  Histopathological image analysis is the gold standard to diagnose cancer.
Carcinoma is a subtype of cancer that constitutes more than 80% of all cancer
cases. Squamous cell carcinoma and adenocarcinoma are two major subtypes of
carcinoma, diagnosed by microscopic study of biopsy slides. However, manual
microscopic evaluation is a subjective and time-consuming process. Many
researchers have reported methods to automate carcinoma detection and
classification. The increasing use of artificial intelligence (AI) in the
automation of carcinoma diagnosis also reveals a significant rise in the use of
deep network models. In this systematic literature review, we present a
comprehensive review of the state-of-the-art approaches reported in carcinoma
diagnosis using histopathological images. Studies are selected from well-known
databases with strict inclusion/exclusion criteria. We have categorized the
articles and recapitulated their methods based on specific organs of carcinoma
origin. Further, we have summarized pertinent literature on AI methods,
highlighted critical challenges and limitations, and provided insights on
future research direction in automated carcinoma diagnosis. Out of 101 articles
selected, most of the studies experimented on private datasets with varied
image sizes, obtaining accuracy between 63% and 100%. Overall, this review
highlights the need for a generalized AI-based carcinoma diagnostic system.
Additionally, it is desirable to have accountable approaches to extract
microscopic features from images of multiple magnifications that should mimic
pathologists' evaluations.
