| Type of Material: | Thesis |
| Title: | Diabetic Retinopathy Detection Using SOBA Machine Learning Framework |
| Researcher: | Vijayan,T |
| Guide: | Sangeetha,M |
| Department: | Department of Electronics and Communication Engineering |
| Publisher: | Bharath University, Chennai |
| Place: | Chennai |
| Year: | 2021 |
| Language: | English |
| Subject: | Computer Science | Computer Science Artificial Intelligence | Engineering and Technology | Electronics and Communication Engineering | Engineering and Technology |
| Dissertation/Thesis Note: | PhD; Department of Electronics and Communication Engineering, Bharath University, Chennai, Chennai; 2021; D17EC503 |
| Fulltext: | Shodhganga |
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| 040 | __ | |aBHAU_600073|dIN-AhILN |
| 041 | __ | |aeng |
| 100 | __ | |aVijayan,T|eResearcher |
| 110 | __ | |aDepartment of Electronics and Communication Engineering|bBharath University, Chennai|dChennai|ein|0U-0446 |
| 245 | __ | |aDiabetic Retinopathy Detection Using SOBA Machine Learning Framework |
| 260 | __ | |aChennai|bBharath University, Chennai|c2021 |
| 300 | __ | |dDVD |
| 502 | __ | |bPhD|cDepartment of Electronics and Communication Engineering, Bharath University, Chennai, Chennai|d2021|oD17EC503 |
| 520 | __ | |aComputer vision-based image classification for disease proliferation or possibility of prognosis is an important approach and becomes one of the major needed tasks in the medical industry. Having such significant thrust area attracted the research studies here and triggered to address the problem of identifying the Diabetic Retinopathy (DR). As more research work surfaced out recently with encouraging results a novel framework SOBA is proposed and verified with unique combination of image processing algorithms of first of its kind and the four components are designed as follows. Firstly, the S-Aspect denotes the models in shallow learning based on architecture with few layers in a neural network or few levels in a decision trees/Rules and probabilistic networks. Secondly the O-aspect as Orchestration of Deep learning architectures. Thirdly the B-Aspect denotes balancing the class distribution and finally the A-Aspect as the attribute reduction. This framework can be considered as basis for the selection of |
| 650 | __ | |aElectronics and Communication Engineering|2UGC |
| 650 | __ | |aEngineering and Technology|2AIU |
| 653 | __ | |aComputer Science |
| 653 | __ | |aComputer Science Artificial Intelligence |
| 653 | __ | |aEngineering and Technology |
| 700 | __ | |aSangeetha,M|eGuide |
| 856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/349841|yShodhganga |
| 905 | __ | |afromsg |
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