卷积神经网络在皮肤癌病灶分类与检测中的应用

Abdala Nour, B. Boufama
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引用次数: 3

摘要

皮肤癌是最常见的癌症之一,在世界范围内广泛传播。通过早期、准确的诊断,治疗皮肤癌的机会很高。这启发了我们设计一个深度学习模型,使用传统的神经网络来自动分类和检测不同类型的皮肤癌图像。通过这种方式,该系统采取措施预防和早期发现皮肤癌,从而找到潜在的最佳治疗方法。本研究的目的是应用基于卷积神经网络的系统元启发式优化和图像检测技术,高效准确地检测和分类不同类型的皮肤病变。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Convolutional Neural Network Strategy for Skin Cancer Lesions Classifications and Detections
Skin cancer is one of the most common forms of cancer that has widespread as a disease around the world. With early, accurate diagnosis, the chances of treating skin cancer are high. This has inspired us to design a deep learning model that uses a conventional neural network to automatically classify and detect different types of skin cancer images. Through this way the system takes actions to prevent and early detect skin cancer, leading to potentially the best approach for treatment. The goal of this research is to apply the systematic meta heuristic optimization and image detection techniques based on a convolutional neural network to efficiently and accurately detect and classify different types of skin lesions.
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