Mask region-based CNNs for cervical cancer progression diagnosis on pap smear examinations

Rutili de Lima, Carolina; Khan, Said G.; Shah, Syed H.; Ferri, Luthiari

doi:10.1016/j.heliyon.2023.e21388

Cited by 6 publications

(1 citation statement)

References 29 publications

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“…Subsequent scholars have continued this idea, and Sellamuthu et al [51] proposed an improved deep learning algorithm based on a double-tree complex wavelet transform (DTCWT). De et al [52] introduced a mask-region-based CNN method, which also achieved good segmentation performance. Wita et al [53] proceeded from the perspective of features.…”

Section: Segmentation Of Pathological Cellsmentioning

confidence: 99%

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: Segmentation Of Pathological Cellsmentioning

confidence: 99%

High Precision Cervical Precancerous Lesion Classification Method Based on ConvNeXt

Tang,

Zhang,

Gong

et al. 2023

Bioengineering

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Improving cervical cancer classification in PAP smear images with enhanced segmentation and deep progressive learning‐based techniques

Mahajan,

Kaur

2024

Diagnostic Cytopathology

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ObjectiveCervical cancer, a prevalent and deadly disease among women, comes second only to breast cancer, with over 700 daily deaths. The Pap smear test is a widely utilized screening method for detecting cervical cancer in its early stages. However, this manual screening process is prone to a high rate of false‐positive outcomes because of human errors. Researchers are using machine learning and deep learning in computer‐aided diagnostic tools to address this issue. These tools automatically analyze and sort cervical cytology and colposcopy images, improving the precision of identifying various stages of cervical cancer.MethodologyThis article uses state‐of‐the‐art deep learning methods, such as ResNet‐50 for categorizing cervical cancer cells to assist medical professionals. The method includes three key steps: preprocessing, segmentation using k‐means clustering, and classifying cancer cells. The model is assessed based on performance metrics viz; precision, accuracy, kappa score, precision, sensitivity, and specificity. In the end, the high success rate shows that the ResNet50 model is a valuable tool for timely detection of cervical cancer.OutputsIn conclusion, the infected cervical region is pinpointed using spatial K‐means clustering and preprocessing operations. This sequence of actions is followed by a progressive learning technique. The Progressive Learning technique then proceeded through several stages: Stage 1 with 64 × 64 images, Stage 2 with 224 × 224 images, Stage 3 with 512 × 512 images, and the final Stage 4 with 1024 × 1024 images. The outcomes show that the suggested model is effective for analyzing Pap smear tests, achieving 97.4% accuracy and approx. 98% kappa score.

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Precise Identification of Different Cervical Intraepithelial Neoplasia (CIN) Stages, Using Biomedical Engineering Combined with Data Mining and Machine Learning

Kruczkowski,

Drabik-Kruczkowska,

Wesołowski

et al. 2024

Interdisciplinary Cancer Research

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Mask region-based CNNs for cervical cancer progression diagnosis on pap smear examinations

Cited by 6 publications

References 29 publications

High Precision Cervical Precancerous Lesion Classification Method Based on ConvNeXt

High Precision Cervical Precancerous Lesion Classification Method Based on ConvNeXt

Improving cervical cancer classification in PAP smear images with enhanced segmentation and deep progressive learning‐based techniques

Precise Identification of Different Cervical Intraepithelial Neoplasia (CIN) Stages, Using Biomedical Engineering Combined with Data Mining and Machine Learning

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