Raman Spectroscopy of Individual Cervical Exfoliated Cells in Premalignant and Malignant Lesions

Silva-López, Mariana Sarai; Ilizaliturri-Hernández, César A.; Contreras, Hugo Ricardo Navarro; Vázquez, Ángel Gabriel Rodríguez; Ortiz-Dosal, Alejandra; Kolosovas-Machuca, Eleazar Samuel

doi:10.3390/app12052419

Cited by 3 publications

(2 citation statements)

References 41 publications

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“…In fact, when patients are infected with human papillomavirus (HPV), the cervical epithelial cells undergo various morphological changes, including decreased maturation and abnormal proliferation of squamous epithelial cells [13]. This process is referred to as dysplasia, characterized by loss of squamous cell polarity, nuclear enlargement, increased nuclear-to-cytoplasmic ratio, hyperchromasia, and nuclear condensation [14,15]. These phenomena often indicate a higher possibility of cervical cancer progression [16].…”

Section: Introductionmentioning

confidence: 99%

“…These phenomena often indicate a higher possibility of cervical cancer progression [16]. Based on this principle, some researchers have used cytological analysis of cervical scrapings obtained through cell brushing to identify abnormal cervical cells, thereby achieving reliable cervical cancer diagnosis [11,14]. Unfortunately, at present, there is a lack of doctors and diagnostic experience among cervical cytology readers, making it difficult to meet the screening demand for cervical cancer [17].…”

Section: Introductionmentioning

confidence: 99%

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High Precision Cervical Precancerous Lesion Classification Method Based on ConvNeXt

Tang,

Zhang,

Gong

et al. 2023

Bioengineering

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Traditional cervical cancer diagnosis mainly relies on human papillomavirus (HPV) concentration testing. Considering that HPV concentrations vary from individual to individual and fluctuate over time, this method requires multiple tests, leading to high costs. Recently, some scholars have focused on the method of cervical cytology for diagnosis. However, cervical cancer cells have complex textural characteristics and small differences between different cell subtypes, which brings great challenges for high-precision screening of cervical cancer. In this paper, we propose a high-precision cervical cancer precancerous lesion screening classification method based on ConvNeXt, utilizing self-supervised data augmentation and ensemble learning strategies to achieve cervical cancer cell feature extraction and inter-class discrimination, respectively. We used the Deep Cervical Cytological Levels (DCCL) dataset, which includes 1167 cervical cytology specimens from participants aged 32 to 67, for algorithm training and validation. We tested our method on the DCCL dataset, and the final classification accuracy was 8.85% higher than that of previous advanced models, which means that our method has significant advantages compared to other advanced methods.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

High Precision Cervical Precancerous Lesion Classification Method Based on ConvNeXt

Tang,

Zhang,

Gong

et al. 2023

Bioengineering

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Raman spectroscopic deep learning with signal aggregated representations for enhanced cell phenotype and signature identification

Lu,

Huang,

Shen

et al. 2024

PNAS Nexus

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Feature representation is critical for data learning, particularly in learning spectroscopic data. Machine learning (ML) and deep learning (DL) models learn Raman spectra for rapid, non-destructive, and label-free cell phenotype identification, which facilitate diagnostic, therapeutic, forensic, and microbiological applications. But these are challenged by high-dimensional, unordered and low-sample spectroscopic data. Here we introduced novel 2D image-like dual signal and component aggregated representations by restructuring Raman spectra and principal components, which enables spectroscopic DL for enhanced cell phenotype and signature identification. New ConvNet models DSCARNets significantly outperformed the state-of-the-art (SOTA) ML and DL models on six benchmark datasets, mostly with >2% improvement over the SOTA performance of 85%-97% accuracies. DSCARNets also performed well on four additional datasets against SOTA models of extremely high performances (>98%) and two datasets without a published supervised phenotype classification model. Explainable DSCARNets identified Raman signatures consistent with experimental indications.

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Hybrid Techniques for Diagnosis with WSIs for Early Detection of Cervical Cancer Based on Fusion Features

et al. 2022

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Cervical cancer is a global health problem that threatens the lives of women. Liquid-based cytology (LBC) is one of the most used techniques for diagnosing cervical cancer; converting from vitreous slides to whole-slide images (WSIs) allows images to be evaluated by artificial intelligence techniques. Because of the lack of cytologists and cytology devices, it is major to promote automated systems that receive and diagnose huge amounts of images quickly and accurately, which are useful in hospitals and clinical laboratories. This study aims to extract features in a hybrid method to obtain representative features to achieve promising results. Three proposed approaches have been applied with different methods and materials as follows: The first approach is a hybrid method called VGG-16 with SVM and GoogLeNet with SVM. The second approach is to classify the cervical abnormal cell images by ANN classifier with hybrid features extracted by the VGG-16 and GoogLeNet. A third approach is to classify the images of abnormal cervical cells by an ANN classifier with features extracted by the VGG-16 and GoogLeNet and combine them with hand-crafted features, which are extracted using Fuzzy Color Histogram (FCH), Gray Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP) algorithms. Based on the mixed features of CNN with features of FCH, GLCM, and LBP (hand-crafted), the ANN classifier reached the best results for diagnosing abnormal cells of the cervix. The ANN network achieved with the hybrid features of VGG-16 and hand-crafted an accuracy of 99.4%, specificity of 100%, sensitivity of 99.35%, AUC of 99.89% and precision of 99.42%.

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Raman Spectroscopy of Individual Cervical Exfoliated Cells in Premalignant and Malignant Lesions

Cited by 3 publications

References 41 publications

High Precision Cervical Precancerous Lesion Classification Method Based on ConvNeXt

High Precision Cervical Precancerous Lesion Classification Method Based on ConvNeXt

Raman spectroscopic deep learning with signal aggregated representations for enhanced cell phenotype and signature identification

Hybrid Techniques for Diagnosis with WSIs for Early Detection of Cervical Cancer Based on Fusion Features

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