Computer-Aided Cancer Diagnosis via Machine Learning and Deep Learning: A comparative review

In the past decade, the number of computer-aided-diagnosis (CAD) studies via Machine Learning (ML) and deep learning (DL) algorithms has grown exponentially and has seen an incredible spike in their applications, especially in the biomedical field [1, 2, 3, 4, 5, 6]. Their use in cancer detection is numerous and allows for rapid diagnosis of different cancer types. The world has seen an impressive increase in cancer cases. Not only the number has continuously grown; but with around 9 million cancer deaths in 2017 worldwide, and 2 million new cases and 600 thousand cancer deaths in 2021 in the United States alone [7], the need for appropriate tools of detection and diagnosis is becoming more and more pressing both for accuracy and rapidity [8]. According to the American Cancer Society, the chances of survival over 5 years for an individual increase by 90% when cancers are detected early. Similarly, screening for breast cancers has resulted in a lower death risk of 20-40% [9, 10]. However, lung cancers are detected in their later stages in 70 % of the cases [11]. In addition to decreasing chances of survival, late cancer detection leads to potential outbreaks of cancerous cells in other parts of the body leading to metastasis which needs to be prevented at all costs and provide a challenge for machine learning techniques. Amongst all cancers, the tracheal, bronchus, and lung cancers are the most prevalent ones with a little under 2 million deaths, closely followed by colon, stomach, and liver cancers (digestive tract cancers) with around 800 thousand deaths for the year of 2017.