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Prediction and Estimation of Lung Cancer and Authenticating by CNN-ECC Model

机译:CNN-ECC模型对肺癌的预测和估计和验证

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Miscellany of data analysis on the genesis of disease and the outcome of mortality is very crucial to keep track of the death rates induced due to the disease. The primary detection of the presence of viral infections in lungs is one of the major concerns in the health industry in today's scenario. These infections can lead to mortality. Therefore, the classification and analysis of disease are very pivotal along with security of data. Hence, it is essential for detecting diseases using CNN algorithm at an early stage and generation of medical report automatically. The method is tested for different modals with various lung infections like pneumonia, COVID-19, and cancerous growth in lungs. For these system-generated reports, encryption using ECC algorithm is used to prevent the breach of information while being exchanged from hospital to other organizations or vice versa.
机译:对疾病的成因和死亡结果的数据分析的误区是对跟踪由于疾病引起的死亡率来说是至关重要的。 肺部病毒感染存在的主要检测是当今情景中卫生行业的主要问题之一。 这些感染可能导致死亡率。 因此,疾病的分类和分析与数据的安全性非常关。 因此,在早期使用CNN算法检测疾病是必要的,并且自动生成医疗报告。 该方法针对不同的模态进行了不同的肺部感染,如肺炎,Covid-19和肺中的癌变生长。 对于这些系统生成的报告,使用ECC算法的加密用于防止违反信息的信息,同时从医院交换到其他组织,反之亦然。

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