Deep learning for enhancing cancer identification and diagnosis

Abstract

One of the promising and significant fields of technology is the use of automated computer techniques, particularly machine learning, to facilitate and enhance medical analysis and diagnosis. In the area of artificial intelligence (AI), deep learning techniques using artificial neural networks (CNNs) so-called because they superficially resemble biological neural networks - are computational network models for discovering large, high-dimensional data sets (such as medical datasets) for complex structures and patterns. In this paper, the focus is on summarizing contemporary applications of various deep learning algorithms in the direction of cancer identification and diagnosis, and on re-implementing supervised learning for cancer detection after a new database based on the lung cancer detection project from the book Deep Learning with PyTorch. The automatic detection of lung malignancies from patient CT scans was re-implemented by deep learning. In this paper, the technique is applied to data provided by the Iraqi Oncology Teaching Hospital. The author demonstrates that the application of the new data will also maintain the accuracy of the identification, and thus promises to develop into a more comprehensive and general cancer detection and diagnosis method in the future as the algorithm and technology are further refined.