Y Delannoy, G Tournel, C Tonnel, D Turck, V Hedouin, D Gosset
[Clinical forensic valuation of school violence]
Archives de pediatrie : organe officiel de la Societe francaise de pediatrie
skin cancer diagnosis challenges dermatologists due complex visual variations across diagnostic categories convolutional neural networks cnns specifically efficient net b0b7 series shown superiority multiclass skin cancer classification study addresses limitations visual examination presenting tailored preprocessing pipeline designed efficient net models leveraging transfer learning pretrained imagenet weights research aims enhance diagnostic accuracy imbalanced multiclass classification context study develops specialized image preprocessing pipeline involving image scaling dataset augmentation artifact removal tailored nuances efficient net models using efficient net b0b7 dataset transfer learning finetunes cnns pretrained imagenet weights rigorous evaluation employs key metrics like precision recall accuracy f1 score confusion matrices assess impact transfer learning finetuning efficient net variants performance classifying diverse skin cancer categories research showcases effectiveness tailored preprocessing pipeline efficient net models transfer learning finetuning significantly enhance models ability discern diverse skin cancer categories evaluation eight efficient net models b0b7 skin cancer classification reveals distinct performance patterns across various cancer classes majority class benign kertosis achieves high accuracy 87 challenges arise accurately classifying eczema classes melanoma despite minority representation 242 images attains average accuracy 8051 across models however suboptimal performance observed predicting warts molluscum 907 psoriasis 842 instances highlighting need targeted improvements accurately identifying specific skin cancer types study skin cancer classification utilizes efficientnets b0b7 transfer learning imagenet weights pinnacle performance observed efficientnetb7 achieving groundbreaking top1 accuracy 844 top5 accuracy 971 remarkably efficient 84 times smaller leading cnn detailed perclass classification exactitudes confusion matrices affirm proficiency signaling potential efficientnets precise dermatological image analysis