## 5 Conclusions  
This paper presents a method for the automated detection and classification of overlapping nuclei from CPE images using maximum entropy thresholding, new combinations of geometric and textural features, and double-strategy RF. First, the images were enhanced on their green color channel using histogram stretching and CLAHE. Then, maximum entropy thresholding was employed to segment the cell nuclei from their surrounding background (i.e., cytoplasm, blood cells, artifacts, and so on). The post-processing step was performed to eliminate any false findings and preserve the shape of the segmented nuclei using morphological operations. A new combination of 16 geometrical and 10 textural features was extracted for each extracted nucleus region. A double-strategy RF algorithm was applied to perform two tasks: ensemble feature selection to select the most relevant features,  
Figure 18: The visual results of detected touching, overlapping and clustering nuclei through the proposed method (the red bounding boxes indicate the different forms of detected overlapping nuclei). \begin{table}
\begin{tabular}{l c} \hline \hline
**Algorithm Steps** & **Compuatation Time (Seconds)** \\ \hline Nuclei segmentation using maximum entropy thresholding & 2.07 s \\ Geometric and textural features extraction & 2.02 s \\ Classification using double-strategy RF & 1.07 s \\ Entire Algorithm & 5.17 s \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Computational time of each processing step and the entire algorithm. and an ensemble classifier to identify the presence of overlapped nuclei using selected features. RF ensemble feature selection selected eight features out of a total of 26 features that were used as input to the RF ensemble classifier. The proposed method was evaluated on 4000 nuclei from CPE images with respect to six performance measures and AUC. It yielded 96.6% sensitivity, 98.7% specificity, 92.62% precision, 94.6% F1 score, 98.4% accuracy, 97.6% G mean, and AUC 99.0%. Only 5.17 s of computation time was required to run the entire algorithm. The performance from using RF's selected features was compared to the performance of all features by coupling with five different classifiers: NB, SVM, KNN, DT, and RF. The comparison revealed that RF's selected features were better in terms of generalization capability and yielded significant improvements in accuracy for most classifiers, except NB. It is also worth noting that the RF ensemble classifier provided favorable accuracy compared to other classifiers. The synergy between the proposed features and a double-strategy RF achieved the promising results. Furthermore, the achieved results were compared with the results obtained from previous works. The results prove that the proposed algorithm yields superior results compared to previous works. It is our finding that the combination of geometric and textural features is more effective than the features used in previous studies. Due to its high accuracy and computational simplicity, the proposed algorithm can be used as a new basis in developing algorithms for separating overlapping nuclei, and can also serve as a new supportive tool in developing advanced automated cell analysis systems. Furthermore, the proposed method has the potential to integrate with the existing separation algorithms of overlapping nuclei to enhance separation accuracy by accurately locating the overlapping nuclei to be separated. Separation of overlapping nuclei or extraction of the interregional contour of each nucleus has been deferred for future study. [PERSON], [PERSON], and [PERSON] conceived and designed the study; [PERSON] organized the experiments, performed the simulation and data analysis, and wrote the manuscript; [PERSON] acquired the images, provided the medical reviews, annotated the pathologic cells, and verified the results; and [PERSON] and [PERSON] coordinated the whole effort as supervisors of this research. This research received no external funding.