Heterogeneous ensemble learning: modified ConvNextTiny for detecting molecular expression of breast cancer on standard biomarkers

Indo Intan^1*Sitti Harlina¹Andrea Stevens Karnyoto²Berti Julian Nelwan³Devin Setiawan⁴Amalia Yamin⁵Ririn Endah Puspitasari³

• ¹Department of Informatics Engineering, Dipa Makassar University, Makassar, Indonesia
• ²Computer Science Department, BINUS Graduate Program-Master of Computer Science, Faculty of Engineering, Binus University, West Jakarta, Jakarta, Indonesia
• ³Department of Anatomical Pathology, Faculty of Medicine, Hasanuddin University, Indonesia, Makassar, Indonesia
• ⁴Department of Electrical Engineering and Computer Science, School of Engineering, University of Kansas, Lawrence, Kansas, United States
• ⁵Laboratory of Anatomical Pathology, Wahidin Sudirohusodo General Hospital, Indonesia, Makassar, Indonesia

Breast cancer is the highest-ranking type of cancer, with 2.3 million new cases diagnosed each year. Immunohistochemistry (IHC) is the gold standard for determining the expression of cancer malignancies in patients with the ultimate goal of determining prognosis and therapy. Immunohistochemistry refers to the four WHO standard biomarkers: estrogen receptor, progesterone receptor, human epidermal growth factor receptor-2, and Ki-67. The indications of the four biomarkers focus on the quantity of cell nuclei and the intensity of brown cell membranes. Our study aims to detect the expression of breast cancer malignancy as an initial step in determining prognosis and therapy. We implemented homogeneous and heterogeneous ensemble learning models. The homogeneous ensemble learning model uses the majority vote technique to select the best performance between the Xception, ResNet50V2, InceptionResNet50V2, and ConvNextTiny models. The heterogeneous ensemble learning model takes the ConvNextTiny model as the best model. Feature engineering in ConvNextTiny combines convolution and cell-quantification features as feature fusion. ConvNextTiny, which applies feature fusion, can detect the expression of cancer malignancy. Heterogeneous ensemble learning outperforms homogeneous ensemble learning. The model performs well for accuracy, precision, recall, F1-score, and ROC-AUC of 0.997, 0.973, 0.991, 0.982, and 0.994, respectively. These results indicate that the model can classify malignancy expressions of breast cancer well. This model still requires the configuration of the visual laboratory device to test the real-time model capabilities.