  Early diagnosis of cancer often allows for a more vast choice of therapy
opportunities. After a cancer diagnosis, staging provides essential information
about the extent of disease in the body and the expected response to a
particular treatment. The leading importance of classifying cancer patients at
the early stage into high or low-risk groups has led many research teams, both
from the biomedical and bioinformatics field, to study the application of Deep
Learning (DL) methods. The ability of DL to detect critical features from
complex datasets is a significant achievement in early diagnosis and cell
cancer progression. In this paper, we focus the attention on osteosarcoma.
Osteosarcoma is one of the primary malignant bone tumors which usually afflicts
people in adolescence. Our contribution to the classification of osteosarcoma
cells is made as follows: a DL approach is applied to discriminate human
Mesenchymal Stromal Cells (MSCs) from osteosarcoma cells and to classify the
different cell populations under investigation. Glass slides of differ-ent cell
populations were cultured including MSCs, differentiated in healthy bone cells
(osteoblasts) and osteosarcoma cells, both single cell populations or mixed.
Images of such samples of isolated cells (single-type of mixed) are recorded
with traditional optical microscopy. DL is then applied to identify and
classify single cells. Proper data augmentation techniques and cross-fold
validation are used to appreciate the capabilities of a convolutional neural
network to address the cell detection and classification problem. Based on the
results obtained on individual cells, and to the versatility and scalability of
our DL approach, the next step will be its application to discriminate and
classify healthy or cancer tissues to advance digital pathology.
