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Time Series Analysis of Clinical Dataset Using ImageNet Classifier

By
RADHA R ,
RADHA R

Department of Data Science and Business Systems, SRM Institute of Science and Technology, Kattankulathur, Chennai, India

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RADHA N ,
RADHA N

Department of Information Technology, Sri Sivasubramania Nadar College of Engineering, Chennai, India

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SWATHIKA R ,
SWATHIKA R

Department of Information Technology, Sri Sivasubramania Nadar College of Engineering, Chennai, India

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POONGAVANAM N, ,
POONGAVANAM N,

Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, India

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MISHMALA SUSHITH ,
MISHMALA SUSHITH

Department of Information Technology, Adithya Institute of Technology, Coimbatore, India

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Abstract

Deep learning is a bunch of calculations in AI that endeavor to learn in numerous levels, comparing to various degrees of deliberation. It regularly utilizes counterfeit brain organizations. The levels in these learned factual models compare to unmistakable degrees of ideas, where more significant level ideas are characterized from lower-level ones, and a similar lower level ideas can assist with characterizing numerous more elevated level ideas. As of late, an AI (ML) region called profound learning arose in the PC vision field and turned out to be exceptionally famous in many fields. It began from an occasion in late 2018, when a profound learning approach in light of a convolutional brain organization (CNN) won a mind-boggling triumph in the most popular overall com management rivalry, ImageNet Characterization. From that point forward, scientists in many fields, including clinical picture examination, have begun effectively partaking in the dangerously developing field of profound learning. In this section, profound learning procedures and their applications to clinical picture examination are studied. This study outlined 1) standard ML procedures in the PC vision field, 2) what has changed in ML when the presentation of profound learning, 3) ML models in profound learning, and 4) uses of profound figuring out how to clinical picture examination. Indeed, even before the term existed, profound learning, in particular picture input ML, was applied to an assortment of clinical picture examination issues, including harm and non-harm characterization, harm type grouping, harm or organ division, and sore location.

How to Cite

1.
R R, N R, R S, N, P, SUSHITH M. Time Series Analysis of Clinical Dataset Using ImageNet Classifier. Salud, Ciencia y Tecnología [Internet]. 2024 Apr. 12 [cited 2024 May 28];4:837. Available from: https://revista.saludcyt.ar/ojs/index.php/sct/article/view/837

The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.

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