Image Processing Reference

In-Depth Information

Table 1

Blood cell variables

#

Variable

Type

Domain

1

Area

Ranged 163:1376

2

Convex area Ranged 187:1781

3

Perimeter

Ranged 58.97056:218.2082

4

Eccentricity

Ranged 0:0.965444268

5

Target

Binary

(1 for benign cell, 0 for distorted cell)

Apart from the NN, the recursive binary C&R tree has been applied to the term of regression

because all variables contain numeric values, as shown in
Table 1
. In this table, all variables

range in type, and only the output (target) variable has binary values (e.g., 1 for benign cells

and 0 for distorted cells). The binary tree is divided into two branches based on the Gini index

and recursively trained with a maximum tree depth of five levels, and it stops when it achieves

a parent branch minimum of 2% and a child branch minimum of 1%.

Finally, the performance of each classification model is evaluated using three statistical

measures: classification accuracy, sensitivity, and specificity. These measures are defined as

true positive (TP), true negative (TN), false positive (FP), and false negative (FN). A TP de-

cision occurs when the positive prediction of the classifier coincides with a positive prediction

of the previous segmentation. A TN decision occurs when both the classifier and the segment-

ation suggest the absence of a positive prediction. An FP occurs when the system labels the

benign cell (positive prediction) as a malignant or distorted one. Finally, An FN occurs when

the system labels a negative (malignant) cell as positive. Moreover, the classification accuracy

is defined as the ratio of the number of correctly classified cells to the total number of cells,

and it is equal to the sum of TP and TN divided by the total number of RBCs (
N
), as shown in

(6)

Sensitivity refers to the rate of correctly classified positives, and it is equal to TP divided by

the sum of TP and FN.

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