Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control
Dzaky Hanif Arjuna
Institut Teknologi Sepuluh Nopember
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Automated Breast Cancer Cell Counting: Comparing Multi-class Segmentation and Two-stage Classification Strategies Dzaky Hanif Arjuna; Edy Kurniawan; Reza Fuad Rachmadi; I Ketut Eddy Purnama
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 3, August 2026
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i3.2639

Abstract

The manual interpretation of Hematoxylin and Eosin (H&E) histopathology images for breast cancer diagnosis is hindered by time constraints and observer bias. This research aims to develop an automated system using deep learning for cell detection and classification by evaluating two key approaches: Multi-class Segmentation (single-stage) and Segmentation followed by Classification (two-stage). The U-Net architecture was employed for segmentation, while MobileNetV2 and VGG16 were used for classification. The models were evaluated on the public IHC4BC dataset and primary data from Airlangga University Hospital (RSUA). The study also evaluated the impact of Resizing and Tiling data processing strategies. Experimental results showed that although the MobileNetV2 and VGG16 classification models achieved a high testing accuracy of 98.80%, the integrated two-stage system exhibited a high counting error, with a Mean Absolute Error (MAE) of 119.87 for positive cells, primarily due to under-segmentation of overlapping cells. In contrast, the Multi-class Segmentation approach utilizing the Tiling strategy demonstrated superior performance. This model effectively preserved spatial resolution while distinguishing cell types simultaneously, achieving the lowest MAE of 18.46 for positive cells and 1.66 for negative cells. This study concludes that Multi-class Segmentation with the Tiling strategy is the most effective and accurate approach for automated cell counting in histopathology images.
Co-Authors Edy Kurniawan I Ketut Eddy Purnama Reza Fuad Rachmadi