An Architecture for Microprocessor-Executable Skin Cancer Classification

Carlos Vicente Niño Rondón, Diego Andrés Castellano Carvajal, B. M. Delgado, Sergio Alexander Castro Casadiego, Dinael Guevara Ibarra
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Abstract

Skin cancer ranks as the most common malignant tumor among all types of cancer. Melanoma accounts for 1% of all cancer cases. However, it is responsible for the majority of deaths from this type of cancer. According to the American Cancer Society, it is expected that 99,780 new cases of melanoma will be diagnosed and about 7,650 people will die from this type of cancer. This work presents an executable architecture on reduced plate systems for skin cancer classification, complemented with image enhancement and feature enhancement stages, information extraction using VGG16 network architecture, feature reduction applying Principal Component Analysis and classification stage using gradient augmented decision trees (XGBoost). The architecture was tested on Raspberry Pi 4B reduced board system and developed with Python programming language and open-source libraries. In turn, the images processed and used are part of the ISIC Challenge Dataset. An average power value of 2.93 W out of a maximum of 3.6 W was obtained in the execution of the diagnostic tool. In turn, the minimum required software architecture response time was 0.09 seconds. The demand for the execution of the diagnostic tool in the Central Processing Unit was on average 20.63 % over a maximum value of 24.5 % respectively. On the other hand, the results at the software level of the architecture were compared with the scientific literature and presented improvements of about 9 % in terms of accuracy in skin cancer classification. The diagnostic tool is replicable and affordable due to reduced hardware requirements and cost of implementation.
一种微处理器可执行皮肤癌分类体系结构
皮肤癌是各种癌症中最常见的恶性肿瘤。黑色素瘤占所有癌症病例的1%。然而,这类癌症的大多数死亡都是由它造成的。根据美国癌症协会的数据,预计将有99780例新的黑色素瘤病例被诊断出来,大约7650人将死于这种类型的癌症。本文提出了一种用于皮肤癌分类的简化板系统的可执行架构,辅以图像增强和特征增强阶段,使用VGG16网络架构进行信息提取,使用主成分分析进行特征约简,使用梯度增强决策树(XGBoost)进行分类阶段。该架构在树莓派4B精简板系统上进行了测试,并使用Python编程语言和开源库进行了开发。反过来,处理和使用的图像是ISIC挑战数据集的一部分。在诊断工具的执行过程中,获得的最大3.6 W的平均功率值为2.93 W。相应地,所需的最小软件体系结构响应时间为0.09秒。在中央处理单元中执行诊断工具的需求平均为20.63%,高于24.5%的最大值。另一方面,将该体系结构软件层面的结果与科学文献进行比较,发现皮肤癌分类的准确率提高了约9%。由于降低了硬件需求和实现成本,该诊断工具可复制且价格合理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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