Accuracy of colposcopy-directed biopsy in detecting early cervical neoplasia

Stübs, Frederik A.; Mehlhorn, Grit; Gaß, Paul; Schulmeyer, Carla E.; Geppert, Carol I.; Adler, Werner; Beckmann, MW; Koch, M

doi:10.1055/s-0038-1671339

Cited by 2 publications

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“…The authors attained 94.28% of PN, 93.15% of RL and 93.28% of AY on the cervical images. Stübs et al (2018) detected cervical cancer at an earlier stage using biopsy testing method. This method was entirely based on the linear featuring method which was tested on the real time clinical dataset cervical images.…”

Section: Fig 1cervical Image With Cancer Regionsmentioning

confidence: 99%

Cervical Cancer Detection Using Gabor Featured Convolutional Neural Networks

D. Baskar

2024

jes

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Cervical cancer is identified as the most killing disease in women patients from past two decades. Even though various medical treatments are available at present, its death rate is not reduced. Hence, various computational and automated methods are proposed to improve the medical treatments as early as possible at an earlier stage. From the past decades, machine and deep learning methods are used for detecting the cervical cancer images. The deep learning algorithms provide superior results for the cervical cancer detection system. Therefore, Gabor Featured Convolutional Neural Networks (GFCNN) is proposed to detect the cancer regions in cervical images. It proposes Cervical Cancer Detection System (CCDS) is significantly analyzed by applying the methodologies on the cervical images which are available in Guanacaste Dataset. The proposed CCDS is constructed with the following modules Gabor transforms, Feature computations, Classification and Segmentation. The Gabor transform transforms the pixel coordinates and then intrinsic variation features are determined. These features are classified by the GFCNN classifier. The GFCNN method is tested on the set of cervical images in Guanacaste Dataset and its performance is analyzed with respect to precision, recall and cancer accuracy rate. The proposed CCDs method achieved 98.42% of PN, 98.54% of RL and 98.45% of AY. The results of GFCNN method for cervical cancer detection and segmentation system are compared with other state-of-the-art methods.

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Section: Fig 1cervical Image With Cancer Regionsmentioning

confidence: 99%

Cervical Cancer Detection Using Gabor Featured Convolutional Neural Networks

D. Baskar

2024

jes

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“…ere are signi cant di erences in the detection rate of lesions among di erent colposcopy physicians. is has aroused people's attention to the insu cient diagnosis of lesions (including missed diagnosis of cervical cancer) and excessive diagnosis of lesions [6][7][8][9]. Excessive diagnosis of lesions may lead to the excessive treatment of low-grade cervical lesions, increasing the risk of infection and economic burden [10].…”

Section: Introductionmentioning

confidence: 99%

An Improved Image Classification Method for Cervical Precancerous Lesions Based on ShuffleNet

Fang

Yang

Wang

et al. 2022

Computational Intelligence and Neuroscience

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With the rapid development of deep learning, automatic lesion detection is used widely in clinical screening. To solve the problem that existing deep learning-based cervical precancerous lesion detection algorithms cannot meet high classification accuracy and fast running speed at the same time, a ShuffleNet-based cervical precancerous lesion classification method is proposed. By adding channel attention to the ShuffleNet, the network performance is improved. In this study, the image dataset is classified into five categories: normal, cervical cancer, LSIL (CIN1), HSIL (CIN2/CIN3), and cervical neoplasm. The colposcopy images are expanded to solve the problems of the lack of colposcopy images and the uneven distribution of images from each category. For the test dataset, the accuracy of the proposed CNN models is 81.23% and 81.38%. Our classifier achieved an AUC score of 0.99. The experimental results show that the colposcopy image classification network based on artificial intelligence has good performance in classification accuracy and model size, and it has high clinical applicability.

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Accuracy of colposcopy-directed biopsy in detecting early cervical neoplasia

Cited by 2 publications

References 0 publications

Cervical Cancer Detection Using Gabor Featured Convolutional Neural Networks

Cervical Cancer Detection Using Gabor Featured Convolutional Neural Networks

An Improved Image Classification Method for Cervical Precancerous Lesions Based on ShuffleNet

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