| Type of Material: | Thesis |
| Title: | Developing lightweight cnn models for detecting lesions in the medical imaging |
| Researcher: | Sanagala, Siva Skandha |
| Guide: | Gupta, Suneet Kumar | Vijaya Kumar Koppula |
| Department: | School of Computer Science Engineering and Technology |
| Publisher: | Bennett University |
| Place: | Greater Noida |
| Year: | 2022 |
| Language: | English |
| Subject: | Computer Science | Computer Science Information Systems | Engineering and Technology | Computer Science and Information Technology | Engineering and Technology |
| Dissertation/Thesis Note: | PhD; School of Computer Science Engineering and Technology, Bennett University, Greater Noida; 2022 |
| Fulltext: | Shodhganga |
| 003 | IN-AhILN | |
| 005 | 2026-10-14 12:08:59 | |
| 008 | __ | 261014t2022||||ii#||||g|m||||||||||eng|| |
| 035 | __ | |a(IN-AhILN)th_464155 |
| 040 | __ | |dIN-AhILN|aBENN_201310 |
| 041 | __ | |aeng |
| 100 | __ | |eResearcher|aSanagala, Siva Skandha |
| 110 | __ | |aSchool of Computer Science Engineering and Technology|0U-0890|dGreater Noida|bBennett University|ein |
| 245 | __ | |aDeveloping lightweight cnn models for detecting lesions in the medical imaging |
| 260 | __ | |aGreater Noida|c2022|bBennett University |
| 300 | __ | |axxix;194p.|dDVD |
| 502 | __ | |cSchool of Computer Science Engineering and Technology, Bennett University, Greater Noida|d2022|bPhD |
| 518 | __ | |oDate of Award|d2022 |
| 518 | __ | |d2022|oDate of Completion |
| 520 | __ | |aClassification and characterization of the lesions in medical imaging are getting interested in the field of computer-aided diagnostics (CAD) systems for radiology. A num ber of machine learning (ML) algorithms are proposed for classification and characterization. All the existing models suffer from inter-observer variability. CNN models are based on Deep Neural Networks, which are distinct from typical Machine Learning methods such as k-NN, Decision-Trees, and so on. Moreover, the performance of Deep Neural Network-based approaches is better as compared to traditional Machine Learning approaches as these models extract the features from training data automatically. In the past, the researchers have proposed many CNN architectures such as VGG16, VGG19, Inception V3, MobileNet, ResNet50, etc. for the classification of 1000 class ImageNet datasets. These models can also be utilized for the classification of other datasets by transfer learning. While pre-trained CNN models work well, the high number of paramet |
| 650 | __ | |aComputer Science and Information Technology|2UGC |
| 650 | __ | |aEngineering and Technology|2AIU |
| 653 | __ | |aComputer Science |
| 653 | __ | |aComputer Science Information Systems |
| 653 | __ | |aEngineering and Technology |
| 700 | __ | |aGupta, Suneet Kumar|eGuide |
| 700 | __ | |aVijaya Kumar Koppula|eGuide |
| 856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/474583|yShodhganga |
| 905 | __ | |afromsg |
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