DRDr II: Detecting the Severity Level of Diabetic Retinopathy Using Mask RCNN and Transfer Learning

Shenavarmasouleh, Farzan; Mohammadi, Farid Ghareh; Amini, M. Hadi; Arabnia, Hamid R.

doi:10.48550/arxiv.2011.14733

Cited by 2 publications

(4 citation statements)

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“…Table 2 presents the accuracy of the test scenarios with their associated confusion matrices. The results show that the test option with classes, bounding boxes, and masks yields the best result in comparison with others and we achieve slightly better performance than our previous result in DRDrII [4].…”

Section: Phase Two Resultsmentioning

confidence: 52%

“…Following our work in DRDr [3] and DRDrII [4], our main goal here is to combine the previous networks and use the new model crafted by unifying them to 1. detect everything about lesions via a single model: lesions masks, bounding boxes, lesion types, along with the overall severity of the instances per image. 2. increase the accuracy of the severity classification that we achieved in DRDrII.…”

Section: Goalsmentioning

confidence: 99%

“…For example, researchers in [3] presented a new method called DRDr to generate segmentation masks for two popular types of eye lesions: exudates and microaneurysms. Additionally, Shenavarmasouleh et al in [4] established DRDr2 to extend the lesion detection process by generating instance masks and classifying the fundus images with respect to the severity cases. DRDr2 seeks for lesions severity manually which is considered as its main drawback.…”

Section: Related Workmentioning

confidence: 99%

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DRDrV3: Complete Lesion Detection in Fundus Images Using Mask R-CNN, Transfer Learning, and LSTM

Shenavarmasouleh,

Mohammadi,

Amini

et al. 2021

Preprint

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Medical Imaging is one of the growing fields in the world of computer vision. In this study, we aim to address the Diabetic Retinopathy (DR) problem as one of the open challenges in medical imaging. In this research, we propose a new lesion detection architecture, comprising of two sub-modules, which is an optimal solution to detect and find not only the type of lesions caused by DR, their corresponding bounding boxes, and their masks; but also the severity level of the overall case. Aside from traditional accuracy, we also use two popular evaluation criteria to evaluate the outputs of our models, which are intersection over union (IOU) and mean average precision (mAP). We hypothesize that this new solution enables specialists to detect lesions with high confidence and estimate the severity of the damage with high accuracy.

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Section: Phase Two Resultsmentioning

confidence: 52%

Section: Goalsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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DRDrV3: Complete Lesion Detection in Fundus Images Using Mask R-CNN, Transfer Learning, and LSTM

Shenavarmasouleh,

Mohammadi,

Amini

et al. 2021

Preprint

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“…Apart from playing a pivotal role in the recent advances of the main fields, these datasets also proved to be useful when used with transfer learning methods to help underlying disciplines such as biomedical imaging [18,19,20]. However, the aforementioned datasets are prune to restrictions.…”

Section: Introductionmentioning

confidence: 99%

Embodied AI-Driven Operation of Smart Cities: A Concise Review

Shenavarmasouleh¹,

Mohammadi²,

Amini³

et al. 2021

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A smart city can be seen as a framework, comprised of Information and Communication Technologies (ICT). An intelligent network of connected devices that collect data with their sensors and transmit them using wireless and cloud technologies in order to communicate with other assets in the ecosystem plays a pivotal role in this framework. Maximizing the quality of life of citizens, making better use of available resources, cutting costs, and improving sustainability are the ultimate goals that a smart city is after. Hence, data collected from these connected devices will continuously get thoroughly analyzed to gain better insights into the services that are being offered across the city; with this goal in mind that they can be used to make the whole system more efficient. Robots and physical machines are inseparable parts of a smart city. Embodied AI is the field of study that takes a deeper look into these and explores how they can fit into real-world environments. It focuses on learning through interaction with the surrounding environment, as opposed to Internet AI which tries to learn from static datasets. Embodied AI aims to train an agent that can See (Computer Vision), Talk (NLP), Navigate and Interact with its environment (Reinforcement Learning), and Reason (General Intelligence), all at the same time. Autonomous driving cars and personal companions are some of the examples that benefit from Embodied AI nowadays. In this paper, we attempt to do a concise review of this field. We will go through its definitions, its characteristics, and its current achievements along with different algorithms, approaches, and solutions that are being used in different components of it (e.g. Vision, NLP, RL). We will then explore all the available simulators and 3D interactable databases that will make the research in this area feasible. Finally, we will address its challenges and identify its potentials for future research.

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DRDr II: Detecting the Severity Level of Diabetic Retinopathy Using Mask RCNN and Transfer Learning

Cited by 2 publications

References 19 publications

DRDrV3: Complete Lesion Detection in Fundus Images Using Mask R-CNN, Transfer Learning, and LSTM

DRDrV3: Complete Lesion Detection in Fundus Images Using Mask R-CNN, Transfer Learning, and LSTM

Embodied AI-Driven Operation of Smart Cities: A Concise Review

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