:: ICCAS 2021 ::

WB4		Malaysia-Korea Joint Session on the Application of Deep Learning on Medical Images and Multi-Agent Control Systems
Time : October 13 (Wed) 13:00-14:30 Room : Room 4 (Online, 2F Mara)		Chair : Prof.Hyun Myung (KAIST, Korea)



13:00-13:15	WB4-1

The Diagnosis of COVID-19 by Means of Transfer Learning through X-ray Images
Anwar P.P. Abdul Majeed, Amiir Haamzah Mohamed Ismail(Universiti Malaysia Pahang, Malaysia)

This research investigates the diagnosis of COVID-19 through X-ray images by using transfer learning and fine-tuning of the fully connected layer. Hyperparameters such as dropout, p, number of neurons, and activation functions are investigated on which combinations of these hyperparameters will yield a good classification of the disease. The VGG19 learning model created by the Visual Geometry Group is used for extraction of features from the patient’s chest X-ray images. The findings in this research will open new possibilities in the diagnostics of COVID-19.



13:15-13:30	WB4-2

The Diagnosis of Diabetic Retinopathy: A Transfer Learning Approach
Anwar P.P. Abdul Majeed, Farhan Nabil Mohd Noor(Universiti Malaysia Pahang, Malaysia)

Diabetic Retinopathy (DR) is one of the complications of diabetes mellitus that occurs to the eye. It damages the blood vessels, which cause the leaking of the blood and other fluids due to the elevated blood glucose level. This research investigates the effectiveness of automatic screening by employing the Transfer Learning model such as VGG16 to extract the features and fed them to the Support Vector Machine (SVM), k-Nearest Neighbour (kNN) and Random Forest (RF) for the classification of DR



13:30-13:45	WB4-3

Automated Gastrointestinal Tract Classification Via Deep Learning and The Ensemble Method
Anwar P.P. Abdul Majeed, Omair Rashed Abdulwareth Almanifi(Universiti Malaysia Pahang, Malaysia)

Colorectal cancer is a leading cause of death among the cancer family with a record of almost a million moralities in 2020 alone. In most cases early diagnosis is possible by catching any of the precursors of the disease, many of which appear on the Gastrointestinal (GI) tract. This study shall embark on the use of the stacking ensemble method with multiple pre-trained CNN models for an accurate classification of GI tract using the publicly available dataset, i.e. Kvasir.