使用卷积神经网络预测皮肤癌

Karthikayani. K, Sadhana S, L. C, Suman A. Patil, Anandbabu Gopatoti, Shanta Phani
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引用次数: 2

摘要

皮肤损伤是最致命的疾病之一。一开始的独立解剖和处理会导致它接触到身体的其他部位。由于皮肤细胞的快速发展,当暴露在阳光下时就会发生这种情况。一种皮肤溃疡识别的自动化系统有望减少早期发现的工作量、时间和人类生命。一种预防皮肤癌的技术是基于图像和重要的学习。本文提出了一种用于表示皮肤感染的增强系统。本研究收集了9种皮肤病。它包含了九种临床显著的皮肤癌,如光化性角化病、基底细胞癌、无害角化病、皮肤纤维瘤、黑色素瘤、痣、脂溢性角化病和鳞状细胞癌。卷积神经网络被用来对皮肤疾病进行分类并诊断其严重程度。在诊断系统中,既考虑了图像,又利用了显著学习。通过使用不同的方法,还添加了更多的图片。最后,深度学习方法也有助于确保任务的精度。采用本文提出的CNN方法,平均准确率达到96.12%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of Skin Cancer using Convolutional Neural Network
Skin damage is one of the most fatal illnesses. Unassisted dissection and management at the beginning will result in it contacting other areas of the body. Due to the rapid progression of skin cells, it occurs when exposed to sunlight An automated system for skin sore recognition is expected to reduce effort, time, and human life for early detection. A technique for preventing skin cancer is based on both images and significant learning. An enhanced system is proposed in the paper for representing skin infections. In this study, nine types of skin illnesses were collected. It contains nine clinically significant skin cancers, such as actinic keratosis, basal cell carcinoma, innocuous keratosis, dermatofibromas, melanomas, nevus, seborrheic keratosis, and squamous cell carcinoma. Convolutional Neural Networks are used to classify skin diseases into various classes as well as diagnose the severity of them. In the diagnosis system, pictures are taken into consideration as well as significant learning is utilized. More pictures have also been added by using different methodologies. Finally, the deep learning approach also helps to ensure the task's precision. By using the proposed CNN procedure, an average accuracy of 96.12 percent is obtained.
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