Detection of cervical lesion region from colposcopic images based on feature reselection

Bai, Bing; Du, Yongzhao; Liu, Peizhong; Sun, Pengming; Li, Ping; Lv, Yuchun

doi:10.1016/j.bspc.2019.101785

Cited by 39 publications

(13 citation statements)

References 17 publications

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“…From 1997—when cervical cancer screening was performed for the first time with AI—until today, various machine learning algorithms have been applied for the detection of cervical cancer [ 30 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 ]. Common machine learning (ML) models included deep learning (DL), k-nearest neighbors (KNN), artificial neural network (ANN), decision tree (DT), random forest (RF), support vector machine (SVM), logistic regression (LR), synthetic minority oversampling technique (SMOTE), convolutional neural network (CNN), multilayer perceptron (MLP), deep neural networks (DNN), the PAPNET test, and ResNet (residual neural network or a combination of techniques) [ 36 , 39 , 45 , 46 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 ,…”

Section: Resultsmentioning

confidence: 99%

“…AI-assisted tools appear to be very suitable for the cervical cancer diagnostic protocol, which recommends colposcopy in cases of an abnormal PAP smear and/or high-risk HPV and the collection of diagnostic tissue samples before initiating any potentially invasive treatment [ 138 ]. In response to this demand, a few notable studies were published on the use of AI in colposcopy for the detection of cervical cancer [ 69 , 70 , 71 , 72 , 73 , 77 , 79 , 137 , 138 , 139 , 140 , 141 , 142 , 143 , 144 , 145 , 146 , 147 , 148 , 149 , 150 , 151 , 152 , 153 , 154 , 155 , 156 ]. According to several investigations, the AI diagnostic approach could support or even potentially replace conventional colposcopy, permit more objective tissue specimen sampling, and reduce the number of cervical cancer cases in developing nations by offering an economical screening option in low-resource settings [ 137 , 141 ].…”

Section: Resultsmentioning

confidence: 99%

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Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review

et al. 2022

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Objective: The likelihood of timely treatment for cervical cancer increases with timely detection of abnormal cervical cells. Automated methods of detecting abnormal cervical cells were established because manual identification requires skilled pathologists and is time consuming and prone to error. The purpose of this systematic review is to evaluate the diagnostic performance of artificial intelligence (AI) technologies for the prediction, screening, and diagnosis of cervical cancer and pre-cancerous lesions. Materials and Methods: Comprehensive searches were performed on three databases: Medline, Web of Science Core Collection (Indexes = SCI-EXPANDED, SSCI, A & HCI Timespan) and Scopus to find papers published until July 2022. Articles that applied any AI technique for the prediction, screening, and diagnosis of cervical cancer were included in the review. No time restriction was applied. Articles were searched, screened, incorporated, and analyzed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. Results: The primary search yielded 2538 articles. After screening and evaluation of eligibility, 117 studies were incorporated in the review. AI techniques were found to play a significant role in screening systems for pre-cancerous and cancerous cervical lesions. The accuracy of the algorithms in predicting cervical cancer varied from 70% to 100%. AI techniques make a distinction between cancerous and normal Pap smears with 80–100% accuracy. AI is expected to serve as a practical tool for doctors in making accurate clinical diagnoses. The reported sensitivity and specificity of AI in colposcopy for the detection of CIN2+ were 71.9–98.22% and 51.8–96.2%, respectively. Conclusion: The present review highlights the acceptable performance of AI systems in the prediction, screening, or detection of cervical cancer and pre-cancerous lesions, especially when faced with a paucity of specialized centers or medical resources. In combination with human evaluation, AI could serve as a helpful tool in the interpretation of cervical smears or images.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review

et al. 2022

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“…( 7 ) used the Gabor filter method. Meanwhile, most researchers have used K-means, which is a machine learning algorithm ( 8 , 9 ), and ( 10 ). However, these methods are sensitive to noise and have the defect of over-segmentation.…”

Section: Related Workmentioning

confidence: 99%

Segmentation of the cervical lesion region in colposcopic images based on deep learning

Fan

et al. 2022

Front. Oncol.

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BackgroundColposcopy is an important method in the diagnosis of cervical lesions. However, experienced colposcopists are lacking at present, and the training cycle is long. Therefore, the artificial intelligence-based colposcopy-assisted examination has great prospects. In this paper, a cervical lesion segmentation model (CLS-Model) was proposed for cervical lesion region segmentation from colposcopic post-acetic-acid images and accurate segmentation results could provide a good foundation for further research on the classification of the lesion and the selection of biopsy site.MethodsFirst, the improved Faster Region-convolutional neural network (R-CNN) was used to obtain the cervical region without interference from other tissues or instruments. Afterward, a deep convolutional neural network (CLS-Net) was proposed, which used EfficientNet-B3 to extract the features of the cervical region and used the redesigned atrous spatial pyramid pooling (ASPP) module according to the size of the lesion region and the feature map after subsampling to capture multiscale features. We also used cross-layer feature fusion to achieve fine segmentation of the lesion region. Finally, the segmentation result was mapped to the original image.ResultsExperiments showed that on 5455 LSIL+ (including cervical intraepithelial neoplasia and cervical cancer) colposcopic post-acetic-acid images, the accuracy, specificity, sensitivity, and dice coefficient of the proposed model were 93.04%, 96.00%, 74.78%, and 73.71%, respectively, which were all higher than those of the mainstream segmentation model.ConclusionThe CLS-Model proposed in this paper has good performance in the segmentation of cervical lesions in colposcopic post-acetic-acid images and can better assist colposcopists in improving the diagnostic level.

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“…At present, many researches on the intelligent diagnosis of colposcopic images do not consider the impact of SR regions [ 8 , 9 ] or just perform simple threshold processing to eliminate reflective pixels. Only a few studies on the recognition and classification of cervical lesions have considered the interference of SR regions.…”

Section: Related Workmentioning

confidence: 99%

Integration of Global and Local Features for Specular Reflection Inpainting in Colposcopic Images

Wang

et al. 2021

Journal of Healthcare Engineering

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Objective. To explore an inpainting method that can balance texture details and visual observability to eliminate the specular reflection (SR) regions in the colposcopic image, thus improving the accuracy of clinical diagnosis for cervical cancer. Methods. (1) To ensure smoothness, Gaussian Blur and filling methods are applied to the global image. (2) Striving to preserve the anatomical texture details of the colposcopic image as much as possible, the exemplar-based method is applied to local blocks. (3) The colposcopic images inpainted in the previous two steps are integrated, so that important information of non-SR regions is preserved based on eliminating SR regions. Results. In the subjective visual assessment of inpainting results, the average of 3.55 ranks first in the five comparison sets. As to the clinical test, comparing the diagnosis results of 6 physicians before and after eliminating SR regions, the average accuracy of two kinds of classifications increased by 1.44% and 2.03%, respectively. Conclusions. This method can effectively eliminate the SR regions in the colposcopy image and present a satisfactory visual effect. Significance. As a preprocessing method for computer-aided diagnosis systems, it can also improve physicians’ accuracy in clinical diagnosis.

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Detection of cervical lesion region from colposcopic images based on feature reselection

Cited by 39 publications

References 17 publications

Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review

Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review

Segmentation of the cervical lesion region in colposcopic images based on deep learning

Integration of Global and Local Features for Specular Reflection Inpainting in Colposcopic Images

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