# The Usefulness of Gradient-Weighted CAM in Assisting Medical Diagnoses
## 3 Results  
For this experiment, a binary classification was used to predict whether the CT image contains a tumor or not. The threshold to determine whether the generic CNN was sufficiently trained was set arbitrarily at 95% accuracy for the testing set. So, the CT images in the training set were sampled, resampled, and trained until the 20% testing data set was able to reach a classification accuracy of 95.89%, with only 14 false classifications. The following figure, Figure 5, shows example outputs from the first run, where \"Yes/No\" indicates whether the tumor was detected, and the actual values of \"1/0\" are DeepLesion labels, where \"1\" indicates the image contains tumor\", and \"0\" indicates \"has no tumor\". The following table, Table 2, shows the statistics from the first and second runs. After reaching an accuracy of 95.89%, the training was stopped, and the Grad-CAM method was applied for each of the test images, including those that were falsely classified. The following figure, Figure 6, shows some of the CT images with their associated heat-maps overlaid. In the legends of the figures, \"Actual\" shows the label for DeepLesion, and \"Predicted\" shows the output of the CNN classifier. Figure 5: Example of outputs from the first run. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**First Run:** & \multicolumn{3}{c}{**Second Run:**} \\ \hline Training Sample Error & 5\% & \
\begin{tabular}{l} Training Sample Error \\ Rate \\ \end{tabular} & 5\% \\ Testing Sample Error & 14.586\% &
\begin{tabular}{l} Testing Sample Error \\ Rate \\ \end{tabular} & 8.091\% \\ Accuracy & 85.414\% & Accuracy & 91.909\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification statistics for the first and second runs. Figure 6: CT images with Grad-CAM-generated heat-maps overlaid. A qualified clinical radiologist from the Center of Acute and Critical Imaging in the Chang Gung Memorial Hospital at Linkou, located in Taoyuan, Taiwan, was consulted for this investigation. He was shown the original CT images in the test set and those same images with their associated heat-maps overlaid. Unfortunately, because of time and other constraints, responses for only 16 images of all the images that were presented to the clinician were received. The following table, Table 3, lists the DeepLesion labels plus the diagnoses from the clinician. The first column is simply an arbitrary number assigned to the image; the second column contains the translucent version of the heat-map overlaying the original image. The third and fourth columns show the DeepLesion labels and the CNN classification results on whether the image contains tumor(s). The fifth column contains the trained radiologist's diagnosis based on each image. The last column shows the subjective evaluation of the radiologist of the helpfulness of the Grad-CAM-generated heat-map in reaching the diagnosis. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Image** & **Overlaid Image** & \begin{tabular}{c} **Deep Lesion** \\ **Label** \\ \end{tabular}  & **CNN Result** & \ \begin{tabular}{c} **Clinical Radiologist’s** \\ **Diagnosis** \\ \end{tabular}  & \begin{tabular}{c} **Grad-CAM** \\ **Helpfulness** \\ \end{tabular}