| Type of Material: | Thesis |
| Title: | Medical image analysis for pneumonia detection using deep cnn multimodal and transfer learning model a machine learning application |
| Researcher: | Baraiya, Mehul M |
| Guide: | Kothari, Ashish M |
| Department: | Electronics and Communication Engineering |
| Publisher: | Atmiya University |
| Place: | Rajkot |
| Year: | 2023 |
| Language: | English |
| Subject: | Chest Radiographs | Deep Convolutional Neural Networks | Engineering | Engineering and Technology | Engineering Electrical and Electronic | Machine Learning | Medical Image Analysis | Multimodal Learning | Pneumonia Detection | Transfer Learning | Electronics and Communication Engineering | Engineering and Technology |
| Dissertation/Thesis Note: | PhD; Electronics and Communication Engineering, Atmiya University, Rajkot; 2023 |
| Fulltext: | Shodhganga |
| 003 | IN-AhILN | |
| 005 | 2026-10-08 17:22:48 | |
| 008 | __ | 261008t2023||||ii#||||g|m||||||||||eng|| |
| 035 | __ | |a(IN-AhILN)th_464025 |
| 040 | __ | |dIN-AhILN|aATMI_360005 |
| 041 | __ | |aeng |
| 100 | __ | |eResearcher|aBaraiya, Mehul M |
| 110 | __ | |aElectronics and Communication Engineering|0U-0967|dRajkot|bAtmiya University|ein |
| 245 | __ | |aMedical image analysis for pneumonia detection using deep cnn multimodal and transfer learning model a machine learning application |
| 260 | __ | |aRajkot|c2023|bAtmiya University |
| 300 | __ | |aA4|dDVD|c139 |
| 502 | __ | |bPhD|d2023|cElectronics and Communication Engineering, Atmiya University, Rajkot |
| 518 | __ | |oDate of Award|d2023 |
| 518 | __ | |d2019|oDate of Registration |
| 518 | __ | |d2023|oDate of Completion |
| 520 | __ | |aAn important worldwide health issue is pneumonia, a potentially fatal respiratory illness. For a patient to receive appropriate treatment and care, pneumonia must be promptly and accurately detected. Recent years have seen the emergence of promising tools for automated pneumonia detection from chest radiographs, including medical image analysis techniques and machine learning algorithms. The deep convolutional neural network (Deep-CNN) multimodal model and transfer learning techniques are utilised to construct and evaluate a machine learning application for pneumonia detection in this abstract.The suggested methodology makes use of Deep-CNNsand Transfer Learning Model combination, which have proven to perform exceptionally well in image analysis applications. By using a multimodal approach, the model makes use of both the contextual information and visual data retrieved from chest radiographs, improving its capacity to identify significant patterns and features suggestive of pneumonia. Additionally, transfe |
| 650 | __ | |aElectronics and Communication Engineering|2UGC |
| 650 | __ | |aEngineering and Technology|2AIU |
| 653 | __ | |aChest Radiographs |
| 653 | __ | |aDeep Convolutional Neural Networks |
| 653 | __ | |aEngineering |
| 653 | __ | |aEngineering and Technology |
| 653 | __ | |aEngineering Electrical and Electronic |
| 653 | __ | |aMachine Learning |
| 653 | __ | |aMedical Image Analysis |
| 653 | __ | |aMultimodal Learning |
| 653 | __ | |aPneumonia Detection |
| 653 | __ | |aTransfer Learning |
| 700 | __ | |aKothari, Ashish M|eGuide |
| 856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/511651|yShodhganga |
| 905 | __ | |afromsg |
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