Cervix Type and Cervical Cancer Classification System Using Deep Learning Techniques

Habtemariam, Lidiya Wubshet; Zewde, Elbetel Taye; Simegn, Gizeaddis Lamesgin

doi:10.2147/mder.s366303

Cited by 39 publications

(28 citation statements)

References 23 publications

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“…AI has been studied in cervical disease by many scientists 18,35–37 . The AI system can be used as a decision support tool in the diagnosis of cervical cancer, especially in low resources settings, where the expertise and the means are limited 38 . The AI system can also be used as a prognosis predictor.…”

Section: Discussionmentioning

confidence: 99%

“… 18 , 35 , 36 , 37 The AI system can be used as a decision support tool in the diagnosis of cervical cancer, especially in low resources settings, where the expertise and the means are limited. 38 The AI system can also be used as a prognosis predictor. The AI prognostic prediction support system was developed by a Japanese scientist.…”

Section: Discussionmentioning

confidence: 99%

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Application of EfficientNet‐B0 and GRU‐based deep learning on classifying the colposcopy diagnosis of precancerous cervical lesions

Chen

et al. 2023

Cancer Medicine

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Background: Colposcopy is indispensable for the diagnosis of cervical lesions.However, its diagnosis accuracy for high-grade squamous intraepithelial lesion (HSIL) is at about 50%, and the accuracy is largely dependent on the skill and experience of colposcopists. The advancement in computational power made it possible for the application of artificial intelligence (AI) to clinical problems. Here, we explored the feasibility and accuracy of the application of AI on precancerous and cancerous cervical colposcopic image recognition and classification. Methods:The images were collected from 6002 colposcopy examinations of normal control, low-grade squamous intraepithelial lesion (LSIL), and HSIL. For each patient, the original, Schiller test, and acetic-acid images were all collected.We built a new neural network classification model based on the hybrid algorithm. EfficientNet-b0 was used as the backbone network for the image feature extraction, and GRU(Gate Recurrent Unit)was applied for feature fusion of the three modes examinations (original, acetic acid, and Schiller test). Results:The connected network classifier achieved an accuracy of 90.61% in distinguishing HSIL from normal and LSIL. Furthermore, the model was applied to "Trichotomy", which reached an accuracy of 91.18% in distinguishing the HSIL, LSIL and normal control at the same time. Conclusion:Our results revealed that as shown by the high accuracy of AI in the classification of colposcopic images, AI exhibited great potential to be an effective tool for the accurate diagnosis of cervical disease and for early therapeutic intervention in cervical precancer.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Application of EfficientNet‐B0 and GRU‐based deep learning on classifying the colposcopy diagnosis of precancerous cervical lesions

Chen

et al. 2023

Cancer Medicine

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“…Sequential images with a resolution of 416 × 416 successfully extracted from the video were given a bounding box. The process of giving bounding boxes was done using DarkLabel 23 . DarkLabel is a tool for annotating object detection, annotation formats available in DarkLabel are Pascal VOC, YOLO, and Multiple Object Tracking (MOT).…”

Section: Methodsmentioning

confidence: 99%

Annotated Datasets of Oil Palm Fruit Bunch Piles for Ripeness Grading Using Deep Learning

et al. 2023

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The quality of palm oil is strongly influenced by the maturity level of the fruit to be processed into palm oil. Many studies have been carried out for detecting and classifying the maturity level of oil palm fruit to improve the quality with the use of computer vision. However, most of these studies use datasets in the form of images of oil palm fresh fruit bunches (FFB) with incomplete categorization according to real conditions in palm oil mills. Therefore, this study introduces a new complete dataset obtained directly from palm oil mills in the form of videos and images with different categories in accordance with the real conditions faced by the grading section of the palm oil mill. The video dataset consists of 45 videos with a single category of FFB videos and 56 videos with a collection of FFB with multiple categories for each video. Videos are collected using a smart phone with a size of 1280 × 720 pixels with .mp4 format. In addition, this dataset has also been annotated and labelled based on the maturity level of oil palm fruit with 6 categories, which are unripe, under-ripe, ripe, overripe, empty bunches and abnormal fruit.

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“…The transformation region is extracted from the cervix based on three features such as color, intensity, and orientation by using the saliency map visualization. Habtemariam et al 17 presented two clinically related methods for cervix type and cervical cancer classification using the EfficientNetB0 pre-trained model. The transformation region is detected from the cervix images by employing MobileNetv2-YOLOv3.…”

Section: Related Workmentioning

confidence: 99%

Transfer learning supported accurate assessment of multiclass cervix type images

Natarajan

Lakshmi

2022

Proc Inst Mech Eng H

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Cervical cancer predominately affects women compared to lung, breast and endometrial cancer. Premature stage identification and proper treatment of this cancer may lead to 100% survival rate. The cervix type is very prominent in the detailed diagnosis of cervical cancer. High expertise and experienced gynecologist are required for an accurate diagnosis of cervical cancer. To reduce their burden, a model is proposed, based on deep learning that automatically classifies the cervix types. This paper presents Modified Deep Convolutional Neural Networks namely Modified VGG16 (MVGG16), Modified VGG19 (MVGG19), Modified ResNet50 (MRN50), Modified InceptionV3 (MIV3), and Modified InceptionResNetV2 (MIRNV2) for the classification of cervix type images. These modified networks are implemented using a Multiclass Support Vector Machine classifier. The performance metrics are tabulated and compared with pre-trained models. The simulation results show that MIRNV2 achieves the best performance compared to other models with an overall Accuracy of 92.91% and a Kappa score of 0.88. MIRNV2 model also gives better classification accuracy of 96.62% for type 1, 93.58% for type 2, and 95.61% for type 3 cervix images. Hence, this facilitates the application of MIRNV2 as a diagnostic tool to assist the gynecologist in the classification of cervix type images.

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Cervix Type and Cervical Cancer Classification System Using Deep Learning Techniques

Cited by 39 publications

References 23 publications

Application of EfficientNet‐B0 and GRU‐based deep learning on classifying the colposcopy diagnosis of precancerous cervical lesions

Application of EfficientNet‐B0 and GRU‐based deep learning on classifying the colposcopy diagnosis of precancerous cervical lesions

Annotated Datasets of Oil Palm Fruit Bunch Piles for Ripeness Grading Using Deep Learning

Transfer learning supported accurate assessment of multiclass cervix type images

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