Artificial Neural Networks (ANN) for automatic detection of dendritic-shaped cancer cells of cutaneous melanoma in Reflectance Confocal Microscopy (RCM) images

Abstract

Melanoma (MM) is one of the tumors with the highest incidence. In Italy, MM affected about 13,700 patients out of 373,000 new cases of cancer in 2018, with prognosis dependent on the degree of tumor invasion and presence of metastasis at diagnosis: only an early detection can lead to a better prognosis. Recent evidence suggests that MM is a family of different tumors with varying abilities to grow and metastasize: dendritic-shaped tumor cells were typically found in thin MM in situ. Reflectance Confocal Microscopy (RCM) is a non-invasive imaging tool that enables in vivo observation of the skin at a quasi-histological resolution, providing transverse-section grayscale images related to refractive index of different tissues. In this work, a dataset of RCM images, from 13 healthy subjects and 22 patients affected by MM in situ, were used to train a Multi-Layer Perceptron (MLP) artificial neural network. Each image was subdivided into sub-blocks, labeled as positive if containing significant clusters of dendritic-shaped tumour cells. In each block, various standard features were calculated, e.g. Haralick's and features from the run-length matrices. The MLP was trained to recognize the presence of clusters of dendritic-shaped cancer cells. The preliminary results are encouraging, giving AUC=0.81 with about 73% accuracy. Tests are currently underway to improve quality.