International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Intelligent Diagnosis of Pneumonia using Machine & Deep learning Techniques |
|---|---|
| ABSTRACT | One of the most active areas of research is the possibility for big data to be used for predictive purposes with machine learning or deep learning techniques or algorithms to improve medicine and health. Therefore, there appears to be a recent need for a chest x-ray to plan to identify pneumonia in men and prediction specialists due to the growing amount of medical data and the inadvertent difficulty of detecting different medical conditions. The trained model, a legitimate feature extraction model, and a number of classifiers should therefore be able to distinguish between positive and negative data with high validity. In order to show how pneumonia can be predicted, this study used big data, deep learning, or machine learning approaches. Therefore, it would be wiser to develop an automated predictor to forecast pneumonia using big data and deep learning approaches. Convolutional neural networks, or CNNs, would likewise do well overall, with additional classifiers being used for this prediction. |
| TITLE | |
| AUTHOR | ESHAN DEO, OM AWALEKAR, TEJAS KHODADE, ANUPAMA BHALERAO |
| PUBLICATION DATE | 2025-10-29 |
| VOLUME | 175 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1310024 |
| pdf/24_Intelligent Diagnosis of Pneumonia using Machine & Deep learning Techniques.pdf | |
| KEYWORDS |
