Polarized Micro-Raman Spectroscopy and 2D Convolutional Neural Network Applied to Structural Analysis and Discrimination of Breast Cancer

拉曼光谱 乳腺癌 卷积神经网络 各向异性 材料科学 光谱学 人工智能 癌症 计算机科学 核磁共振 模式识别(心理学) 生物系统 光学 生物 物理 遗传学 量子力学
作者
Linwei Shang,Jinlan Tang,Jinjin Wu,Hui Shang,Xing Huang,Yilin Bao,Zhibing Xu,Huijie Wang,Jianhua Yin
出处
期刊:Biosensors [MDPI AG]
卷期号:13 (1): 65-65 被引量:8
标识
DOI:10.3390/bios13010065
摘要

Raman spectroscopy has been efficiently used to recognize breast cancer tissue by detecting the characteristic changes in tissue composition in cancerization. In addition to chemical composition, the change in bio-structure may be easily obtained via polarized micro-Raman spectroscopy, aiding in identifying the cancerization process and diagnosis. In this study, a polarized Raman spectral technique is employed to obtain rich structural features and, combined with deep learning technology, to achieve discrimination of breast cancer tissue. The results reconfirm that the orientation of collagen fibers changes from parallel to vertical during breast cancerization, and there are significant structural differences between cancerous and normal tissues, which is consistent with previous reports. Optical anisotropy of collagen fibers weakens in cancer tissue, which is closely related with the tumor's progression. To distinguish breast cancer tissue, a discrimination model is established based on a two-dimensional convolutional neural network (2D-CNN), where the input is a matrix containing the Raman spectra acquired at a set of linear polarization angles varying from 0° to 360°. As a result, an average discrimination accuracy of 96.01% for test samples is achieved, better than that of the KNN classifier and 1D-CNN that are based on non-polarized Raman spectra. This study implies that polarized Raman spectroscopy combined with 2D-CNN can effectively detect changes in the structure and components of tissues, innovatively improving the identification and automatic diagnosis of breast cancer with label-free probing and analysis.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
聪慧勒完成签到,获得积分10
2秒前
paper完成签到 ,获得积分10
2秒前
艾科研完成签到,获得积分10
3秒前
传奇3应助smile采纳,获得10
3秒前
赘婿应助沉静青旋采纳,获得10
4秒前
旧旧完成签到 ,获得积分10
5秒前
5秒前
果粒程完成签到 ,获得积分10
7秒前
ghost发布了新的文献求助10
9秒前
不会学术的羊完成签到,获得积分10
9秒前
11秒前
11秒前
独孤完成签到 ,获得积分10
12秒前
MFiWanting完成签到,获得积分10
14秒前
坦率的乐蕊完成签到 ,获得积分10
16秒前
16秒前
ccq发布了新的文献求助10
16秒前
优雅冬灵完成签到,获得积分10
18秒前
迷路的初柔完成签到 ,获得积分10
19秒前
20秒前
科研路上的干饭桶完成签到,获得积分10
20秒前
顺风顺水顺财神完成签到 ,获得积分10
21秒前
852应助栗子鱼采纳,获得10
21秒前
Martinsoar发布了新的文献求助10
22秒前
23秒前
会飞的鱼完成签到,获得积分10
23秒前
24秒前
25秒前
Zero完成签到,获得积分10
27秒前
香蕉觅云应助哈哈2022采纳,获得10
28秒前
沉静青旋发布了新的文献求助10
30秒前
31秒前
机智的紫丝完成签到,获得积分10
31秒前
janie完成签到,获得积分10
31秒前
32秒前
32秒前
轩xuan发布了新的文献求助10
36秒前
干净的人达完成签到 ,获得积分10
36秒前
loulan完成签到,获得积分10
42秒前
傅宛白完成签到,获得积分10
43秒前
高分求助中
Sustainability in Tides Chemistry 2800
Kinetics of the Esterification Between 2-[(4-hydroxybutoxy)carbonyl] Benzoic Acid with 1,4-Butanediol: Tetrabutyl Orthotitanate as Catalyst 1000
The Young builders of New china : the visit of the delegation of the WFDY to the Chinese People's Republic 1000
Rechtsphilosophie 1000
Bayesian Models of Cognition:Reverse Engineering the Mind 888
Handbook of Qualitative Cross-Cultural Research Methods 600
Very-high-order BVD Schemes Using β-variable THINC Method 568
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3137539
求助须知:如何正确求助?哪些是违规求助? 2788516
关于积分的说明 7787114
捐赠科研通 2444837
什么是DOI,文献DOI怎么找? 1300071
科研通“疑难数据库(出版商)”最低求助积分说明 625796
版权声明 601023