Deep Learning for Accurate, Efficient, Economical, and ConsistentCancer Diagnosis Compared to Traditional Biopsy

Abstract

Early and precise cancer diagnosis is essential for enhancing the effectiveness of treatments. Traditional biopsy techniques, while reliable, are often time-consuming and economically inefficient. Furthermore, variations in diagnostic assessments among physicians introduce additional uncertainty in outcomes. This paper investigates the application of machine learning (ML) and deep learning (DL) methods to improve diagnostic accuracy and efficiency. It evaluates the advantages and disadvantages of feature-based versus image-based diagnostic approaches and introduces a new diagnostic workflow named AIStain. This workflow encompasses two pathways: one involving feature extraction followed by classical machine learning techniques, and the other using convolutional neural networks (CNNs) for deep learning analysis. Our analysis demonstrates that integrating machine learning can significantly enhance diagnostic speed, reduce costs, and improve consistency across evaluations without compromising accuracy. By leveraging advanced computational techniques, this approach aims to standardize cancer diagnostics and reduce the dependency on subjective human evaluation, potentially transforming cancer diagnosis practices.