1. A method for classifying defects comprising: identifying, using a processor, a primary scanning mode; performing, using an image data acquisition subsystem, a first hot scan using the identified primary scanning mode; selecting, using the processor, a plurality of defects of interest and nuisance data in the first hot scan; performing, using the image data acquisition subsystem, one or more additional scans using one or more secondary scanning modes; collecting, using the processor, one or more image sets, each image set comprising a primary scanning mode image and one or more secondary scanning mode images corresponding to a selected defect of interest or nuisance data; dividing, using the processor, each collected image set into a first image subset and a second image subset; training, using the processor, for each pair of primary scanning mode and secondary scanning mode, a convolutional neural network (CNN) with the corresponding first image subset; determining an ideal secondary scanning mode, using the processor, by applying each CNN to the corresponding second image subset; performing, using the image data acquisition subsystem, a final hot scan using the ideal secondary scanning mode; and classifying, using the processor, defects from the final hot scan by using the CNN corresponding to the ideal secondary scanning mode to filter out nuisance data in the final hot scan.