Classification of Nasopharyngeal Cases using DenseNet Deep Learning
  Architecture
W. S. H. M. W. Ahmad M. F. A. Fauzi M. K. Abdullahi Jenny T. H. Lee N. S. A. Basry A Yahaya A. M. Ismail A. Adam Elaine W. L. Chan F. S. Abas
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.