  Due to cellular heterogeneity, cell nuclei classification, segmentation, and
detection from pathological images are challenging tasks. In the last few
years, Deep Convolutional Neural Networks (DCNN) approaches have been shown
state-of-the-art (SOTA) performance on histopathological imaging in different
studies. In this work, we have proposed different advanced DCNN models and
evaluated for nuclei classification, segmentation, and detection. First, the
Densely Connected Recurrent Convolutional Network (DCRN) model is used for
nuclei classification. Second, Recurrent Residual U-Net (R2U-Net) is applied
for nuclei segmentation. Third, the R2U-Net regression model which is named
UD-Net is used for nuclei detection from pathological images. The experiments
are conducted with different datasets including Routine Colon Cancer(RCC)
classification and detection dataset, and Nuclei Segmentation Challenge 2018
dataset. The experimental results show that the proposed DCNN models provide
superior performance compared to the existing approaches for nuclei
classification, segmentation, and detection tasks. The results are evaluated
with different performance metrics including precision, recall, Dice
Coefficient (DC), Means Squared Errors (MSE), F1-score, and overall accuracy.
We have achieved around 3.4% and 4.5% better F-1 score for nuclei
classification and detection tasks compared to recently published DCNN based
method. In addition, R2U-Net shows around 92.15% testing accuracy in term of
DC. These improved methods will help for pathological practices for better
quantitative analysis of nuclei in Whole Slide Images(WSI) which ultimately
will help for better understanding of different types of cancer in clinical
workflow.
