A study of CNN and transfer learning in medical imaging: Advantages, challenges, future scope
This paper presents a comprehensive study of Convolutional Neural Networks (CNN) and
transfer learning in the context of medical imaging. Medical imaging plays a critical role in …
transfer learning in the context of medical imaging. Medical imaging plays a critical role in …
Artificial intelligence in dermatology image analysis: current developments and future trends
Z Li, KC Koban, TL Schenck, RE Giunta, Q Li… - Journal of clinical …, 2022 - mdpi.com
Background: Thanks to the rapid development of computer-based systems and deep-
learning-based algorithms, artificial intelligence (AI) has long been integrated into the …
learning-based algorithms, artificial intelligence (AI) has long been integrated into the …
Evolution of image segmentation using deep convolutional neural network: A survey
From the autonomous car driving to medical diagnosis, the requirement of the task of image
segmentation is everywhere. Segmentation of an image is one of the indispensable tasks in …
segmentation is everywhere. Segmentation of an image is one of the indispensable tasks in …
A review of object detection models based on convolutional neural network
Convolutional neural network (CNN) has turned to be the state of the art for object detection
task of computer vision. In this chapter, we have reviewed some popular state-of-the-art …
task of computer vision. In this chapter, we have reviewed some popular state-of-the-art …
A survey on deep transfer learning to edge computing for mitigating the COVID-19 pandemic
Global Health sometimes faces pandemics as are currently facing COVID-19 disease. The
spreading and infection factors of this disease are very high. A huge number of people from …
spreading and infection factors of this disease are very high. A huge number of people from …
Improving the efficiency of RMSProp optimizer by utilizing Nestrove in deep learning
There are several methods that have been discovered to improve the performance of Deep
Learning (DL). Many of these methods reached the best performance of their models by …
Learning (DL). Many of these methods reached the best performance of their models by …
CNN-LSTM vs. LSTM-CNN to predict power flow direction: a case study of the high-voltage subnet of northeast Germany
The massive installation of renewable energy sources together with energy storage in the
power grid can lead to fluctuating energy consumption when there is a bi-directional power …
power grid can lead to fluctuating energy consumption when there is a bi-directional power …
Comparative analysis of skin cancer (benign vs. malignant) detection using convolutional neural networks
MR Hasan, MI Fatemi… - Journal of …, 2021 - Wiley Online Library
We live in a world where people are suffering from many diseases. Cancer is the most
threatening of them all. Among all the variants of cancer, skin cancer is spreading rapidly. It …
threatening of them all. Among all the variants of cancer, skin cancer is spreading rapidly. It …
Machine learning in detecting covid-19 misinformation on twitter
Social media platforms such as Facebook, Instagram, and Twitter are an inevitable part of
our daily lives. These social media platforms are effective tools for disseminating news …
our daily lives. These social media platforms are effective tools for disseminating news …
Deep learning techniques for solar tracking systems: A systematic literature review, research challenges, and open research directions
Solar tracking systems have gained attention in recent years due to their potential to
increase the efficiency of various solar energy applications. Both traditional machine …
increase the efficiency of various solar energy applications. Both traditional machine …