 false negative value when compared to existing approaches. Thus, the proposed segmentation techniques outperform the existing techniques and produce the desired result so that an accurate diagnosis is made at the earliest stage to save human lives and to increase their life span.
海马体是一个位于大脑内侧颞叶深处的小而复杂的海马形状的微小结构。它是边缘系统的关键组成部分，负责调节情绪、记忆和空间导航。这项研究专注于从人类头部的磁共振（MR）图像中高精度、低假阳性率和假阴性率地自动分割海马体。这种分割技术比临床中使用的手动分割方法快得多。与诸如UNet和卷积神经网络（CNN）等现有方法不同，所提出的算法通过深度神经模糊（DNF）技术的半监督迭代学习算法更快地学习分布，从而生成与真实图像相似的图像。为了评估其有效性，在所提出的分割技术在来自Kaggle的18900张图像的大型数据集上进行了评估，并将结果与现有方法的结果进行了比较。基于实验部分报告的结果分析，与现有方法相比，半监督深度神经模糊迭代学习系统（SS-DNFIL）中提出的方案实现了0.97的骰子系数、0.93的杰卡德系数、0.95的灵敏度（真阳性率）、0.97的特异性（真阴性率）、0.09的假阳性值和0.08的假阴性值。因此，所提出的分割技术优于现有技术并产生了预期的结果，以便在最早阶段进行准确诊断以挽救生命并延长寿命。
