Department of Computer Science and Engineering, School of Computing, Vel Tech Rangarajan Dr Sangunthala R &D Institute of Science and Technology, Chennai, India.
Department of Computer Science and Engineering, SNS College of Technology, Coimbatore, India.
Comput Intell Neurosci. 2022 Jul 4;2022:9539503. doi: 10.1155/2022/9539503. eCollection 2022.
Skin disease is the major health problem around the world. The diagnosis of skin disease remains a challenge to dermatologist profession particularly in the detection, evaluation, and management. Health data are very large and complex due to this processing of data using traditional data processing techniques is very difficult. In this paper, to ease the complexity while processing the inputs, we use multilayered perceptron with backpropagation neural networks (MLP-BPNN). The image is collected from the devices that contain nanotechnology sensors, which is the state-of-art in the proposed model. The nanotechnology sensors sense the skin for its chemical, physical, and biological conditions with better detection specificity, sensitivity, and multiplexing ability to acquire the image for optimal classification. The MLP-BPNN technique is used to envisage the future result of disease type effectively. By using the above MLP-BPNN technique, it is easy to predict the skin diseases such as melanoma, nevus, psoriasis, and seborrheic keratosis.
皮肤病是全世界主要的健康问题。皮肤病的诊断仍然是皮肤科医生面临的一个挑战，特别是在检测、评估和管理方面。由于健康数据非常庞大和复杂，因此使用传统的数据处理技术来处理这些数据非常困难。在本文中，为了在处理输入时减轻复杂性，我们使用具有反向传播神经网络 (MLP-BPNN) 的多层感知器。图像是从包含纳米技术传感器的设备中收集的，这是所提出模型中的最新技术。纳米技术传感器可以感知皮肤的化学、物理和生物状况，具有更好的检测特异性、灵敏度和多重检测能力，从而获取用于最佳分类的图像。MLP-BPNN 技术可用于有效地预测疾病类型的未来结果。通过使用上述 MLP-BPNN 技术，很容易预测黑色素瘤、痣、银屑病和脂溢性角化病等皮肤病。
