Kale Shilpa Jaykumar, Chavan Pramod U
Department of Electronics & Telecommunication Engineering, Vishwakarma Institute of Information Technology, Pune, Maharashtra, India.
Department of Electronics & Telecommunication Engineering, K. J. College of Engineering & Management Research, Pune, Maharashtra, India.
J Med Eng Technol. 2025 May;49(4):97-121. doi: 10.1080/03091902.2025.2484691. Epub 2025 Apr 12.
The most common cause of dementia, which includes significant cognitive impairment that interferes with day-to-day activities, is Alzheimer's Disease (AD). Deep learning techniques performed better on diagnostic tasks. However, current methods for detecting Alzheimer's disease lack effectiveness, resulting in inaccurate results. To overcome these challenges, a novel deep ensemble architecture for AD classification is proposed in this research. The proposed model involves key phases, including Preprocessing, Segmentation, Feature Extraction, and Classification. Initially, Median filtering is employed for preprocessing. Subsequently, an improved U-Net architecture is employed for segmentation, and then the features including Improved Shape Index Histogram (ISIH), Multi Binary Pattern (MBP), and Multi Texton are extracted from the segmented image. Then, an En-LeCILSTM is proposed, which combines the LeNet, CNN and improved LSTM models. Finally, the resultant output is obtained by averaging the intermediate output of each model, leading to improved detection accuracy. Finally, the proposed model's efficiency is assessed through various analyses, including classifier comparison, and performance metric evaluation. As a result, the En-LeCILSTM model scored a higher accuracy of 0.963 and an F-measure of 0.908, which surpasses the result of traditional methods. The outcomes demonstrate that the proposed model is notably more effective in detecting Alzheimer's disease.
痴呆症是指包括严重认知障碍并干扰日常活动的病症，其最常见的病因是阿尔茨海默病（AD）。深度学习技术在诊断任务中表现更佳。然而，目前用于检测阿尔茨海默病的方法缺乏有效性，导致结果不准确。为克服这些挑战，本研究提出了一种用于AD分类的新型深度集成架构。所提出的模型涉及关键阶段，包括预处理、分割、特征提取和分类。首先，采用中值滤波进行预处理。随后，采用改进的U-Net架构进行分割，然后从分割后的图像中提取包括改进形状指数直方图（ISIH）、多二进制模式（MBP）和多纹理等特征。然后，提出了一种En-LeCILSTM，它结合了LeNet、CNN和改进的LSTM模型。最后，通过对每个模型的中间输出求平均得到最终输出，从而提高检测准确率。最后，通过包括分类器比较和性能指标评估在内的各种分析来评估所提出模型的效率。结果，En-LeCILSTM模型的准确率达到了更高的0.963，F值为0.908，超过了传统方法的结果。结果表明，所提出的模型在检测阿尔茨海默病方面明显更有效。
