A convolutional neural network of low complexity for tumor anomaly detection

TL;DR

This paper introduces a low-complexity convolutional neural network with robust image augmentation for accurate tumor detection across multiple datasets, emphasizing efficiency, robustness to overfitting, and ease of re-training.

Contribution

A novel, simple CNN architecture combined with a robust augmentation method that achieves high accuracy and reduces overfitting in tumor classification tasks.

Findings

Achieved classification accuracies of 99.33%, 100%, and 99.7% on brain, kidney, and lung datasets.

Demonstrated robustness to overfitting, especially with small datasets.

Model is computationally efficient and easily re-trainable with additional data.

Abstract

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.