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.

翻译：癌性肿瘤的自动检测主要在过去十年引起了广泛关注，这是由于早期有效诊断的必要性，以便对潜在风险进行最有效的治疗。已有多种机器学习和人工智能方法被用于开发可信赖的辅助工具，以有效促进这一尝试。本文提出了一种低复杂度的卷积神经网络架构，用于肿瘤分类，并辅以鲁棒的图像增强方法。基于包含脑部、肾脏和肺部图像的三个数据集，对所提出的深度学习模型的有效性进行了研究，结果显示其具有显著的诊断效率，三个数据集的分类准确率分别达到99.33%、100%和99.7%。此外，我们还使用四种评估指标对图像增强预处理步骤的影响进行了广泛考察。与文献中的其他模型相比，所提出的低复杂度方案使我们的模型对过拟合情况具有较强的鲁棒性，而过拟合通常伴随医疗分类挑战中常见的小数据集出现。最后，由于该模型架构简单，未带来显著的计算负担，因此在添加更多体积图像时可轻松进行重新训练。