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All Journal Andalasian International Journal of Applied Science, Engineering, and Technology
Hyperastuty, A. Santika
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Civil Engineering, Building, Construction & Architecture Electrical & Electronics Engineering Energy Industrial & Manufacturing Engineering Mechanical Engineering

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A Histopathology Grading of Breast Cancer Using Visual Geometry Group Method Hyperastuty, A. Santika; Setiawan, Fachruddin Ari; Pradana, Dio Alif; Puspitasari, Rahma Ajeng; Inayah, Lailatul; Winarti, Eko
Andalasian International Journal of Applied Science, Engineering and Technology Vol. 5 No. 2 (2025): July 2025
Publisher : LPPM Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/aijaset.v5i02.255

Abstract

Breast cancer continues to rank among the world's leading causes of death for women. Developing successful treatment plans requires a timely and accurate diagnosis. Although histopathological image analysis is still the gold standard for evaluating malignancy, it is prone to inconsistencies and human error. The objective of this research is to use the Visual Geometry Group's (VGG16) deep learning technique to automate the evaluation of breast cancer histology. A collection of breast cancer histopathology images spanning 85 epochs was used to train the VGG16 model, which is well-known for its excellent performance in image classification tasks. For training and testing, the model uses batch sizes of 33 and 64, respectively, and a Stochastic Gradient Descent (SGD) optimizer with a learning rate of 0.01. With an F1 score of 0.98, 89.3% training accuracy, and 98% validation accuracy, the experimental findings show excellent performance. These results indicate that VGG16 is highly effective in distinguishing between different tissue grades of breast cancer. Despite its high performance, challenges remain regarding computational efficiency and interpretability for clinical use. Future research should focus on exploring lightweight architectures, improving model explanations, and validating more diverse and larger datasets to enhance real-world applicability in digital pathology.
Co-Authors Inayah, Lailatul Pradana, Dio Alif Puspitasari, Rahma Ajeng Setiawan, Fachruddin Ari winarti, eko