Computer-aided diagnosis of cervical dysplasia using colposcopic images

Ma, Jinghang; You, Shang-Feng; Xue, Ji-Sen; Li, Xin; Chen, Yiyao; Hu, Yan; Feng, Zhen

doi:10.3389/fonc.2022.905623

Cited by 5 publications

(3 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The gold standard for diagnosing CIN and invasive cervical cancer involves a colposcopy-guided biopsy followed by a subsequent pathological diagnosis. However, challenges such as a shortage of well-trained colposcopists, weak correlation between visual and pathological diagnoses, and disagreements among experts 6,7 contribute to the inadequate sensitivity and specificity of colposcopy, particularly in developing countries. 8,9 Computer-Aided Medical Diagnosis (CMD) overcomes the limitations associated with manual examination, offering a diagnostic tool that is more reliable and efficient.…”

Section: Introductionmentioning

confidence: 99%

Multifractal analysis for detection of different stages in cervical cancer from colposcopy images

Singha Deo,

Pal,

Panigrahi

et al. 2024

Optical Biopsy XXII: Toward Real-Time Spectroscopic Imaging and Diagnosis

View full text Add to dashboard Cite

Cervical cancer is of significant health concern globally, particularly in developing countries where access to advanced healthcare facilities and medical resources is limited, leading to increased mortality rates. The gold standard for diagnosing CIN (cervical intraepithelial neoplasia) and invasive cervical cancer involves performing a colposcopy-guided biopsy followed by a pathological diagnosis. However, its effectiveness is challenged by limited sensitivity in distinguishing between various stages of cervical cancer, especially in regions where there is a shortage of skilled colposcopists and insufficient access to medical resources. This study presents a method for categorizing infectious, pre-cancerous, and cancerous conditions through the application of multifractal analysis, specifically two dimensional multifractal detrended fluctuation analysis (2D MFDFA), using images obtained through colposcopy. The utilization of multifractal parameters, namely the generalized Hurst exponent and the width of the singularity spectrum, in the analysis distinctly demonstrated variations among the infectious, precancerous, and cancerous conditions. Therefore, it offers valuable insights to healthcare professionals, assisting in the accurate classification and effective management of cervical cancer using Hurst exponent and multifractal spectrum width.

show abstract

Section: Introductionmentioning

confidence: 99%

Multifractal analysis for detection of different stages in cervical cancer from colposcopy images

Singha Deo,

Pal,

Panigrahi

et al. 2024

Optical Biopsy XXII: Toward Real-Time Spectroscopic Imaging and Diagnosis

View full text Add to dashboard Cite

show abstract

“…Investigations in the areas of optical coherence tomography, radiology, computerized tomography scan, colonoscopy, and pathologic slides have suggested that computer algorithms, trained on a large number of medical images in a convolutional neural network (CNN), may approach or even exceed the diagnostic accuracy of clinicians. 4,5…”

Section: Introductionmentioning

confidence: 99%

Computed-Assisted Diagnosis in Diagnostic Cervical Cancer Imaging : A Ten Years Systematic Review

Kusuma Dewi,

Kurnia Dewi,

Meitadani

2024

JARMHS

View full text Add to dashboard Cite

Background: Cervical cancer is one of the most common malignant tumors in the world, and it is the fourth leading cause of cancer in women. The morbidity and mortality of cervical cancer in the developing countries are distinctly higher than those in the developed countries. Computer-assisted diagnosis is key for scaling up cervical cancer screening, but current algorithms perform poorly on whole slide image analysis and generalization. The aim: This study aims to show about computed-assisted diagnosis in diagnostic cervical cancer imaging. Methods: By comparing itself to the standards set by the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2020, this study was able to show that it met all of the requirements. So, the experts were able to make sure that the study was as up-to-date as it was possible to be. For this search approach, publications that came out between 2014 and 2024 were taken into account. Several different online reference sources, like Pubmed and SagePub, were used to do this. It was decided not to take into account review pieces, works that had already been published, or works that were only half done. Result: In the PubMed database, the results of our search brought up 153 articles, whereas the results of our search on SagePub brought up 180 articles. The results of the search conducted for the last year of 2014 yielded a total 53 articles for PubMed and 72 articles for SagePub. The result from title screening, a total 18 articles for PubMed and 27 articles for SagePub. In the end, we compiled a total of 10 papers. We included five research that met the criteria. Conclusion: Computed-assisted medical diagnosis can successfully complete a variety of medical tasks by efficiently exploring the essence of a large amount of clinical data. The colposcopy-guided cervical biopsy is essential for detecting CIN in cervical cancer screening, but there are difficulties with increasing sensitivity globally.

show abstract

“…Machine learning has already been used to help diagnose CIN [5], and various methods to classify CIN in colposcopic images have been proposed, such as methods using support vector machines [6,7] and deep learning [8,9]. Recently, huge datasets have been provided to compete for CIN grade classification performance [10] in kaggle, a well-known competition platform for data science and machine learning [11].…”

Section: Introductionmentioning

confidence: 99%

Diagnosis Assistance in Colposcopy by Segmenting Acetowhite Epithelium Using U-Net with Images before and after Acetic Acid Solution Application

2023

View full text Add to dashboard Cite

Colposcopy is an essential examination tool to identify cervical intraepithelial neoplasia (CIN), a precancerous lesion of the uterine cervix, and to sample its tissues for histological examination. In colposcopy, gynecologists visually identify the lesion highlighted by applying an acetic acid solution to the cervix using a magnifying glass. This paper proposes a deep learning method to aid the colposcopic diagnosis of CIN by segmenting lesions. In this method, to segment the lesion effectively, the colposcopic images taken before acetic acid solution application were input to the deep learning network, U-Net, for lesion segmentation with the images taken following acetic acid solution application. We conducted experiments using 30 actual colposcopic images of acetowhite epithelium, one of the representative types of CIN. As a result, it was confirmed that accuracy, precision, and F1 scores, which were 0.894, 0.837, and 0.834, respectively, were significantly better when images taken before and after acetic acid solution application were used than when only images taken after acetic acid solution application were used (0.882, 0.823, and 0.823, respectively). This result indicates that the image taken before acetic acid solution application is helpful for accurately segmenting the CIN in deep learning.

show abstract

Computer-aided diagnosis of cervical dysplasia using colposcopic images

Cited by 5 publications

References 37 publications

Multifractal analysis for detection of different stages in cervical cancer from colposcopy images

Multifractal analysis for detection of different stages in cervical cancer from colposcopy images

Computed-Assisted Diagnosis in Diagnostic Cervical Cancer Imaging : A Ten Years Systematic Review

Diagnosis Assistance in Colposcopy by Segmenting Acetowhite Epithelium Using U-Net with Images before and after Acetic Acid Solution Application

Contact Info

Product

Resources

About