Deep Learning and the Future of Biomedical Image Analysis

Jyotiyana, Monika; Kesswani, Nishtha

doi:10.1007/978-3-030-33966-1_15

Cited by 15 publications

(3 citation statements)

References 43 publications

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“…The radiologist is considered one of the most expensive doctors in hospitals to compensate for the risk of radiation he/she is daily exposed to. DL technologies are now working to diagnose oncological and lung diseases more effectively than doctors [28,29]. It places the job of the radiologist among the economically feasible jobs by automating it with AI technology.…”

Section: Automatic Radiation Analysismentioning

confidence: 99%

Artificial Intelligence Applications in Healthcare Sector: Ethical and Legal Challenges

2022

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Recently, artificial intelligence (AI) has been one of the hottest topics in the technological world. Although it is involved in many domains, it was recently involved in the healthcare sector. AI can be used for diagnostics, drug development, treatment personalization, gene editing, disease prediction, and many more. It helps to improve healthcare services by benefiting medical professionals, hospitals, and patients. Saudi Arabia has a particular interest in the healthcare sector, and it has a clear vision for the future, which points toward the development of AI-based technologies. Few studies investigated the use of AI in Saudi healthcare, and most of them focused on healthcare employees' perceptions. This study is beyond the focus of the existing works. It aims at: 1) presenting the main AI-based healthcare applications; 2) exploring the use of AI in the Saudi healthcare sector; 3) addressing their ethical and legal challenges, along with the policy questions in Saudi healthcare; 4) studying the benefits of these AI-based applications and the acceptance of professionals to use AI in daily practice; 5) introducing the new Personal Data Protection Law (PDPL) in Saudi Arabia; and 6) discussing the importance of AI to the future of Saudi healthcare. To this purpose, a survey was distributed among four main Saudi hospitals. The findings showed that AI should not only lead to better health but also save manpower and simplify the healthcare processes. The respondents agreed that AI helps reflect human intellectual competencies and pushes its limits. Doi: 10.28991/ESJ-2022-06-04-05 Full Text: PDF

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Section: Automatic Radiation Analysismentioning

confidence: 99%

Artificial Intelligence Applications in Healthcare Sector: Ethical and Legal Challenges

2022

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“…Object detection and segmentation are two of the fundamental tasks in computer vision with numerous applications ranging from autonomous driving [28] to medical imaging [29]. The first successful architectures for object detection were region-based models [30], which first, generated region proposals, and performed classification and bounding box regression on the proposals independently.…”

Section: Detection and Segmentationmentioning

confidence: 99%

Fully neural object detection solutions for robot soccer

Szemenyei

Estivill‐Castro

2021

Neural Comput & Applic

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RoboCup is one of the major global AI events, gathering hundreds of teams from the world’s best universities to compete in various tasks ranging from soccer to home assistance and rescue. The commonality of these three seemingly dissimilar tasks is that in order to perform well, the robot needs to excel at the all major AI tasks: perception, control, navigation, strategy and planning. In this work, we focus on the first of these by presenting what is—to our knowledge—the first fully neural vision system for the Nao robot soccer. This is a challenging task, mainly due to the limited computational capabilities of the Nao robot. In this paper, we propose two novel neural network architectures for semantic segmentation and object detection that ensure low-cost inference, while improving accuracy by exploiting the properties of the environment. These models use synthetic transfer learning to be able to learn from a low number of hand-labeled images. The experiments show that our models outperform state-of-the-art methods such as Tiny YOLO at a fraction of the cost.

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“…In the health care sector, deep learning plays an important role in early and accurate diagnosis of disease and patient classification in a very short time. Deep Learning [1] is very popular in the diagnosis and the prediction of many diseases like diabetes, cancer, Alzheimer's and Parkinsonism, and many more. In recent years many researchers are also working on Parkinson's disease.…”

Section: Introductionmentioning

confidence: 99%

A deep learning approach for classification and diagnosis of Parkinson’s disease

2022

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Deep learning techniques are playing an important role in the classification and prediction of diseases.Undoubtedly deep learning has a promising future in the health sector, especially in medical imaging.The popularity of deep learning approaches is because of their ability to handle a large amount of data related to the patients with accuracy, reliability in a short span of time. However, the practitioners may take time in analyzing and generating reports. In this paper, we have proposed a Deep Neural Network-based classification model for Parkinson's disease. Our proposed method is one such good example giving faster and more accurate results for the classification of Parkinson's disease patients with excellent accuracy of 94.87%. Based on the attributes of the dataset of the patient, the model can be used for the identification of Parkinsonism's. We have also compared the results with other existing

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Deep Learning and the Future of Biomedical Image Analysis

Cited by 15 publications

References 43 publications

Artificial Intelligence Applications in Healthcare Sector: Ethical and Legal Challenges

Artificial Intelligence Applications in Healthcare Sector: Ethical and Legal Challenges

Fully neural object detection solutions for robot soccer

A deep learning approach for classification and diagnosis of Parkinson’s disease

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