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  • br resultant image is given in Fig d Next morphological


    resultant image is given in Fig. 6d. Next, morphological operations are employed for the removal of unwanted objects to produce an image provided in Fig. 6e. Similarly, objects which are closer to the nucleus are removed using area-based threshold with α = 30 for the threshold value, i.e., the object whose area is below 30 is removed. The resulted image is given in Fig. 6f.
    Theoretically, a large number of features can be extracted for malignant Echinomycin classification, but in practice, the abundance of features creates the problem of overfitting. In the proposed approach, 20 features are extracted from 400 microscopic images. In order to select optimal features, the CAGA algorithm is used, which helps in obtaining those features which give higher accu-racy in the malignant cell detection and classification. It must be noted that CAGA benefited from the structure made by a chain of agents along with dynamic neighboring genetic, operators for the selection of optimal features.
    Table 1
    Confusion matrix.
    Predicted (malignant) Predicted (benign) Total
    3.3. Data performance measurement of classifier
    The performance evaluation of each classifier is analyzed using the confusion matrix. In the confusion matrix, true-positive (TP) represents those malignant cell cases which are correctly classi-fied as positive (malignant), where false-positive (FP) represents those non-malignant cell cases which are classified as positive (malignant) (also known as Type 1 error). Similarly, true-negative (TN) denotes those non-malignant cell cases which are correctly classified as negative (non-malignant), and false-negative (FN) shows those malignant cell cases which are classified as negative (non-malignant) (also known as Type 2 error).
    For the proposed approach, the performance evaluation through confusion matrix is given in Table 1, where n = 29 000 is the total number of cells in the image.
    From the confusion matrix, various factors such as accu-racy, sensitivity, specificity, precision, false-positive rate, and F-measure, etc. can be determined to measure the performance of a given classifier. Each of these factors is briefly discussed below:
    • Accuracy: The measurement for correct classification. Math-ematically, Devonian is given by:
    Accuracy =
    • Sensitivity: The measurement for True-Positive (TP) such as a person has the tumor. Mathematically, it is Echinomycin given by:
    Sensitivity =
    • Specificity: The measurement for True-Negative (TN) such as a person does not have the tumor. Mathematically, it is given by:
    Specificity =
    • Precision: The ratio of the positive cases that are calculated correctly. Mathematically, it is given by:
    Precision =
    • False Positive (FP)-Rate: The ratio of negative cases that are not correctly classified as positives. Mathematically, it is given by:
    • The Recall or TP-Rate: It is the measure of the correctly clas-sified positive cases, which can be shown by the following equation:
    Recall =
    Some times higher precision is important, while in various situations, higher recall is very important. 
    Fig. 7. Results without CAGA.
    • F-Measure: It is the combination of precision and recall, which can be represented by the following equation:
    Precision + Recall
    3.3.1. Experimental results with and without CAGA
    In the first scenario, experiments are conducted on total 20 features using three different classifiers, i.e., SVM, NB, and RF with k-fold cross validation (k = 10) without using CAGA. According to the bar chart in Fig. 7, the accuracy, sensitivity, specificity, and F-Measure for each of the given classifier, i.e., SVM, NB, and RF, are determined for the evaluation of their performances. It can be observed from Fig. 7, that the average performance results of ac-curacy, sensitivity, specificity, and F-Measure are 93.99%, 96.22%, and 94.10%, achieved for SVM, NB, and RF classifier, respectively.