Machine Learning Implementation on Medical Domain to Identify Disease Insights using TMS

Sasubilli, Satya Murthy; Kumar, Abhishek; Dutt, Vishal

doi:10.1109/icacce49060.2020.9154960

Cited by 94 publications

(8 citation statements)

References 7 publications

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“…Contrasting positive and negative aspects of the healthcare industry, AI provides a number of advantages to people, some of which include streamlined decision making [10][11][12]19,61], health surveillance, including monitoring of elderly patients [13,53,69,72,85], early diagnosis [11,50,51,70,86,87] and process simplification [19,37,62].…”

Section: Advantages and Drawbacks For The Healthcare Sectormentioning

confidence: 99%

“…Computing in the cloud and AI are two technologies that are increasingly being put to use in many applications within the medical sector. These systems are responsible for data collection, processing, storage, monitoring, and collaboration [86,96,118,119]. Despite the fact that these systems offer a number of benefits, there are also a number of obstacles associated with them, including ethical concerns, security concerns, consequences for users' privacy, and cybersecurity concerns.…”

Section: Ethical Issues About Aimentioning

confidence: 99%

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Recent Advances of Artificial Intelligence in Healthcare: A Systematic Literature Review

et al. 2023

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The implementation of artificial intelligence (AI) is driving significant transformation inside the administrative and clinical workflows of healthcare organizations at an accelerated rate. This modification highlights the significant impact that AI has on a variety of tasks, especially in health procedures relating to early detection and diagnosis. Papers done in the past imply that AI has the potential to increase the overall quality of services provided in the healthcare industry. There have been reports that technology based on AI can improve the quality of human existence by making life simpler, safer, and more productive. A comprehensive analysis of previous scholarly research on the use of AI in the health area is provided in this research in the form of a literature review. In order to propose a classification framework, the review took into consideration 132 academic publications sourced from scholarly sources. The presentation covers both the benefits and the issues that AI capabilities provide for individuals, medical professionals, corporations, and the health industry. In addition, the social and ethical implications of AI are examined in the context of the output of value-added medical services for decision-making processes in healthcare, privacy and security measures for patient data, and health monitoring capabilities.

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Section: Advantages and Drawbacks For The Healthcare Sectormentioning

confidence: 99%

Section: Ethical Issues About Aimentioning

confidence: 99%

Recent Advances of Artificial Intelligence in Healthcare: A Systematic Literature Review

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Using the node information in behavioral data, the original data were converted to semantic trajectories. Following the time-based trajectory segmentation method, this paper correctly extracts the trajectory from the activities of each student on a day [21][22][23]. Note that the day refers to the "day" for the student to complete a set of activities, rather than a natural day (from 00:00 to 00:00).…”

Section: Preparatory Workmentioning

confidence: 99%

Abnormal Behavior Recognition in Classroom Pose Estimation of College Students Based on Spatiotemporal Representation Learning

Xie¹,

Zhang²,

Liu³

2021

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Artificial intelligence and fifth generation (5G) technology are widely adopted to evaluate the classroom poses of college students, with the help of campus video surveillance equipment. To ensure the effective learning in class, it is important to detect and intervene in abnormal behaviors like sleeping and using cellphones in time. Based on spatiotemporal representation learning, this paper presents a deep learning algorithm to evaluate classroom poses of college students. Firstly, feature engineering was adopted to mine the moving trajectories of college students, which were used to determine student distribution and establish a classroom prewarning system. Then, k-means clustering (KMC) was employed for cluster analysis on different student groups, and identify the features of each group. For a specific student group, the classroom surveillance video was decomposed into several frames; the edge of each frame was extracted by edge detection algorithm, and imported to the proposed convolutional neural network (CNN). Experimental results show that our algorithm is 5% more accurate than the benchmark three-dimensional CNN (C3D), making it an effective tool to recognize abnormal behaviors of college students in class.

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“…Solutions based on ML and DL (subsets of AI) are progressively utilized for decision making and prediction in medical imaging [5]. Among other things, enablers for such utilization are: (i) there exists an abundant amount of medical data available globally for research and development activities [6], and (ii) medical data is reasonable formed and labeled [7]. A typical digital pathology task includes classification of whole-slide images as being either healthy or containing signs of cancer [4].…”

Section: Introductionmentioning

confidence: 99%

Color-Optimized One-Pixel Attack Against Digital Pathology Images

Korpihalkola

Sipola

Kokkonen

2021

2021 29th Conference of Open Innovations Association (FRUCT)

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Modern artificial intelligence based medical imaging tools are vulnerable to model fooling attacks. Automated medical imaging methods are used for supporting the decision making by classifying samples as regular or as having characters of abnormality. One use of such technology is the analysis of whole-slide image tissue samples. Consequently, attacks against artificial intelligence based medical imaging methods may diminish the credibility of modern diagnosis methods and, at worst, may lead to misdiagnosis with improper treatment. This study demonstrates an advanced color-optimized one-pixel attack against medical imaging. A state-of-the-art one-pixel modification is constructed with minimal effect on the pixel's color value. This multi-objective approach mitigates the unnatural coloring of raw none-pixel attacks. Accordingly, it is infeasible or at least cumbersome for a human to see the modification in the image under analysis. This color-optimized one-pixel attack poses an advanced cyber threat against modern medical imaging and shows the importance of data integrity with image analysis.

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Machine Learning Implementation on Medical Domain to Identify Disease Insights using TMS

Cited by 94 publications

References 7 publications

Recent Advances of Artificial Intelligence in Healthcare: A Systematic Literature Review

Recent Advances of Artificial Intelligence in Healthcare: A Systematic Literature Review

Abnormal Behavior Recognition in Classroom Pose Estimation of College Students Based on Spatiotemporal Representation Learning

Color-Optimized One-Pixel Attack Against Digital Pathology Images

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