Classification of Nasopharyngeal Cases using DenseNet Deep Learning Architecture

Autor:	Ahmad, W. S. H. M. W., Fauzi, M. F. A., Abdullahi, M. K., Lee, Jenny T. H., Basry, N. S. A., Yahaya, A, Ismail, A. M., Adam, A., Chan, Elaine W. L., Abas, F. S.
Rok vydání:	2024
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Nasopharyngeal carcinoma (NPC) is one of the understudied yet deadliest cancers in South East Asia. In Malaysia, the prevalence is identified mainly in Sarawak, among the ethnic of Bidayuh. NPC is often late-diagnosed because it is asymptomatic at the early stage. There are several tissue representations from the nasopharynx biopsy, such as nasopharyngeal inflammation (NPI), lymphoid hyperplasia (LHP), nasopharyngeal carcinoma (NPC) and normal tissue. This paper is our first initiative to identify the difference between NPC, NPI and normal cases. Seven whole slide images (WSIs) with gigapixel resolutions from seven different patients and two hospitals were experimented with using two test setups, consisting of a different set of images. The tissue regions are patched into smaller blocks and classified using DenseNet architecture with 21 dense layers. Two tests are carried out, each for proof of concept (Test 1) and real-test scenario (Test 2). The accuracy achieved for NPC class is 94.8% for Test 1 and 67.0% for Test 2. Comment: This article has been accepted in the Journal of Engineering Science and Technology (JESTEC) and awaiting publication
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2404.03188 Zobrazit plný text záznamu View this record from Arxiv