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:在过去十年中,对癌症肿瘤的自动检测引起了人们的兴趣,主要原因是早期和高效诊断的必要性,这将导致尽可能最有效地处理即将到来的风险。采用了几种机器学习和人工智能方法,目的是提供可信赖的帮助工具,从而有效地促进这一尝试。在本条中,我们提出了一个通过强健的图像增强方法强化的肿瘤分类的低复杂性神经神经系统网络结构。根据包含大脑、肾和肺部图像的3个数据集,对所介绍的深层学习模型的有效性进行了调查,显示分类精度分别为99.33%、100%和99.7%的3个数据集具有显著的诊断效率。还利用4个评估措施广泛研究了增强前处理步骤的影响。与文献中的其他模型相比,拟议的低复杂性计划使得我们的模型非常适合通常伴随医疗分类挑战中经常遇到的小数据集的过度情况。最后,该模型可以很容易在包括更多数量图像的情况下进行再培训,因为其简单化结构并没有造成重大的计算负担。