# An Efficient Explainability of Deep Models on Medical Images
## 4 Results
### Development Environment
#### 4.1.2 Validation Measurements  
Validation metrics are used to assess the performance of a machine learning model and measure its ability to make accurate predictions. Commonly used measures include: Accuracy, Precision, Recall, f1-score and Loss.  
Accuracy (or overall precision): is a measure that evaluates the overall performance of a classification model by giving the fraction of the total number of samples that have been correctly classified by the classifier. It is calculated by dividing the sum of true positives (TP) and true negatives (TN) by the sum of true positives, true negatives, false positives (FP) and false negatives (FN).  
\[Accuracy=\frac{T_{p}+T_{n}}{T_{p}+T_{n}+F_{p}+F_{p}} \tag{5}\]  
Precision: measures the model's ability to correctly identify positive examples among all examples classified as positive. It is defined as the number of true positives (\(Tp\)) over the number of true positives plus the number of false positives (\(Fp\)).  
\[Precision=\frac{T_{p}}{T_{p}+F_{p}} \tag{6}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Tools** & **Description** \\ \hline _Google Colab Pro_ & Is a cloud-based online development and collaboration platform that provides a working environment for running Jupyter notebooks \\ _Python_ & Is a versatile, user-friendly interpreted programming language, widely used in software development and machine learning. \\ _TensorFlow_ & Is an open source machine learning library developed by Google \\ _Keras_ & Is an open source deep learning library written in Python. \\ _OpenCV (Open Source Computer Vision)_ & Is an open-source library used for image processing and computer vision. \\ _Tf\_explain_ & Is an open source library based on TensorFlow, which provides explainability tools and methods for machine learning models. \\ _Lime (Local Interpretable Model-Agnostic Explanations & Is a Python tool for interpreting machine learning models. It generates local, comprehensible explanations for individual predictions, regardless of the complexity of the model used, making the results of machine learning models more transparent and accessible. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Development Tools.  
Recall: measures the model's ability to correctly identify positive examples among all truly positive examples. It is defined as the number of true positives (\(Tp\)) over the number of true positives plus the number of false negatives (\(Fn\)).  
\[Recall=\frac{T_{p}}{T_{p}+F_{n}} \tag{7}\]  
F1_score: can be interpreted as a weighted average of precision and recall, where an F1 score reaches its best value at 1 and its worst score at 0. The relative contribution of precision and recall to the F1_score is equal. The formula for the F1_score is as follows:  
\[F1\_score=2\frac{P\times R}{P+R} \tag{8}\]  
Loss: also known as error, measures the amount of error between model predictions and actual values. It is often used as a measure of model performance during training. The aim is to minimize the loss, which means that the model makes more accurate predictions.  
Mean Opinion Score (MOS): is a measure widely used in communication and information processing systems to evaluate the subjective quality perceived by users. It is a rating scale ranging from 1 (very poor quality) to 5 (exceptional quality), where users are asked to rate a specific experience. The SMO is calculated by taking the average of all the scores awarded, providing a valuable indication of the overall perceived quality of a system or service.