Lakshmi, S.Vijayasenan, D.Sumam David, S.Sreeram, S.Suresh, P.K.2026-02-062019IEEE Region 10 Annual International Conference, Proceedings/TENCON, 2019, Vol.2019-October, , p. 2310-231421593442https://doi.org/10.1109/TENCON.2019.8929640https://idr.nitk.ac.in/handle/123456789/30969Ki-67 labeling index is a widely used biomarker for the diagnosis and monitoring of cancer. Many automated techniques have been proposed for evaluating Ki-67 index. In this paper, we introduce an integrated deep learning based approach. We use MobileUnet model for segmentation and classification and connected component based algorithm for the estimation of Ki-67 index in bladder cancer cases. The average F1 score is 0.92 and dice score is 0.96. The mean absolute error in the evaluated Ki-67 index is 2.1. We also explore possible pre-processing steps to generalize the segmentation model to at least one another type of cancer. Histogram matching and re-sizing improve the performance in breast cancer data by 12% in F1 score and 8% in dice score. © 2019 IEEE.Carcinoma bladderDeep Neural NetworkKi-67 index