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SCALED CONJUGATE GRADIENT BASED DECISION SUPPORT SYSTEM FOR AUTOMATED DIAGNOSIS OF SKIN CANCER

机译:基于尺度共轭梯度的皮肤癌自动诊断决策系统

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摘要

Melanoma is the most deathful form of skin cancer but early diagnosis can ensure a high rate of survival. Early diagnosis is one of the greatest challenges due to lack of experience of general practitioners (GPs). This paper presents a clinical decision support system designed for the use of general practitioners, aiming to save time and resources in the diagnostic process. Segmentation, pattern recognition, and lesion detection are the important steps in the proposed decision support system. The system analyses the images to extract the affected area using a novel proposed segmentation method. It determinates the underlying features which indicate the difference between melanoma and benign images and makes a decision. Considering the efficiency of neural networks in classification of complex data, scaled conjugate gradient based neural network is used for classification. The presented work also considers analyzed performance of other efficient neural network training algorithms on the specific skin lesion diagnostic problem and discussed the corresponding findings. The best diagnostic rates obtained through the proposed decision support system are around 92%.
机译:黑色素瘤是最致命的皮肤癌形式,但早期诊断可以确保高存活率。由于缺乏全科医生(GPs)的经验,早期诊断是最大的挑战之一。本文介绍了一种专为全科医师使用而设计的临床决策支持系统,旨在节省诊断过程中的时间和资源。分割,模式识别和病变检测是所提出的决策支持系统中的重要步骤。该系统使用一种新颖的分割方法对图像进行分析以提取受影响的区域。它确定表明黑素瘤和良性图像之间差异的潜在特征并做出决定。考虑到神经网络在复杂数据分类中的效率,使用基于比例共轭梯度的神经网络进行分类。提出的工作还考虑了其​​他有效神经网络训练算法对特定皮肤病变诊断问题的分析性能,并讨论了相应的发现。通过建议的决策支持系统获得的最佳诊断率约为92%。

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