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Issues in Medical Diagnosis Using Computational Techniques

机译:计算技术在医学诊断中的问题

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摘要

Early diagnosis of disease may save patient's life in case of detection of brain tumours and Cancerous tissues, Prediction of Cardiovascular Disease and many more. Computer Aided Diagnosis can assist physician to diagnose disease like cancer early and help consult specialist for further treatment. Paper reviews various techniques presented by researchers for medical diagnosis and their performance issues are discussed. Novel hybrid classifier to classify medical images is proposed. Proposed method first extracts features from images and converts them into transaction database. Then parallel Frequent Pattern (FP) Growth is applied to mine association rules and classified by Decision Tree classifier. Two methods combined together, Parallel FP-Growth association rule mining and Decision Tree classification gives more efficiency and accuracy for proposed system. Various classifiers are compared on real dataset using experiments.
机译:在发现脑部肿瘤和癌性组织,预测心血管疾病等方面,疾病的早期诊断可以挽救患者的生命。计算机辅助诊断可以帮助医生及早诊断癌症等疾病,并帮助咨询专科医生进行进一步治疗。论文回顾了研究人员提出的用于医学诊断的各种技术,并对它们的性能问题进行了讨论。提出了一种用于医学图像分类的新型混合分类器。提出的方法首先从图像中提取特征并将其转换为交易数据库。然后将并行频繁模式(FP)增长应用于矿山关联规则并由决策树分类器进行分类。两种方法相结合,并行FP-Growth关联规则挖掘和决策树分类为所提出的系统提供了更高的效率和准确性。使用实验在真实数据集上比较各种分类器。

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