Deep learning as a new tool in the diagnosis of mycosis fungoides.

Mycosis Fungoides (MF) makes up the most of the cutaneous lymphomas. As a malignant disease, the greatest diagnostical challenge is to timely differentiate MF from inflammatory diseases. Contemporary computational methods successfully identify cell nuclei in histological specimens. Deep learning methods are especially favored for such tasks. A deep learning model was used to detect nuclei Hematoxylin-Eosin(H-E) stained micrographs. Nuclear properties are extracted after detection. A multi-layer perceptron classifier is used to detect lymphocytes specifically among the detected nuclei. The comparisons for each property between MF and non-MF were carried out using statistical tests the results are compared with the findings in the literature to provide a descriptive analysis as well. Random forest classifier method is used to build a model to classify MF and non-MF lymphocytes. 10 nuclear properties were statistically significantly different between MF and non-MF specimens. MF nuclei were smaller, darker and more heterogenous. Lymphocyte detection algorithm had an average 90.5% prediction power and MF detection algorithm had an average 94.2% prediction power. This project aims to fill the gap between computational advancement and medical practice. The models could make MF diagnoses easier, more accurate and earlier. The results also challenge the manually examined and defined nuclear properties of MF with the help of data abundance and computer objectivity.