Cognitive-Inspired Computer Vision Assist System for Diabetic Retinopathy Detection from Fundus Images
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B. Lakshmanan
Abstract
Diabetic retinopathy (DR) is one of the major reasons for blindness in humans. DR detection at the early stage is a very hard and challenging one. Rapid advancements in computer-aided systems necessitate an error-prone framework that can detect DR severity grading accurately. We attempt to design a cognitive-inspired intelligent tool for detecting DR from diabetic retinal fundus images. We presented novel framework which consists of stages of a deep light-weight convolution neural network (CNN) model that can be able to process and perform well even for lowquality diabetic retinopathy images. The proposed framework can be able to extract hard and soft exudates from the images very accurately for further processing using a small attention-based mechanism inspired by the small face attention network. The system is evaluated on the benchmark APTOS dataset and shows improved performance over traditional techniques.
Abstract
Diabetic retinopathy (DR) is one of the major reasons for blindness in humans. DR detection at the early stage is a very hard and challenging one. Rapid advancements in computer-aided systems necessitate an error-prone framework that can detect DR severity grading accurately. We attempt to design a cognitive-inspired intelligent tool for detecting DR from diabetic retinal fundus images. We presented novel framework which consists of stages of a deep light-weight convolution neural network (CNN) model that can be able to process and perform well even for lowquality diabetic retinopathy images. The proposed framework can be able to extract hard and soft exudates from the images very accurately for further processing using a small attention-based mechanism inspired by the small face attention network. The system is evaluated on the benchmark APTOS dataset and shows improved performance over traditional techniques.
Kapitel in diesem Buch
- Frontmatter I
- Contents V
- List of Authors VII
- The Learning of Deep Learning: Overview, Methods, and Applications 1
- Foundation of Cognitive Computing 19
- Applications and Implications of Artificial Intelligence and Deep Learning in Computer Vision 35
- A Study of Voice Recognition System Using Deep Learning Techniques 53
- Building Machine Learning–Based Prediction System for Critical Diseases 75
- An Overview of Internet of Things and Machine Learning for Smart Healthcare 97
- Nutrition Food Recognition Using Deep Learning Algorithm for Physically Challenged Human Being 113
- Healthcare Data Analysis Using Deep Learning Paradigm 129
- Cognitive Authentication for Smart Healthcare System 149
- Cognitive-Inspired Computer Vision Assist System for Diabetic Retinopathy Detection from Fundus Images 165
- A Novel Deep Belief Neural Network Model for Abstractive Text Summarization 179
- Index 201
Kapitel in diesem Buch
- Frontmatter I
- Contents V
- List of Authors VII
- The Learning of Deep Learning: Overview, Methods, and Applications 1
- Foundation of Cognitive Computing 19
- Applications and Implications of Artificial Intelligence and Deep Learning in Computer Vision 35
- A Study of Voice Recognition System Using Deep Learning Techniques 53
- Building Machine Learning–Based Prediction System for Critical Diseases 75
- An Overview of Internet of Things and Machine Learning for Smart Healthcare 97
- Nutrition Food Recognition Using Deep Learning Algorithm for Physically Challenged Human Being 113
- Healthcare Data Analysis Using Deep Learning Paradigm 129
- Cognitive Authentication for Smart Healthcare System 149
- Cognitive-Inspired Computer Vision Assist System for Diabetic Retinopathy Detection from Fundus Images 165
- A Novel Deep Belief Neural Network Model for Abstractive Text Summarization 179
- Index 201
