基于可重构计算的黑色素瘤检测 CAD 系统提案。
Proposal of the CAD System for Melanoma Detection Using Reconfigurable Computing.
机构信息
Laboratory of Machine Learning and Intelligent Instrumentation, Federal University of Rio Grande do Norte, Natal, RN 59078-970, Brazil.
College of Engineering, Swansea University, Swansea, Wales SA2 8PP, UK.
出版信息
Sensors (Basel). 2020 Jun 3;20(11):3168. doi: 10.3390/s20113168.
This work proposes dedicated hardware to real-time cancer detection using Field-Programmable Gate Arrays (FPGA). The presented hardware combines a Multilayer Perceptron (MLP) Artificial Neural Networks (ANN) with Digital Image Processing (DIP) techniques. The DIP techniques are used to extract the features from the analyzed skin, and the MLP classifies the lesion into melanoma or non-melanoma. The classification results are validated with an open-access database. Finally, analysis regarding execution time, hardware resources usage, and power consumption are performed. The results obtained through this analysis are then compared to an equivalent software implementation embedded in an ARM A9 microprocessor.
本工作提出了一种使用现场可编程门阵列（FPGA）实时癌症检测的专用硬件。所提出的硬件结合了多层感知器（MLP）人工神经网络（ANN）和数字图像处理（DIP）技术。DIP 技术用于从分析的皮肤中提取特征，而 MLP 将病变分类为黑色素瘤或非黑色素瘤。分类结果通过一个开放访问的数据库进行验证。最后，对执行时间、硬件资源使用情况和功耗进行了分析。然后将通过该分析获得的结果与嵌入在 ARM A9 微处理器中的等效软件实现进行比较。
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