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All Journal Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Teknik Informatika (JUTIF) Journal of Community Development Sevanam: Jurnal Pengabdian Masyarakat
Putu Desiana Wulaning Ayu
Jurusan Teknik Elektro, Universitas Udayana
Religion Humanities Computer Science & IT Education Engineering Nursing Public Health Social Sciences

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Journal : Jurnal Teknik Informatika (JUTIF)

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Comparative Analysis of Augmentation and Filtering Methods in VGG19 and DenseNet121 for Breast Cancer Classification Seneng, I Kadek; Ayu, Putu Desiana Wulaning; Huizen, Roy Rudolf
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4397

Abstract

Breast cancer is one of the most prevalent malignancies and a leading cause of mortality among women worldwide. Mammography plays a crucial role in early detection, yet challenges in manual interpretation have led to the adoption of Convolutional Neural Networks (CNNs) to improve classification accuracy. This study evaluates the performance of Visual Geometry Group (VGG19) and Densely Connected Convolutional Networks (DenseNet121) in mammogram classification. It examines the impact of data augmentation and image enhancement techniques, including Contrast-Limited Adaptive Histogram Equalization (CLAHE), Median Filtering, and Discrete Wavelet Transform (DWT), as well as the influence of varying epochs and learning rates. A novel approach is introduced by assessing data augmentation effectiveness and exploring model adaptations, such as layer incorporation and freezing during training. Classification performance is enhanced through fine-tuning strategies combined with image enhancement techniques, reducing reliance on data augmentation. These findings contribute to medical imaging and computer science by demonstrating how CNN modifications and enhancement methods improve mammogram classification, providing insights for developing robust deep learning-based diagnostic models. The highest performance was achieved using VGG19 with DWT, a learning rate of 0.0001, and 20 epochs, yielding 98.04% accuracy, 98.11% precision, 98% recall, and a 97.99% F1-score. Data augmentation did not consistently enhance results, particularly in clean datasets. Increasing epochs from 10 to 20 improved accuracy, but performance declined at 30 epochs. The confusion matrix showed high accuracy for Benign (100%) and Cancer (99.5%), with more misclassifications in the Normal class (94.5%).
Co-Authors Dandy Pramana Hostiadi Gede Angga Pradipta, Gede Angga Komang Yuli Santika Made Liandana, Made Mahendra, Tubagus Pande Putu Gede Putra Pertama Purnama, I Gusti Agung Vony Roy Rudolf Huizen Seneng, I Kadek