# The Usefulness of Gradient-Weighted CAM in Assisting Medical Diagnoses
## 4 Discussion  
For these 16 images, the radiologist's diagnoses agreed with the DeepLesion labels 12 times and agreed with the CNN results 12 times, though CNN the results differ from the labels in two cases. In these two cases, the radiologist agreed with the DeepLesion labels once and with the CNN results once. In the case where the radiologist agreed with the CNN result, he found the heat-map to be helpful. In the case where the agreement was with the DeepLesion label, the heat-map was considered less helpful. This observation may imply that presenting the heat-maps in addition to the AI-based diagnoses may help physicians in reducing false diagnoses and may help increase the rate of acceptance for AI-based results in the medical community. In addition, for all 16 images, the helpfulness to the radiologist of the Grad-CAM-generated heat-maps ranged from somewhat helpful to really helpful. In no case was a Grad-CAM heat-map considered not helpful at all. From the information contained in the table above, of the clinician's 16 diagnoses, three of them differ from the DeepLesion labels, i.e., images 6, 11 and 14. In the case of image number 6, where the DeepLesion label indicated no tumor, the clinician diagnosed the opposite, which was in agreement with the CNN result. In this case, the radiologist found that the heat-map was helpful. In the case of image number 11, the clinician diagnosed that what appeared to be tumor(s) is actually cyst(s). The Grad-CAM-generated heat-map for image number 11 was deemed only somewhat helpful. Finally, in the case of image number 14, where the DeepLesion label also indicates that the image does contain a tumor, the clinician diagnosed that there is no tumor, but an inflammation of the biliary tract; in this case, the Grad-CAM heat-map was again deemed somewhat helpful. Based on these three cases, and assuming  
Figure 8: Examples of the classification of CT images containing liver tumors.  
Figure 7: Examples of classification of CT images containing lung tumors.  
that the clinician's diagnoses are correct, it is probable that the Grad-CAM heat-maps can be more helpful in the case of a false negative diagnosis than a false positive diagnosis. Though there appears to be no literature discussing the incorrect labeling of DeepLesion, there are studies indicating that the labels in DeepLesion are incomplete [21].  
There is a single case among the 16 images, image 15, where the CNN classifier misclassified. The image contained no tumor, as DeepLesion labeled and was verified by the clinician, but the classifier generated a false positive. The clinician further clarified that the image did contain cyst(s), but not a tumor, which may be the reason for the false classification. The false prediction may be an indication that the classifier was not trained sufficiently to differentiate between cysts and tumors, as the DeepLesion database did not label cysts. However, the Grad-CAM heat-map was somewhat helpful by pointing out the region(s) where the classifier thought tumors may exist.  
The results of these impromptu experiments appeared to show that the classification accuracies for lung and liver tumors for CNN deep learning network models may be higher than average. This is an interesting result that may be worth pursuing in a separate investigation.