A5030355643- Radiomics and Machine Learning in Medical Imaging
- COVID-19 diagnosis using AI
- Lung Cancer Diagnosis and Treatment
- AI in cancer detection
- Medical Imaging and Analysis
- Digital Radiography and Breast Imaging
- Face and Expression Recognition
- Digital Media Forensic Detection
- Advanced Neural Network Applications
- Global Cancer Incidence and Screening
- Image Retrieval and Classification Techniques
- Emotion and Mood Recognition
- Face recognition and analysis
- Image Processing Techniques and Applications
- Video Surveillance and Tracking Methods
Monash University Malaysia
2019-2021
Multimedia University
2017
Different types of Convolutional Neural Networks (CNNs) have been applied to detect cancerous lung nodules from computed tomography (CT) scans. However, the size a nodule is very diverse and can range anywhere between 3 30 millimeters. The high variation sizes makes classifying them difficult challenging task. In this study, we propose novel CNN architecture called Gated-Dilated (GD) networks classify as malignant or benign. Unlike previous studies, GD network uses multiple dilated...
Tuberculosis is threatening and hinders the socioeconomic development of countries burdened with TB cases. 75% cases are documented in productive age group 15–54 years. The definitive diagnoses methods timely expensive lack sensitivity recognizing all stages. CAD systems (Computer Aided Detection) will facilitate mass screening. In this work, we experimented use spatial pyramid Speed-up Robust Features (SURF) diagnosing TB. Though dense information representing lung anatomy imply substantial...
Architecture, size, and shape of glands are most important patterns used by pathologists for assessment cancer malignancy in prostate histopathological tissue slides. Varying structures along with cumbersome manual observations may result subjective inconsistent assessment. Cribriform gland irregular border is an feature Gleason pattern 4. We propose using deep neural networks cribriform classification images. $163708$ Hematoxylin Eosin (H\&E) stained images were extracted from...
Background and Objective: Early detection of lung cancer is crucial as it has high mortality rate with patients commonly present the disease at stage 3 above. There are only relatively few methods that simultaneously detect classify nodules from computed tomography (CT) scans. Furthermore, very studies have used semi-supervised learning for prediction. This study presents a complete end-to-end scheme to using state-of-the-art Self-training Noisy Student method on comprehensive CT screening...
Purpose: Lung nodules have very diverse shapes and sizes, which makes classifying them as benign/malignant a challenging problem. In this paper, we propose novel method to predict the malignancy of that capability analyze shape size nodule using global feature extractor, well density structure local extractor. Methods: We use Residual Blocks with 3x3 kernel for extraction, Non-Local extract features. The Block has ability features without huge number parameters. key idea behind is apply...