Plant disease diagnosis using deep transfer learning architectures- VGG19, MobileNetV2 and Inception-V3
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.
Autores principales: | Kobra, Khadija-Tul, Suham, Rahmatul Rashid, Fairooz, Maisha |
---|---|
Otros Autores: | Uddin, Jia |
Formato: | Tesis |
Lenguaje: | English |
Publicado: |
Brac University
2022
|
Materias: | |
Acceso en línea: | http://hdl.handle.net/10361/17334 |
Ejemplares similares
-
Myocardial infarction detection using ECG signal applying deep learning techniques - ConvNet, VGG16, InceptionV3 and MobileNet
por: Promita, Samanta Tabassum, et al.
Publicado: (2022) -
Comparative analysis between Inception-v3 and other learning systems using facial expressions detection
por: Nivrito, AKM, et al.
Publicado: (2016) -
Deep neural network models for COVID-19 diagnosis from CT-Scan, explainability and analysis using trained models
por: Islam, Tahsin, et al.
Publicado: (2021) -
A comparative study of lung cancer prediction using deep learning
por: Mugdho, Aka Mohammad, et al.
Publicado: (2023) -
Detection of prodromal parkinson’s disease with fMRI data and deep neural network approaches
por: Shahriar, Farhan, et al.
Publicado: (2021)