Automatic Classification of Cervical Cells Using Deep Learning Method

Yu, Suxiang; Feng, Xinxing; Wang, Bin; Dun, Hua; Zhang, Shuai; Zhang, Ruihong; Huang, Xin

doi:10.1109/access.2021.3060447

Cited by 37 publications

(14 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Here, the terms t and u are denoted as the optimal constants obtained from the FCM and region growth, respectively. The two optimal constants of the FCM and region growing are lies in the interval of [1,2], respectively. The center of the image segment and size of the cervical image is correspondingly denoted as e s and A.…”

Section: Objective Model Of Segmentationmentioning

confidence: 99%

“…Cervical cancer is caused by several cervix cells that become vulnerable to the human body. 1 It plays a major role in increasing the death rate, especially in women. 2 Cervical cancer can be diagnosed and treated earlier, which is possible only through testing the body with histological and gynecological examinations against cervical cancer regularly.…”

Section: Introductionmentioning

confidence: 99%

“…Cervical cancer is caused by several cervix cells that become vulnerable to the human body 1 . It plays a major role in increasing the death rate, especially in women 2 .…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive fuzzy‐region growing fusion and improved CNN‐ANFIS‐based automated segmentation and classification of cervical cancer

Jeyshri

Kowsigan

2022

Concurrency and Computation

View full text Add to dashboard Cite

Summary The cervical cancer screening mechanisms help to classify cervical lesions into cancerous, precancerous, and normal. But, some of the classification approaches provide optimal outcomes based on the precise spotting and segmenting of cervical cancer. Moreover, still, challenges persist in the precise spotting of cervical cancer. The conventional screening methods for cervical cancer require more experienced pathologists, but it may also lead to poor efficiency and low accuracy. To overcome these issues, medical image processing introduces both machine learning and deep learning methods into cervical cell classification. The major scope of this task is to plan a new cervical cancer diagnosis method using intelligent approaches. Here, the segmentation of abnormality is done by the novel adaptive fuzzy‐region growing fusion model, which is improved by the hybridized optimization algorithm named gray wolf‐tunicate swarm algorithm (GW‐TSA). Then, the new ensemble classification termed Improved convolutional neural network with artificial neural network fuzzy inference system, which depends on the proposed GW‐TSA for performance enhancement through optimizing the hyperparameters. Finally, the simulation findings show that the designed approach is achievable and provides the highest accuracy rate in diagnosing cervical cancer.

show abstract

Section: Objective Model Of Segmentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive fuzzy‐region growing fusion and improved CNN‐ANFIS‐based automated segmentation and classification of cervical cancer

Jeyshri

Kowsigan

2022

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…The studies described above are summarized in Table 1. DL models have generally been used to develop automatic classification methods [30][31][32]. The deep models have unique benefits and have achieved good results for computer vision problems.…”

Section: Literature Reviewmentioning

confidence: 99%

Multilevel Deep Feature Generation Framework for Automated Detection of Retinal Abnormalities Using OCT Images

Barua

Chan

Doğan

et al. 2021

Entropy

View full text Add to dashboard Cite

Optical coherence tomography (OCT) images coupled with many learning techniques have been developed to diagnose retinal disorders. This work aims to develop a novel framework for extracting deep features from 18 pre-trained convolutional neural networks (CNN) and to attain high performance using OCT images. In this work, we have developed a new framework for automated detection of retinal disorders using transfer learning. This model consists of three phases: deep fused and multilevel feature extraction, using 18 pre-trained networks and tent maximal pooling, feature selection with ReliefF, and classification using the optimized classifier. The novelty of this proposed framework is the feature generation using widely used CNNs and to select the most suitable features for classification. The extracted features using our proposed intelligent feature extractor are fed to iterative ReliefF (IRF) to automatically select the best feature vector. The quadratic support vector machine (QSVM) is utilized as a classifier in this work. We have developed our model using two public OCT image datasets, and they are named database 1 (DB1) and database 2 (DB2). The proposed framework can attain 97.40% and 100% classification accuracies using the two OCT datasets, DB1 and DB2, respectively. These results illustrate the success of our model.

show abstract

“…Hussain et al 11 proposed an ensemble CNN model utilizing fine‐tuned Resnet‐50, Resnet‐101 and Googlenet for getting better performance. Yu et al 12 incorporated a combination of CNN, inception, and spatial pyramid pooling for the effective classifications of pap images. Shi et al 13 implemented a graph convolution network for improving classification performance.…”

Section: Introductionmentioning

confidence: 99%

Enhancing convolutional neural network model with spectral features for the identification of cervical dysplasia

Kutty

Menon

2022

Int J Imaging Syst Tech

View full text Add to dashboard Cite

Cervical cell classification plays a key role in the computer-based screening and diagnosis. This article focuses on cell classification and grading to differentiate the stages of cervical dysplasia. The proposed framework uses a unification of wavelet transform and convolutional neural network (CNN) for segregating spectral and spatial features from papanicolaou stained (pap) smear images. A correlation-based feature selection is adopted to find relevant features from the CNN model. Random Forest classifier is then incorporated

show abstract

Automatic Classification of Cervical Cells Using Deep Learning Method

Cited by 37 publications

References 31 publications

Adaptive fuzzy‐region growing fusion and improved CNN‐ANFIS‐based automated segmentation and classification of cervical cancer

Adaptive fuzzy‐region growing fusion and improved CNN‐ANFIS‐based automated segmentation and classification of cervical cancer

Multilevel Deep Feature Generation Framework for Automated Detection of Retinal Abnormalities Using OCT Images

Enhancing convolutional neural network model with spectral features for the identification of cervical dysplasia

Contact Info

Product

Resources

About