info@biomedres.us   +1 (720) 414-3554   One Westbrook Corporate Center, Suite 300, Westchester, IL 60154, USA   Site Map
ISSN: 2574 -1241

Impact Factor : 0.548

  Submit Manuscript

Short CommunicationOpen Access

Medical Database for Detecting Neoplastic Lesions in Human Colorectal Cancer with Deep Learning

Volume 7 - Issue 5

Rita Fioresi*1, Francesco Faglioni2 and Paola Sena3

  • Author Information Open or Close
    • 1Department of Mathematics, University of Bologna, Italy
    • 2Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, Italy
    • 3Department of Biomedical, University of Modena and Reggio Emilia, Italy

    *Corresponding author: Rita Fioresi, Ufficio D9, Dipartimento di Matematica, Piazza Porta San Donato 5, 40126 Bologna, Italy

Received: August 06, 2018;   Published: August 13, 2018

DOI: 10.26717/BJSTR.2018.07.001572

Full Text PDF

To view the Full Article   Peer-reviewed Article PDF

Abstract

Medical databases are fundamental for developing new techniques for early detection of neoplastic cells. They are however difficult to obtain, since the labelling of the images is often operator dependent, requires specialized skills and the written informed consent of the patient. The variability of structures in biological tissue poses a challenge to both manual and automated analysis of histopathology slides. Although some authors showed moderate to good agreement among expert pathologists, and satisfactory results on their intra-observer reliability, other studies found that even experienced pathologists frequently disagree on tissue classification, which may lead to the conclusion that solely using expert scoring as gold standard for histopathological assessment could be insufficient. Hence, there is a growing demand for robust computational methods in order to increase reproducibility of diagnoses. In this note we present a database containing images of preneoplastic and neoplastic colorectal tissues and in a forthcoming paper we will describe our proposed DL algorithm to classify them into the following categories: normal mucosa, early preneoplastic lesions, adenomas, cancer.

Abstract | Aim and Scope | Results and Methodology | References |