Now-a-days, the medical industry is growing a lot with the adaptation of latest technologies as well as the logical evaluation and security norms provides a robust platform to enhance the effectiveness of the industry at a drastic level. In this paper, a digital bio-medical image processing based Pneumonia disease identification system is introduced with enhanced security features. Due to improving the efficiency of the application, a well-known watermarking based security constraint is included to provide the protection to the respective hospital environment and patients as well. To avoid these issues, some sort of security aspects need to be followed so that this paper included watermarking based security to provide a rich level of protection to the images going to be tested. The main intention of this paper is to introduce a novel security enabled digital image processing scheme to identify the Pneumonic disease in earlier stages with respect to the proper classification principles. In this paper, a novel deep learning algorithm is introduced called enhanced Dynamic Learning Neural Network in which it is a hybrid algorithm with the combinations of conventional DLNN algorithm and the Support Vector Classification algorithm. This proposed approach effectively identifies the Pneumonia disease in earlier stages but the security inspection on the testing stage is so important to analyze the disease. The respective testing image is properly watermarked with the logo of the corresponding hospital; the image is processed otherwise the proposed approach skips the image to process. These kinds of security features emphasize the medical industry and boost up the levels more as well as the patients can get an appropriate error free care with the help of such technology. A proper Chest X-Ray based Kaggle dataset is considered to process the system as well as which contains 5856 Chest X-Ray images under two different categories such as Pneumonia and Normal. With respect to processing these images and identifying the Pneumonia disease effectively as well as the proposed watermarking enabled security features provide a good impact in the medical field protection system. The resulting section provides the proper proof to the effectiveness of the proposed approach and its prediction efficiency.

中文翻译：

---

使用动态学习原理检测肺炎疾病的安全生物医学图像处理方案

如今，随着最新技术的采用以及逻辑评估和安全规范，医疗行业正在迅速发展，为大幅提高行业效率提供了一个强大的平台。本文介绍了一种基于数字生物医学图像处理的具有增强安全特性的肺炎疾病识别系统。由于提高了应用程序的效率，包括众所周知的基于水印的安全约束，以向相应的医院环境和患者提供保护。为了避免这些问题，需要遵循某些安全方面，以便本文包括基于水印的安全性，以为要测试的图像提供丰富的保护。本文的主要目的是介绍一种新的安全数字图像处理方案，以根据适当的分类原则在早期阶段识别肺炎疾病。本文介绍了一种新的深度学习算法，称为增强型动态学习神经网络，它是传统DLNN算法和支持向量分类算法相结合的混合算法。这种提议的方法有效地在早期阶段识别出肺炎疾病，但测试阶段的安全检查对于分析疾病非常重要。相应的检测图像带有相应医院的标志适当水印；图像被处理，否则所提出的方法会跳过图像进行处理。这些安全功能强调了医疗行业并提高了水平，并且患者可以在此类技术的帮助下获得适当的无差错护理。考虑使用适当的基于胸部 X 射线的 Kaggle 数据集来处理该系统，其中包含 5856 张胸部 X 射线图像，分为肺炎和正常两种不同类别。关于处理这些图像和有效识别肺炎疾病以及所提出的启用水印的安全特征在医疗领域保护系统中提供了良好的影响。结果部分为所提出方法的有效性及其预测效率提供了适当的证明。