[Retracted] Enhance‐Net: An Approach to Boost the Performance of Deep Learning Model Based on Real‐Time Medical Images
Real‐time medical image classification is a complex problem in the world. Using IoT
technology in medical applications assures that the healthcare sectors improve the quality of …
technology in medical applications assures that the healthcare sectors improve the quality of …
BoostNet: a method to enhance the performance of deep learning model on musculoskeletal radiographs X-ray images
In clinical treatment, deep learning plays a pivotal role in medical image classification. Deep
learning techniques provide opportunities for radiologists and orthopedic to ease out their …
learning techniques provide opportunities for radiologists and orthopedic to ease out their …
[HTML][HTML] Deepening into the suitability of using pre-trained models of ImageNet against a lightweight convolutional neural network in medical imaging: An experimental …
Transfer learning (TL) has been widely utilized to address the lack of training data for deep
learning models. Specifically, one of the most popular uses of TL has been for the pre …
learning models. Specifically, one of the most popular uses of TL has been for the pre …
Two-stage selective ensemble of CNN via deep tree training for medical image classification
Medical image classification is an important task in computer-aided diagnosis systems. Its
performance is critically determined by the descriptiveness and discriminative power of …
performance is critically determined by the descriptiveness and discriminative power of …
Deep convolution neural network for big data medical image classification
Deep learning is one of the most unexpected machine learning techniques which is being
used in many applications like image classification, image analysis, clinical archives and …
used in many applications like image classification, image analysis, clinical archives and …
An evolutionary attention-based network for medical image classification
Deep learning has become a primary choice in medical image analysis due to its powerful
representation capability. However, most existing deep learning models designed for …
representation capability. However, most existing deep learning models designed for …
Medical image classification using a light-weighted hybrid neural network based on PCANet and DenseNet
Z Huang, X Zhu, M Ding, X Zhang - Ieee Access, 2020 - ieeexplore.ieee.org
Medical image classification plays an important role in disease diagnosis since it can
provide important reference information for doctors. The supervised convolutional neural …
provide important reference information for doctors. The supervised convolutional neural …
Deep convolutional neural network based medical image classification for disease diagnosis
Medical image classification plays an essential role in clinical treatment and teaching tasks.
However, the traditional method has reached its ceiling on performance. Moreover, by using …
However, the traditional method has reached its ceiling on performance. Moreover, by using …
[HTML][HTML] Optimization and fine-tuning of DenseNet model for classification of COVID-19 cases in medical imaging
T Chauhan, H Palivela, S Tiwari - International Journal of Information …, 2021 - Elsevier
It's been more than a year that the entire world is fighting against COVID-19 pandemic.
Starting from the Wuhan city in China, COVID-19 has conquered the entire world with its …
Starting from the Wuhan city in China, COVID-19 has conquered the entire world with its …
Deep learning for medical image processing: Overview, challenges and the future
MI Razzak, S Naz, A Zaib - … in BioApps: Automation of decision making, 2018 - Springer
The health care sector is totally different from any other industry. It is a high priority sector
and consumers expect the highest level of care and services regardless of cost. The health …
and consumers expect the highest level of care and services regardless of cost. The health …