  Melanoma diagnosed and treated in its early stages can increase the survival
rate. A projected increase in skin cancer incidents and a dearth of
dermatopathologists have emphasized the need for computational pathology
(CPATH) systems. CPATH systems with deep learning (DL) models have the
potential to identify the presence of melanoma by exploiting underlying
morphological and cellular features. This paper proposes a DL method to detect
melanoma and distinguish between normal skin and benign/malignant melanocytic
lesions in Whole Slide Images (WSI). Our method detects lesions with high
accuracy and localizes them on a WSI to identify potential regions of interest
for pathologists. Interestingly, our DL method relies on using a single CNN
network to create localization maps first and use them to perform slide-level
predictions to determine patients who have melanoma. Our best model provides
favorable patch-wise classification results with a 0.992 F1 score and 0.99
sensitivity on unseen data. The source code is
https://github.com/RogerAmundsen/Melanoma-Diagnosis-and-Localization-from-Whole-Slide-Images-using-Convolutional-Neural-Networks.
