Two Schemes for Automated Diagnosis of Lentigo on Confocal Microscopy Images
Résumé
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.
Mots clés
Cancer
Pipelines
Pathology
Skin
Standards
biomedical optical imaging
convolutional neural nets
feature extraction
image classification
learning (artificial intelligence)
medical image processing
optical microscopy
wavelet transforms
confocal microscopy images
skin pathologies
patch-level
multiple patches
transfer learning
CNN
lentigo
Reflectance Confocal Microscopy
dermatology