TY - GEN
T1 - Deep Feature Extraction of Pap Smear Images Based on Convolutional Neural Network and Vision Transformer for Cervical Cancer Classification
AU - Sholik, Mohammad
AU - Fatichah, Chastine
AU - Amaliah, Bilqis
N1 - Publisher Copyright:
©2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Cervical cancer is a malignant disease that women commonly experience. This cancer can be prevented if screening is carried out early using the pap smear method. The pap smear technique yields a subjective diagnosis. An appropriate decision-making method is needed to overcome this obstacle, such as using a computer-based diagnosis method and applying machine learning. We apply a combination of deep feature extraction using transfer learning from convolutional neural network models and vision transformers to obtain local and global features. Local and global features can represent an image's more comprehensive variety of features. The combined features are then reduced using two steps, principal component analysis and linear discriminant analysis, to obtain a representation of the essential features of the data. The reduced features are then analyzed using several classifiers, including SVM, K-NN, MLP, and LR. The proposed framework was evaluated on three publicly accessible datasets, namely Herlev, Mendeley LBC, and SIPaKMeD, achieving classification accuracies of 97.83% (SVM, K-NN, MLP, and LR), 100% (SVM, K- NN, MLP, and LR), and 98.52% (SVM, K-NN, and LR) respectively.
AB - Cervical cancer is a malignant disease that women commonly experience. This cancer can be prevented if screening is carried out early using the pap smear method. The pap smear technique yields a subjective diagnosis. An appropriate decision-making method is needed to overcome this obstacle, such as using a computer-based diagnosis method and applying machine learning. We apply a combination of deep feature extraction using transfer learning from convolutional neural network models and vision transformers to obtain local and global features. Local and global features can represent an image's more comprehensive variety of features. The combined features are then reduced using two steps, principal component analysis and linear discriminant analysis, to obtain a representation of the essential features of the data. The reduced features are then analyzed using several classifiers, including SVM, K-NN, MLP, and LR. The proposed framework was evaluated on three publicly accessible datasets, namely Herlev, Mendeley LBC, and SIPaKMeD, achieving classification accuracies of 97.83% (SVM, K-NN, MLP, and LR), 100% (SVM, K- NN, MLP, and LR), and 98.52% (SVM, K-NN, and LR) respectively.
KW - Cervical Cancer
KW - Classification
KW - Deep Feature Extraction
KW - Feature Reduction
UR - http://www.scopus.com/inward/record.url?scp=85202297829&partnerID=8YFLogxK
U2 - 10.1109/IAICT62357.2024.10617492
DO - 10.1109/IAICT62357.2024.10617492
M3 - Conference contribution
AN - SCOPUS:85202297829
T3 - Proceedings of the 2024 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2024
SP - 290
EP - 296
BT - Proceedings of the 2024 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2024
Y2 - 4 July 2024 through 6 July 2024
ER -