使用 Alexnet DNN 算法检测维生素缺乏症的新方法

N. Srividhya, K. Divya, N. Sanjana, K. Krishna Kumari, M. Rambhupal
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摘要

维生素缺乏会对整体健康和福祉产生重大影响。早期检测在预防并发症和改善治疗效果方面发挥着至关重要的作用。然而,检测缺乏症的传统方法耗时长、成本高。本项目旨在利用 AlexNet DNN 算法(一种强大的图像分类深度学习模型)开发一种检测维生素缺乏症的新方法。该项目的目的是探索使用图像分析和深度学习技术准确、高效地检测维生素缺乏症的可行性。其目标包括提高检测的准确性,减少误报和漏报,并开发一种可靠、易用的早期检测工具。为实现目标,我们将收集大量描述各种维生素缺乏症的图像数据集。这些图像将经过预处理,以增强特征和减少噪音。AlexNet DNN 算法将在该数据集上进行训练,学习识别与不同缺乏症相关的模式和特征。该算法将经过严格的测试和评估,以确保其有效性。关键词:维生素 缺乏症 AlexNet 深度神经网络(DNN) 有效性
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NOVEL METHOD VITAMIN DEFICIENCY DETECTION USING ALEXNET DNN ALGORITHM
Vitamin deficiencies can have significant impacts on overall health and well-being. Early detection plays a crucial role in preventing complications and improving outcomes. However, traditional methods for detecting deficiencies can be time-consuming and costly. This project aims to develop a novel method for detecting vitamin deficiencies using the AlexNet DNN algorithm, a powerful deep learning model for image classification. The purpose of this project is to explore the feasibility of using image analysis and deep learning techniques to detect vitamin deficiencies accurately and efficiently. The objectives include improving the accuracy of detection, reducing false positives and negatives, and developing a reliable and accessible tool for early detection. To achieve our objectives, we will gather a large dataset of images depicting various vitamin deficiencies. These images will be preprocessed to enhance features and reduce noise. The AlexNet DNN algorithm will be trained on this dataset, learning to recognize patterns and features associated with different deficiencies. The algorithm will undergo rigorous testing and evaluation to ensure its effectiveness. KEYWORDS— Vitamins, Deficiency, AlexNet, Deep Neural Network(DNN), Effectiveness
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