AN AUTOMATED SKIN DISEASE DIAGNOSTIC SYSTEM BASED ON DEEP LEARNING MODEL

L. Omotosho, K. Sotonwa, B. Adegoke, Oluwashina A. Oyeniran, Joshua O. Oyeniyi
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Abstract

The use of computer technology has significantly advanced the medical sector, and many computer technologies have been used to develop healthcare, such as the patient management system, monitoring and control systems, and diagnostic systems. Technological advances in healthcare have also helped in saving numerous patients and are constantly improving our quality of life. Technology in the medical sector has also had a major effect on almost all healthcare professional techniques and practices. In order to facilitate rapid diagnosis and treatment of different skin diseases by the use of a deep learning model, this study developed a comprehensive framework to improve the decision-making of dermatologists in Nigeria in terms of the diagnosis of selected skin diseases. The developed system achieved the network accuracy of 98.44 % and the validation accuracy of the test set is 99.44 % as specified by the training results, further testing reveal that the developed system yielded rejection rate of 2.2 % and recognition accuracy of 97.8 %.
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基于深度学习模型的皮肤病自动诊断系统
计算机技术的使用极大地促进了医疗部门的发展,许多计算机技术已被用于开发医疗保健,如患者管理系统、监测和控制系统以及诊断系统。医疗保健的技术进步也帮助挽救了许多患者,并不断提高我们的生活质量。医疗部门的技术也对几乎所有的医疗专业技术和实践产生了重大影响。为了通过使用深度学习模型促进不同皮肤病的快速诊断和治疗,本研究开发了一个全面的框架,以改进尼日利亚皮肤科医生在诊断选定皮肤病方面的决策。根据训练结果,所开发的系统实现了98.44%的网络准确率和99.44%的测试集验证准确率,进一步的测试表明,所开发系统的拒绝率为2.2%,识别准确率为97.8%。
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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审稿时长
6 weeks
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