Cervical Cancer Diagnostics Healthcare System Using Hybrid Object Detection Adversarial Networks

Elakkiya, R.; Subramaniyaswamy, V; Vijayakumar, Vineetha; Mahanti, Anirban

doi:10.1109/jbhi.2021.3094311

Cited by 54 publications

(25 citation statements)

References 33 publications

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“…On the one hand, there are some studies on the improvement of a single model, such as ColpoNet--a classification architecture of cervical cancer based on self-learning ability [ 22 ], the image classification of cervical lesions based on regularized transfer learning strategy [ 23 ], convolutional neural network recognition based on CapsNet for cervical lesions classification [ 24 ], and the integrated CAIADS model of cervical lesion classification and detection [ 25 ]. On the other hand, there are two common decision-making methods combining the features of convolutional neural networks: Yuan et al [ 26 ] used ResNet to classify the lesion level, segmented the lesion area through U-net, and combined Mask R-CNN for final detection; Cho et al [ 27 ] combined two network models, Inception and ResNet, to classify lesions; Luo et al [ 28 ] optimized the output of the two models, RseNet50 and DenseNet121, through the strategy of decision feature integration and fusion; Elakkiya et al [ 29 ] put forward the FSOD-GAN model combining FR-CNN, GAN, and FSDAE technologies. A newer study also combined convolutional neural networks with clinical features of cervical lesions [ 30 ].…”

Section: Related Workmentioning

confidence: 99%

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Cervical Lesion Classification Method Based on Cross-Validation Decision Fusion Method of Vision Transformer and DenseNet

Wang

Liu

et al. 2022

Journal of Healthcare Engineering

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Objective. In order to better adapt to clinical applications, this paper proposes a cross-validation decision-making fusion method of Vision Transformer and DenseNet161. Methods. The dataset is the most critical acetic acid image for clinical diagnosis, and the SR areas are processed by a specific method. Then, the Vision Transformer and DenseNet161 models are trained by the fivefold cross-validation method, and the fivefold prediction results corresponding to the two models are fused by different weights. Finally, the five fused results are averaged to obtain the category with the highest probability. Results. The results show that the fusion method in this paper reaches an accuracy rate of 68% for the four classifications of cervical lesions. Conclusions. It is more suitable for clinical environments, effectively reducing the missed detection rate and ensuring the life and health of patients.

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Section: Related Workmentioning

confidence: 99%

“…At present, in the classification study of cervical lesions based on colposcopy images, multiple deep convolutional neural network (CNN) models are used for training in model fusion [ 28 , 29 ]. The core of the deep CNN lies in each convolutional layer.…”

Section: Related Workmentioning

confidence: 99%

Cervical Lesion Classification Method Based on Cross-Validation Decision Fusion Method of Vision Transformer and DenseNet

Wang

Liu

et al. 2022

Journal of Healthcare Engineering

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“…For these reasons, some studies have utilized deep learning methods to assist clinicians in colposcopy. Most studies have focused on cervical intraepithelial neoplasia (CIN) classification [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ], and almost all studies include a pre-processing step to first remove non-cervix regions such as the speculum and vaginal walls, in order to extract only the cervix region, which is the region of interest (ROI) in colposcopy. The performance of cervix segmentation significantly affects the accuracy of diagnosis when analyzing colposcopy and is an essential step in training deep learning-based models.…”

Section: Introductionmentioning

confidence: 99%

Encoder-Weighted W-Net for Unsupervised Segmentation of Cervix Region in Colposcopy Images

Park

Yang

Roh

et al. 2022

Cancers

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Cervical cancer can be prevented and treated better if it is diagnosed early. Colposcopy, a way of clinically looking at the cervix region, is an efficient method for cervical cancer screening and its early detection. The cervix region segmentation significantly affects the performance of computer-aided diagnostics using a colposcopy, particularly cervical intraepithelial neoplasia (CIN) classification. However, there are few studies of cervix segmentation in colposcopy, and no studies of fully unsupervised cervix region detection without image pre- and post-processing. In this study, we propose a deep learning-based unsupervised method to identify cervix regions without pre- and post-processing. A new loss function and a novel scheduling scheme for the baseline W-Net are proposed for fully unsupervised cervix region segmentation in colposcopy. The experimental results showed that the proposed method achieved the best performance in the cervix segmentation with a Dice coefficient of 0.71 with less computational cost. The proposed method produced cervix segmentation masks with more reduction in outliers and can be applied before CIN detection or other diagnoses to improve diagnostic performance. Our results demonstrate that the proposed method not only assists medical specialists in diagnosis in practical situations but also shows the potential of an unsupervised segmentation approach in colposcopy.

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“…C ERVICAL cancer is the fourth most common cause of cancer incidence and mortality among women, with approximately 570,000 confirmed cases and 311,000 deaths worldwide in 2018 [1], [14]. Nevertheless, cervical cancer is preventable, and early diagnosis is essential to improve the survival rate of cervical cancer.…”

Section: Introductionmentioning

confidence: 99%

Exploring Contextual Relationships for Cervical Abnormal Cell Detection

Liang¹,

Feng²,

Liu³

et al. 2022

Preprint

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Cervical abnormal cell detection is a challenging task as the morphological discrepancies between abnormal and normal cells are usually subtle. To determine whether a cervical cell is normal or abnormal, cytopathologists always take surrounding cells as references to identify its abnormality. To mimic these behaviors, we propose to explore contextual relationships to boost the performance of cervical abnormal cell detection. Specifically, both contextual relationships between cells and cell-to-global images are exploited to enhance features of each region of interest (RoI) proposals. Accordingly, two modules, dubbed as RoI-relationship attention module (RRAM) and global RoI attention module (GRAM), are developed and their combination strategies are also investigated. We establish a strong baseline by using Double-Head Faster R-CNN with feature pyramid network (FPN) and integrate our RRAM and GRAM into it to validate the effectiveness of the proposed modules. Experiments conducted on a large cervical cell detection dataset reveal that the introduction of RRAM and GRAM both achieves better average precision (AP) than the baseline methods. Moreover, when cascading RRAM and GRAM, our method outperforms the state-of-the-art (SOTA) methods. Furthermore, we also show the proposed feature enhancing scheme can facilitate both image-level and smear-level classification. The code and trained models are publicly available at https://github.com/ CVIU-CSU/CR4CACD.

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Cervical Cancer Diagnostics Healthcare System Using Hybrid Object Detection Adversarial Networks

Cited by 54 publications

References 33 publications

Cervical Lesion Classification Method Based on Cross-Validation Decision Fusion Method of Vision Transformer and DenseNet

Cervical Lesion Classification Method Based on Cross-Validation Decision Fusion Method of Vision Transformer and DenseNet

Encoder-Weighted W-Net for Unsupervised Segmentation of Cervix Region in Colposcopy Images

Exploring Contextual Relationships for Cervical Abnormal Cell Detection

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