Machine learning of Raman spectroscopy data for classifying cancers: a review of the recent literature
N Blake, R Gaifulina, LD Griffin, IM Bell, GMH Thomas - Diagnostics, 2022 - mdpi.com
Raman Spectroscopy has long been anticipated to augment clinical decision making, such
as classifying oncological samples. Unfortunately, the complexity of Raman data has thus far …
as classifying oncological samples. Unfortunately, the complexity of Raman data has thus far …
Novel aspects of Raman spectroscopy in skin research
The analytical technology of Raman spectroscopy has an almost 100‐year history. During
this period, many modifications and developments happened in the method like discovery of …
this period, many modifications and developments happened in the method like discovery of …
Classification of skin cancer using convolutional neural networks analysis of Raman spectra
IA Bratchenko, LA Bratchenko, YA Khristoforova… - Computer Methods and …, 2022 - Elsevier
Background and objective Skin cancer is the most common malignancy in whites accounting
for about one third of all cancers diagnosed per year. Portable Raman spectroscopy setups …
for about one third of all cancers diagnosed per year. Portable Raman spectroscopy setups …
Toward smart diagnostics via artificial intelligence-assisted surface-enhanced Raman spectroscopy
Molecular information contained in bodily fluids (ex. Blood, urine, saliva, or tears) can be
minutely obtained through label-free surface-enhanced Raman spectroscopy (SERS) …
minutely obtained through label-free surface-enhanced Raman spectroscopy (SERS) …
Skin lesion synthesis and classification using an improved DCGAN classifier
The prognosis for patients with skin cancer improves with regular screening and checkups.
Unfortunately, many people with skin cancer do not receive a diagnosis until the disease …
Unfortunately, many people with skin cancer do not receive a diagnosis until the disease …
Visualization of a machine learning framework toward highly sensitive qualitative analysis by SERS
S Luo, W Wang, Z Zhou, Y Xie, B Ren, G Liu… - Analytical …, 2022 - ACS Publications
Surface-enhanced Raman spectroscopy (SERS), providing near-single-molecule-level
fingerprint information, is a powerful tool for the trace analysis of a target in a complicated …
fingerprint information, is a powerful tool for the trace analysis of a target in a complicated …
Utilization of synthetic near-infrared spectra via generative adversarial network to improve wood stiffness prediction
Near-infrared (NIR) spectroscopy is widely used as a nondestructive evaluation (NDE) tool
for predicting wood properties. When deploying NIR models, one faces challenges in …
for predicting wood properties. When deploying NIR models, one faces challenges in …
Tackling the class imbalanced dermoscopic image classification using data augmentation and GAN
Dermoscopy is a noninvasive way to examine and diagnose skin lesions, eg nevus and
melanoma, and is a critical step for skin cancer detection. Accurate classification of …
melanoma, and is a critical step for skin cancer detection. Accurate classification of …
Comparison of the performance of different one-dimensional convolutional neural network models-based near-infrared spectra for determination of chlorpyrifos …
Y Xue, C Zhu, H Jiang - Infrared Physics & Technology, 2023 - Elsevier
This study introduces a fast analytical approach for detecting chlorpyrifos residues in corn
oil, based on a one-dimensional convolutional neural network (1D-CNN) structure for near …
oil, based on a one-dimensional convolutional neural network (1D-CNN) structure for near …
A comparative analysis of data synthesis techniques to improve classification accuracy of raman spectroscopy data
AR Flanagan, FG Glavin - Journal of Chemical Information and …, 2023 - ACS Publications
Raman spectra are examples of high dimensional data that can often be limited in the
number of samples. This is a primary concern when Deep Learning frameworks are …
number of samples. This is a primary concern when Deep Learning frameworks are …