Two Schemes for Automated Diagnosis of Lentigo on Confocal Microscopy Images

Reflectance Confocal Microscopy is an imaging modality increasingly used for diagnosis of skin pathologies in clinical context thanks to specific and rich information they provide. However, few studies apply automatic methods for prediction in this kind of images. In this paper, we investigate in this paper a classification on these images on three categories: Healthy, Benign and Malignant Lentigo. To this end, we implement three feature extraction methods, namely Wavelets, Haralick and CNN through Transfer Learning. Furthermore, we exploit these feature extraction within two approaches: the first one operates on the entire image and the second one operates at patch-level (multiple patches per image) by giving a score to each patch. The scores are merged later to build a final decision for an image. Results show that Transfer learning obtains the best results for the two approaches, particularly with Average pooling.