ItemConference Paper

A Novel Architecture for a Medical Image Recognition System Using Deep Learning-Based Multiple Regression Evaluation

Title
A Novel Architecture for a Medical Image Recognition System Using Deep Learning-Based Multiple Regression Evaluation
Creator
Parashar, Jyoti; Upreti, Kamal; Fatima, Iram; Gangwar, Divya; Saini, Ashok Kumar; Vats, Prashant
Description
Models based on machine learning are optimization models that collect data, assess it, and deliver the reports required by specialists and management to make the best decisions. The application of contemporary machine learning allows the organization to quickly analyze photographs, differentiate voices assist in providing customer service, assess the information that is at hand, and uncover connections to aid in decision-making processes. The results of this investigation use quantitative methodologies to collect data and analyze it using mathematical procedures such as regression modeling as well as analysis of variance. Deep learning techniques applied to digital imaging, particularly in medical treatment, can increase picture quality, aid in modeling, aid in making the best possible diagnosis, and successfully address demands from patients. To analyze the hypothesis, investigators intend to utilize statistical approaches such as descriptive data analysis, regression evaluation, and analysis of variance (ANOVA). The authors employ the purposive sample approach to choose respondents from the healthcare industry. Purpose sampling is a non-probability sampling approach. Researchers collected data from 193 respondents working at hospitals that are privately owned in Southern Asia. As stated by the study, all factors, including efficiently meeting patient needs, have a probability value of under 0.05, indicating that they are statistically noteworthy. Following the study, the coefficient of variance (R squared) is 0.744, or 74.4%. According to the study, there is a high association between better image quality and ML-based digital picture identification systems. The recognition of patterns and the application of artificial intelligence to computerized recognition of pictures also have a close link. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Source
Smart Innovation, Systems and Technologies;Volume;398;pp.325-341
Date
01-01-2025
Publisher
Springer Science and Business Media Deutschland GmbH
Subject
ANOVA analysis; Deep learning; Digital imaging; Regression analysis
Coverage
Parashar J., Department of Computer Science & Engineering, Dr. Akhilesh Das Gupta Institute of Technology & Management, New Delhi, India; Upreti K., Department of Computer Science, Christ University, Delhi NCR Campus, Ghaziabad, India; Fatima I., Department of AI & ML, Dr. Akhilesh Das Gupta Institute of Technology & Management, New Delhi, India; Gangwar D., Department of Management, Babu Banarasi Das Institute of Technology & Management, New Delhi, India; Saini A.K., Department of Computer Science & Engineering, School of Computer Science & Engineering, Manipal University Jaipur, Rajasthan, Jaipur, India; Vats P., Department of Computer Science & Engineering, School of Computer Science & Engineering, Manipal University Jaipur, Rajasthan, Jaipur, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 21903018; ISBN: 978-981975199-0;
Format
online
Language
English
Type
Conference paper

Citation

Parashar, Jyoti; Upreti, Kamal; Fatima, Iram; Gangwar, Divya; Saini, Ashok Kumar; Vats, Prashant, “A Novel Architecture for a Medical Image Recognition System Using Deep Learning-Based Multiple Regression Evaluation,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25628.

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