The utilization of padding scheme on convolutional neural network for cervical cell images classification
T Haryanto, IS Sitanggang… - 2020 International …, 2020 - ieeexplore.ieee.org
2020 International conference on computer engineering, network …, 2020•ieeexplore.ieee.org
Cervical cancer identification through pap-smear images analysis is a challenge for
medicians, especially in distinguishing cells, between the normal and abnormal one. This
study aims to create the classification model of Cervical Cell Images using the Convolutional
Neural Network (CNN) algorithm. The dataset used is the image dataset SIPaKMeD. The
CNN algorithm was implemented using the AlexNet architecture with and non-padding
scheme. Padding is included in the experiments by adding the pixel 0 on the original images …
medicians, especially in distinguishing cells, between the normal and abnormal one. This
study aims to create the classification model of Cervical Cell Images using the Convolutional
Neural Network (CNN) algorithm. The dataset used is the image dataset SIPaKMeD. The
CNN algorithm was implemented using the AlexNet architecture with and non-padding
scheme. Padding is included in the experiments by adding the pixel 0 on the original images …
Cervical cancer identification through pap-smear images analysis is a challenge for medicians, especially in distinguishing cells, between the normal and abnormal one. This study aims to create the classification model of Cervical Cell Images using the Convolutional Neural Network (CNN) algorithm. The dataset used is the image dataset SIPaKMeD. The CNN algorithm was implemented using the AlexNet architecture with and non-padding scheme. Padding is included in the experiments by adding the pixel 0 on the original images to improve the accuracy of the model. The experimental results show that using the utilization padding scheme on the AlexNet architecture can increase the accuracy of the model slightly significantly from 84.88% to 87.32%.
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