1. RaT: Raman Transformer for highly accurate melanoma detection with critical features visualization.
- Author
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Chang M, He C, Du Y, Qiu Y, Wang L, and Chen H
- Subjects
- Humans, Rats, Animals, Neural Networks, Computer, Diagnosis, Differential, Spectrum Analysis, Raman methods, Melanoma diagnosis, Skin Neoplasms chemistry, Skin Neoplasms diagnosis
- Abstract
Melanoma is an important cause of death from skin cancer. Early and accurate diagnosis can effectively reduce mortality. But the current diagnosis relies on the experience of pathologists, increasing the rate of misdiagnosis. In this paper, Raman Transformer (RaT) model is proposed by combining Raman spectroscopy and a Transformer encoder to distinguish the Raman spectra of melanoma and normal tissue. To make the spectral data more suitable for the Transformer encoder, we split the Raman spectrum into segments and map them into block vectors, which are then input into the Transformer encoder and classified using the multi-head self-attention mechanism and the Multilayer Perceptron (MLP). The RaT model achieves 99.69% accuracy, 99.61% sensitivity, and 99.82% specificity, which is higher than the classical principal component analysis with the neural network (PCA + NNET) method. In addition, we visualize and explain the fingerprint peaks found by the RaT model and their corresponding biological information. Our proposed RaT model provides a novel and reliable method for processing Raman spectral data, which is expected to help distinguish melanoma from normal cells, diagnose other diseases, and save human lives., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Min Chang reports financial support was provided by National Natural Science Foundation of China., (Copyright © 2023 Elsevier B.V. All rights reserved.)
- Published
- 2024
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