AI-based carcinoma detection and classification using histopathological images: A systematic review. (March 2022)

• Record Type: Journal Article
• Authors: Prabhu, Swathi; Prasad, Keerthana; Robels-Kelly, Antonio; Lu, Xuequan
• Is Part Of: Computers in biology and medicine. Volume 142(2022)
• Publication Date: 2022-03
• Subjects: Adenocarcinoma -- Squamous cell carcinoma -- Histopathology -- Artificial intelligence -- Deep learning -- Diagnostic system; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.105209
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: 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. Highlights: This study provides a comprehensive summary of the state-of-the-art literature in carcinoma detection and classification. It examines AI-based approaches used in automated detection and classification of carcinoma based on organ of origin. It highlights the relevance of the domain of interest through descriptive analysis. It attempts to understand dataset details and the evolution of performance metrics reported in the literature. It brings out the shortcomings of current AI-based approaches in carcinoma diagnosis.