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Vol. 11, No. 3, August 2026 (Article in Progress)

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Automated breast cancer cell counting: comparing multi-class segmentation and two-stage classification strategies

https://doi.org/10.22219/kinetik.v11i3.2639
Dzaky Hanif Arjuna
Institut Teknologi Sepuluh Nopember
Edy Kurniawan
Institut Teknologi Sepuluh Nopember
Reza Fuad Rachmadi
Institut Teknologi Sepuluh Nopember
I Ketut Eddy Purnama
Institut Teknologi Sepuluh Nopember

Corresponding Author(s) : Dzaky Hanif Arjuna

dzakyhanif10@gmail.com

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 11, No. 3, August 2026 (Article in Progress)

Abstract

The manual interpretation of Hematoxylin and Eosin (H&E) histopathology images for breast cancer diagnosis is hindered by time limitations and observer bias. This research seeks to create an automated system using Deep Learning for cell detection and classification, evaluating two key approaches: Multi-class Segmentation (single-stage) and Segmentation followed by Classification (two-stage). U-Net architecture was employed for segmentation, while MobileNetV2 and VGG16 were used for classification. The models were tested on the public IHC4BC dataset and primary data from Airlangga University Hospital (RSUA). The study also evaluated the impact of Resizing versus Tiling data processing strategies. Experimental results showed that while MobileNetV2 and VGG16 classification models achieved a high testing accuracy of 98.80%, the two-stage integrated system revealed 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 and distinguished cell types simultaneously, achieving the lowest positive cell MAE of 18.46 and a negative cell MAE of 1.66. This study concluded that multi-class segmentation with a Tiling strategy was the most effective and accurate approach for automated cell counting in histopathology images.

Keywords

Breast Cancer Cell Classification Deep Learning Hematoxylin and Eosin MobilenetV2 Multi-class Segmentation U-Net
Arjuna, D. H., Kurniawan, E., Rachmadi, R. F. ., & Purnama, I. K. E. (2026). Automated breast cancer cell counting: comparing multi-class segmentation and two-stage classification strategies . Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 11(3). https://doi.org/10.22219/kinetik.v11i3.2639
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