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, 2026)
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.