[HTML][HTML] Polarimetric imaging via deep learning: A review
Polarization can provide information largely uncorrelated with the spectrum and intensity.
Therefore, polarimetric imaging (PI) techniques have significant advantages in many fields …
Therefore, polarimetric imaging (PI) techniques have significant advantages in many fields …
Classification of SAR and PolSAR images using deep learning: A review
Advancement in remote sensing technology and microwave sensors explores the
applications of remote sensing in different fields. Microwave remote sensing encompasses …
applications of remote sensing in different fields. Microwave remote sensing encompasses …
Internet of health things-driven deep learning system for detection and classification of cervical cells using transfer learning
Cervical cancer is one of the fastest growing global health problems and leading cause of
mortality among women of develo** countries. Automated Pap smear cell recognition and …
mortality among women of develo** countries. Automated Pap smear cell recognition and …
Hybrid models based on genetic algorithm and deep learning algorithms for nutritional Anemia disease classification
Deep learning algorithms are an important part of disease prediction and diagnosis by
analyzing health data. If not diagnosed and treated early, symptoms of nutritional anemia …
analyzing health data. If not diagnosed and treated early, symptoms of nutritional anemia …
Classification and diagnosis of cervical cancer with stacked autoencoder and softmax classification
Cervical cancer is one of the most common cancer types in the world, which causes many
people to lose their lives. Cancer research is of importance since early diagnosis of cancer …
people to lose their lives. Cancer research is of importance since early diagnosis of cancer …
Integration of convolutional neural networks and object-based post-classification refinement for land use and land cover map** with optical and SAR data
Object-based image analysis (OBIA) has been widely used for land use and land cover
(LULC) map** using optical and synthetic aperture radar (SAR) images because it can …
(LULC) map** using optical and synthetic aperture radar (SAR) images because it can …
Diagnosis and classification of cancer using hybrid model based on ReliefF and convolutional neural network
Abstract Machine learning and deep learning methods aims to discover patterns out of
datasets such as, microarray data and medical data. In recent years, the importance of …
datasets such as, microarray data and medical data. In recent years, the importance of …
Transfer learning for SAR image classification via deep joint distribution adaptation networks
J Geng, X Deng, X Ma, W Jiang - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
The problem of different characters of heterogeneous synthetic aperture radar (SAR) images
leads to poor performances for transfer learning of SAR image classification. To address this …
leads to poor performances for transfer learning of SAR image classification. To address this …
KDSAE: Chronic kidney disease classification with multimedia data learning using deep stacked autoencoder network
Abstract In recent times, Chronic Kidney Disease (CKD) has affected more than 10% of the
population worldwide and millions of people die every year. So, early-stage detection of …
population worldwide and millions of people die every year. So, early-stage detection of …
DCAVN: Cervical cancer prediction and classification using deep convolutional and variational autoencoder network
Early detection, early diagnosis and classification of the cancer type facilitates faster disease
management of patients. Cervical cancer is fourth most pervasive cancer type which affects …
management of patients. Cervical cancer is fourth most pervasive cancer type which affects …