ENSEMBLE LEARNING MENGGUNAKAN WEIGHTED VOTING DENGAN PEMBELAJARAN PYRAMIDNET PADA HASIL INCEPTIONV3, MOBILENETV2, DAN VISION TRANSFORMER DALAM KLASIFIKASI CITRA KANKER PAYUDARA

MORTARA, ALDA AMALIA and Desiani, Anita and Hanum, Herlina (2025) ENSEMBLE LEARNING MENGGUNAKAN WEIGHTED VOTING DENGAN PEMBELAJARAN PYRAMIDNET PADA HASIL INCEPTIONV3, MOBILENETV2, DAN VISION TRANSFORMER DALAM KLASIFIKASI CITRA KANKER PAYUDARA. Undergraduate thesis, Sriwijaya University.

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Abstract

Breast cancer is one of the leading causes of death in women. Early detection can be done by classifying ultrasound images (USG) of breast cancer into 3 levels of severity namely normal, benign, and malignant using Deep Learning (DL). DL methods include Convolutional Neural Network (CNN) and transformers. CNN architecture development includes InceptionV3 and MobileNetV2. InceptionV3 recognizes image patterns with various scales using factorized convolutions, but requires large memory and long computation time. MobileNetV2 is more efficient in memory and computation time. However, the bottleneck layer may cause the loss of some important details. CNN uses convolution operations with kernel and stride sizes, so it is limited in capturing global relationships. The transformer method used is Vision Transformer (ViT). ViT captures global relationships by dividing the image into small patches, which are then processed in parallel using self-attention. However, ViT gives higher weight to frequently occurring patterns, making it less optimal for unbalanced data. The weakness of a single architecture can be overcome with ensemble learning. This study applies ensemble learning using weighted voting through PyramidNet. PyramidNet helps the model learn the weights on each data and avoid excessive training. The average ensemble learning performance results obtained 93% accuracy indicating almost completely correct class predictions. Sensitivity 93% shows the model is excellent at classifying data of a certain class. Specificity 93% shows the model is excellent at classifying data that is not a certain class. F1-score of 93% shows the model is balanced in distinguishing each class. Cohen's Kappa of 89% indicates high agreement with the true class. This method improved the performance over a single classifier, with accuracy increasing by14%, sensitivity of 16.66%, F1-score of 17.33%, and Cohen's Kappa of 24.33%. The class performance exceeded 90% across all evaluation metrics. However, the Cohen’s Kappa for each class remained below 90%. The model best predicted the normal class, followed by the malignant, and benign classes. The results prove that the proposed ensemble learning method is effective in the classification of breast cancer ultrasound images.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	InceptionV3, MobileNetV2, ViT, Ensemble Learning, Weighted Voting, Mathematics
Subjects:	Q Science > QA Mathematics > QA1-939 Mathematics > QA1.T553 Mathematics--Periodicals. Computer science--Periodicals. Computer science. Q Science > QA Mathematics > QA8.9-QA10.3 Computer science. Artificial intelligence. Computational complexity. Data structures (Computer scienc. Mathematical Logic and Formal Languages
Divisions:	08-Faculty of Mathematics and Natural Science > 44201-Mathematics (S1)
Depositing User:	Alda Amalia Mortara
Date Deposited:	23 Mar 2025 23:05
Last Modified:	23 Mar 2025 23:05
URI:	http://repository.unsri.ac.id/id/eprint/169919

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