*7.1. Performance Metrics*

Recall (*R*), Precision (*P*), F1-score (*F1*), specificity (*S*), and accuracy were used as the performance criteria to examine deep-learning performance.

• Precision: This metric represented the fraction of genuine positives among the expected positives. As a result, true-positive (*TP*) and false-positive (*FP*) values were important.

$$P = TP / (TP + FP) \tag{3}$$

• Recall: The ratio of true positives accurately categorized by the model was the recall. The recall was calculated using *TP* and *FN* values.

$$R = TP/(TP + FN) \tag{4}$$

• Specificity: This was defined as the proportion of true negatives (those not caused by illness) correctly classified by the model. The *TN* and *FP* values were used to calculate specificity.

$$S = TN/(TN + FP) \tag{5}$$

• F1-Score: The F1-score measured the model's accuracy by combining precision and recall. Doubling the ratio of the total accuracy and recall values defined the F1-scores.

$$F1 = 2 \times (P \times R) / (P + R) \tag{6}$$

• Performance (Speed): This was an important performance metric in image detection and data classification and clustering, particularly when dealing with large datasets and real-time applications. It measured the time required to process and analyze the data and produce the desired output. In image detection, speed is important for applications such as autonomous vehicles, surveillance systems, and medical imaging, where the detection and analysis of images must be performed in real-time. The speed metric is usually measured in frames-per-second (FPS), which represents the number of images that can be processed in one second. In data classification and clustering, speed is important for applications such as recommendation systems, fraud detection, and customer segmentation, where large amounts of data must be analyzed and classified in a timely manner. The speed metric is usually measured in terms of processing time or throughput, which represents the number of data points that can be processed per unit of time.
