The Utilization of Padding Scheme on Convolutional Neural Network for Cervical Cell Images Classification

Haryanto, Toto; Sitanggang, Imas Sukaesih; Agmalaro, Muhammad Asyhar; Rulaningtyas, Riries

doi:10.1109/cenim51130.2020.9297895

Cited by 17 publications

(8 citation statements)

References 7 publications

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“…Win et al [ 66 ] develop a computer-assisted screening system for cervical cancer using digital image processing of Pap smear images; Haryanto et al [ 21 ] aims to create the classification model of Cervical Cell Images using the Convolutional Neural Network (CNN) algorithm; Talo et al [ 53 ] used a deep learning approach to classify cervical cell images obtained from pap smear slides. Mamunur et al [ 38 ] proposed DeepCervix, a hybrid deep feature fusion (HDFF) technique based on DL to classify the cervical cells accurately; Dounias et al [ 12 ] compared the performance of various intelligent methodologies in the task of pap-smear diagnosis; Marinakis et al [ 39 ] proposed an effective genetic algorithm scheme which is combined with a number of nearest neighbor based classifiers.…”

Section: Discussionmentioning

confidence: 99%

Improving cervical cancer classification with imbalanced datasets combining taming transformers with T2T-ViT

Zhao

Shuai

et al. 2022

Multimed Tools Appl

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Cervical cell classification has important clinical significance in cervical cancer screening at early stages. However, there are fewer public cervical cancer smear cell datasets, the weights of each classes’ samples are unbalanced, the image quality is uneven, and the classification research results based on CNN tend to overfit. To solve the above problems, we propose a cervical cell image generation model based on taming transformers (CCG-taming transformers) to provide high-quality cervical cancer datasets with sufficient samples and balanced weights, we improve the encoder structure by introducing SE-block and MultiRes-block to improve the ability to extract information from cervical cancer cells images; we introduce Layer Normlization to standardize the data, which is convenient for the subsequent non-linear processing of the data by the ReLU activation function in feed forward; we also introduce SMOTE-Tomek Links to balance the source data set and the number of samples and weights of the images we use Tokens-to-Token Vision Transformers (T2T-ViT) combing transfer learning to classify the cervical cancer smear cell image dataset to improve the classification performance. Classification experiments using the model proposed in this paper are performed on three public cervical cancer datasets, the classification accuracy in the liquid-based cytology Pap smear dataset (4-class), SIPAKMeD (5-class), and Herlev (7-class) are 98.79%, 99.58%, and 99.88%, respectively. The quality of the images we generated on these three data sets is very close to the source data set, the final averaged inception score (IS), Fréchet inception distance (FID), Recall and Precision are 3.75, 0.71, 0.32 and 0.65 respectively. Our method improves the accuracy of cervical cancer smear cell classification, provides more cervical cell sample images for cervical cancer-related research, and assists gynecologists to judge and diagnose different types of cervical cancer cells and analyze cervical cancer cells at different stages, which are difficult to distinguish. This paper applies the transformer to the generation and recognition of cervical cancer cell images for the first time. Supplementary Information The online version contains supplementary material available at 10.1007/s11042-022-12670-0.

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Section: Discussionmentioning

confidence: 99%

Improving cervical cancer classification with imbalanced datasets combining taming transformers with T2T-ViT

Zhao

Shuai

et al. 2022

Multimed Tools Appl

View full text Add to dashboard Cite

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“…Graph convolutional network 5 7 [21] 2021 SIPaKMeD Deep learning -ResNet-152 5 [22] 2021 SIPaKMeD Deep learning-Compact VGG 5 [23] 2021 SIPaKMeD Ensemble of CNN Models 2 [24] 2020 SIPaKMeD AlexNet 5…”

Section: Table 1 Summary Of Recent Work Done On Pap Smear Cytology Im...mentioning

confidence: 99%

Cervical cancer diagnosis based on cytology pap smear image classification using fractional coefficient and machine learning classifiers

Kalbhor¹,

Shinde²,

Jude

2022

TELKOMNIKA

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Doctors and pathologists have long been concerned about determining the malignancy from cell images. This task is laborious, time-consuming and needs expertise. Due to this reason, automated systems assist pathologists in providing a second opinion to arrive at accurate decision based on cytology images. The classification of cytology images has always been a difficult challenge among the various image analysis approaches due to its extreme intricacy. The thrust for early diagnosis of cervical cancer has always fuelled the research in medical image analysis for cancer detection. In this paper, an investigative study for the classification of cytology images is proposed. The proposed study uses the discrete coefficient transform (DCT) coefficient and Haar transform coefficients as features. These features are given as a input to seven different machine learning algorithms for normal and abnormal pap smear images classification. In order to optimize the feature size, fractional coefficients are used to form the five different sizes of feature vectors. In the proposed work, DCT transform has given the highest classification accuracy of 81.11%. Comparing the different machine learning algorithms the overall best performance is given by the random forest classifier.

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“…Haryanto et al [15] focus on creating the classifier method of Cervical Cell Images with the convolutional neural network (CNN) method. The dataset utilized is the image datasets SIPaKMeD.…”

Section: Related Workmentioning

confidence: 99%

Optimal Deep Learning Based Inception Model for Cervical Cancer Diagnosis

Abukhalil¹,

Alqaralleh²,

Alomari³

2022

Computers, Materials &Amp; Continua

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Prevention of cervical cancer becomes essential and is carried out by the use of Pap smear images. Pap smear test analysis is laborious and tiresome work performed visually using a cytopathologist. Therefore, automated cervical cancer diagnosis using automated methods are necessary. This paper designs an optimal deep learning based Inception model for cervical cancer diagnosis (ODLIM-CCD) using pap smear images. The proposed ODLIM-CCD technique incorporates median filtering (MF) based pre-processing to discard the noise and Otsu model based segmentation process. Besides, deep convolutional neural network (DCNN) based Inception with Residual Network (ResNet) v2 model is utilized for deriving the feature vectors. Moreover, swallow swarm optimization (SSO) based hyperparameter tuning process is carried out for the optimal selection of hyperparameters. Finally, recurrent neural network (RNN) based classification process is done to determine the presence of cervical cancer or not. In order to showcase the improved diagnostic performance of the ODLIM-CCD technique, a series of simulations occur on benchmark test images and the outcomes highlighted the improved performance over the recent approaches with a superior accuracy of 0.9661.

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The Utilization of Padding Scheme on Convolutional Neural Network for Cervical Cell Images Classification

Cited by 17 publications

References 7 publications

Improving cervical cancer classification with imbalanced datasets combining taming transformers with T2T-ViT

Improving cervical cancer classification with imbalanced datasets combining taming transformers with T2T-ViT

Cervical cancer diagnosis based on cytology pap smear image classification using fractional coefficient and machine learning classifiers

Optimal Deep Learning Based Inception Model for Cervical Cancer Diagnosis

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