# Object Detection for Brain Cancer Detection and Localization
## 3 Experimental Analysis
### The Results
To compute mAP, the AP values obtained for all classes are further averaged, resulting in a single metric that summarizes the overall performance of the object detection model across multiple object classes. This aggregation allows for a comprehensive evaluation of the model's accuracy and effectiveness in detecting objects across various categories. Figure 3 shows, in the [EMAIL_ADDRESS] and the [EMAIL_ADDRESS]:0.95 plots, respectively, the mAP value for IOU = 50 and IOU ranging from 50 to 95 (i.e., this value represents different IoU thresholds from 0.5 to 0.95, with a step size equal to 0.05 on average mAP). Table 2 shows the values obtained for Precision, Recall, mAP_0.5, and mAP_0.5:0.95 metrics. From Table 2, we can note that the precision and the recall metrics obtain interesting values in both the training and testing steps: as a matter of fact, the precision is equal to 0.948 in training and 0.943 in testing, while the recall is equal to 0.926 in training and 0.932 in testing. Furthermore, the mAP_0.5 obtains good performances, equal to 0.935 and 0.941, respectively, in the training and in the testing step. To thoroughly analyze the performance achieved in terms of precision and recall, let us delve into the details of the results; Figure 4 displays the precision and recall values plotted on the precision-recall graph. By examining the precision and recall values obtained, we can assess the model's performance in terms of both accuracy and completeness. A high-precision value indicates a low false positive rate, while a high recall value indicates a low false negative rate. Striking a balance between precision and recall is crucial for achieving an effective object detection model. The trend of the precision-recall plot is generally expected to be monotonically decreasing. This is because there is typically a trade-off between precision and recall in object detection tasks. Increasing one metric often leads to a decrease in the other. The trade-off between precision and recall arises from the nature of the detection problem. Setting a higher threshold for classification as a positive prediction may increase precision but decrease recall, as only highly confident predictions are accepted. Conversely, lowering the threshold to include more predictions can increase recall but potentially decrease precision by including more false positives. However, it is important to note that there can be exceptions and variations in the precision-recall graph. In certain cases, due to specific circumstances or insufficient data, the graph may not exhibit a strictly monotonically decreasing trend. These exceptions can arise from factors such as class imbalance, outliers, or unique characteristics of the dataset. Therefore, while the monotonically decreasing trend is typical, it is essential to carefully analyze the specific characteristics of the precision-recall graph in each scenario. From the plot in Figure 4, we can see that this plot exhibits a decreasing \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Step** & **Image** & **Labels** & **Precision** & **Recall** & **Specificity** & **mAP\_0.5** & **mAP\_0.5:0.95** \\ \hline Training & 60 & 59 & 0.948 & 0.926 & 0.931 & 0.935 & 0.388 \\ Testing & 60 & 59 & 0.943 & 0.932 & 0.938 & 0.941 & 0.421 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification results. trend. The precision-recall plot also shows the area under the curve (AUC) values related to the brain tumor detection [EMAIL_ADDRESS]. As previously stated, the precision-recall trend is expected to be monotonically decreasing: this behavior is shown by the precision-recall plot related to a value of [EMAIL_ADDRESS] (with an AUC equal to 0.941). Figure 5 presents the normalized confusion matrix of the proposed YOLO model. The confusion matrix is utilized to gain a more detailed understanding of the model's performance across different classes, identifying both the best-performing and worst-performing classes.