  The automated detection of cancerous tumors has attracted interest mainly
during the last decade, due to the necessity of early and efficient diagnosis
that will lead to the most effective possible treatment of the impending risk.
Several machine learning and artificial intelligence methodologies has been
employed aiming to provide trustworthy helping tools that will contribute
efficiently to this attempt. In this article, we present a low-complexity
convolutional neural network architecture for tumor classification enhanced by
a robust image augmentation methodology. The effectiveness of the presented
deep learning model has been investigated based on 3 datasets containing brain,
kidney and lung images, showing remarkable diagnostic efficiency with
classification accuracies of 99.33%, 100% and 99.7% for the 3 datasets
respectively. The impact of the augmentation preprocessing step has also been
extensively examined using 4 evaluation measures. The proposed low-complexity
scheme, in contrast to other models in the literature, renders our model quite
robust to cases of overfitting that typically accompany small datasets
frequently encountered in medical classification challenges. Finally, the model
can be easily re-trained in case additional volume images are included, as its
simplistic architecture does not impose a significant computational burden.
