Classifying breast cancer tissue by Raman spectroscopy with one-dimensional convolutional neural network

Ma, Dan-Ying; Shang, Linwei; Tang, Jinlan; Bao, Yilin; Fu, Juanjuan; Yin, Jianhua

doi:10.1016/j.saa.2021.119732

Cited by 89 publications

(56 citation statements)

References 22 publications

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“…CNN has also been used to classify a variety of different biological samples using their Raman spectroscopy data including different strains of E. Coli bacteria cells [24], porcine skin samples which were irradiated by ultraviolet light for different durations [25], and breast cancer tissue samples [26]. In one study [24], the CNN ResNet [27] could successfully classify different strains of E. Coli bacteria from highly noisy Raman signals obtained using surface enhanced Raman spectroscopy (SERS) [28].…”

Section: Introductionmentioning

confidence: 99%

“…They obtained a classification accuracy of 96.4% and 92.5% from preprocessed and raw spectra, respectively. Baseline corrected breast cancer samples have also been successfully classified using CNN [26]. In that study, they used data augmentation to increase their training set size from 600 to 5000 spectra using methods such as small spectral shifting (2cm −1 ), expanding the spectral range from 3000 cm −1 to 3600 cm −1 , adding Gaussian noise the spectra, and finally super imposing the spectra within a cluster.…”

Section: Introductionmentioning

confidence: 99%

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Deep Convolutional Neural Networks as a Unified Solution for Raman Spectroscopy-Based Classification in Biomedical Applications

Kazemzadeh¹,

Hisey²,

Zargar³

et al. 2021

Preprint

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<div>Machine learning has shown great potential for classifying diverse samples in biomedical applications based on their Raman spectra. However, the acquired spectra typically require several preprocessing steps before standard machine learning algorithms can accurately and reliably classify them. To simplify this workflow and enable future growth of this technology, we present a unified solution for classifying biological Raman spectra without any need of prepossessing, including denoising and baseline establishment. This method is developed based on a custom version of a convolutional neural network (CNN) elicited from ResNet architecture, combined with our proposed data augmentation technique. The superiority of this method compared to conventional classification techniques is shown by applying it to Raman spectra of different grades of bladder cancer tissue and surface enhanced Raman spectroscopy (SERS) spectra of various strains of E. Coli extracellular vesicles (EVs). These results show that our method is far more robust compared to its conventional counterparts when dealing with the various kinds of spectral baselines produced by different Raman spectrometers.</div>

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Networks as a Unified Solution for Raman Spectroscopy-Based Classification in Biomedical Applications

Kazemzadeh¹,

Hisey²,

Zargar³

et al. 2021

Preprint

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“…Ma et al also classified breast tissue using a CNN [ 27 ]. They compared its performance against four SVMs (each with a different kernel) and Fisher’s Discriminant Analysis (FDA).…”

Section: Resultsmentioning

confidence: 99%

Machine Learning of Raman Spectroscopy Data for Classifying Cancers: A Review of the Recent Literature

et al. 2022

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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 inhibited their routine use in clinical settings. Traditional machine learning models have been used to help exploit this information, but recent advances in deep learning have the potential to improve the field. However, there are a number of potential pitfalls with both traditional and deep learning models. We conduct a literature review to ascertain the recent machine learning methods used to classify cancers using Raman spectral data. We find that while deep learning models are popular, and ostensibly outperform traditional learning models, there are many methodological considerations which may be leading to an over-estimation of performance; primarily, small sample sizes which compound sub-optimal choices regarding sampling and validation strategies. Amongst several recommendations is a call to collate large benchmark Raman datasets, similar to those that have helped transform digital pathology, which researchers can use to develop and refine deep learning models.

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“…is filter is used in particular for the preprocessing of medical images [8]. e procedure of this filter is summarized as follows [7]:…”

Section: Image Preprocessingmentioning

confidence: 99%

“…Ma et al [8] presented that a 1D-CNN model was developed and trained for classification. e Fisher discrimination analysis (FDA) and support vector machine (SVM) classifiers were trained and tested with the same spectral data for comparison.…”

Section: Introductionmentioning

confidence: 99%

Automatic Breast Tumor Diagnosis in MRI Based on a Hybrid CNN and Feature‐Based Method Using Improved Deer Hunting Optimization Algorithm

Vahedi

2021

Computational Intelligence and Neuroscience

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Breast cancer is an unusual mass of the breast texture. It begins with an abnormal change in cell structure. This disease may increase uncontrollably and affects neighboring textures. Early diagnosis of this cancer (abnormal cell changes) can help definitively treat it. Also, prevention of this cancer can help to decrease the high cost of medical caring for breast cancer patients. In recent years, the computer-aided technique is an important active field for automatic cancer detection. In this study, an automatic breast tumor diagnosis system is introduced. An improved Deer Hunting Optimization Algorithm (DHOA) is used as the optimization algorithm. The presented method utilized a hybrid feature-based technique and a new optimized convolutional neural network (CNN). Simulations are applied to the DCE-MRI dataset based on some performance indexes. The novel contribution of this paper is to apply the preprocessing stage to simplifying the classification. Besides, we used a new metaheuristic algorithm. Also, the feature extraction by Haralick texture and local binary pattern (LBP) is recommended. Due to the obtained results, the accuracy of this method is 98.89%, which represents the high potential and efficiency of this method.

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Classifying breast cancer tissue by Raman spectroscopy with one-dimensional convolutional neural network

Cited by 89 publications

References 22 publications

Deep Convolutional Neural Networks as a Unified Solution for Raman Spectroscopy-Based Classification in Biomedical Applications

Deep Convolutional Neural Networks as a Unified Solution for Raman Spectroscopy-Based Classification in Biomedical Applications

Machine Learning of Raman Spectroscopy Data for Classifying Cancers: A Review of the Recent Literature

Automatic Breast Tumor Diagnosis in MRI Based on a Hybrid CNN and Feature‐Based Method Using Improved Deer Hunting Optimization Algorithm

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