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 …
Neural Networks for the Detection of COVID-19 and Other Diseases: Prospects and Challenges
M Azeem, S Javaid, RA Khalil, H Fahim, T Althobaiti… - Bioengineering, 2023 - mdpi.com
Artificial neural networks (ANNs) ability to learn, correct errors, and transform a large amount
of raw data into beneficial medical decisions for treatment and care has increased in …
of raw data into beneficial medical decisions for treatment and care has increased in …
A deep analysis of brain tumor detection from mr images using deep learning networks
Creating machines that behave and work in a way similar to humans is the objective of
artificial intelligence (AI). In addition to pattern recognition, planning, and problem-solving …
artificial intelligence (AI). In addition to pattern recognition, planning, and problem-solving …
Secure medical image transmission using deep neural network in e‐health applications
AA Alarood, M Faheem… - Healthcare …, 2023 - Wiley Online Library
Recently, medical technologies have developed, and the diagnosis of diseases through
medical images has become very important. Medical images often pass through the …
medical images has become very important. Medical images often pass through the …
Classification framework for medical diagnosis of brain tumor with an effective hybrid transfer learning model
Brain tumors (BTs) are deadly diseases that can strike people of every age, all over the
world. Every year, thousands of people die of brain tumors. Brain-related diagnoses require …
world. Every year, thousands of people die of brain tumors. Brain-related diagnoses require …
Grade classification of tumors from brain magnetic resonance images using a deep learning technique
S Srinivasan, PSM Bai, SK Mathivanan… - Diagnostics, 2023 - mdpi.com
To improve the accuracy of tumor identification, it is necessary to develop a reliable
automated diagnostic method. In order to precisely categorize brain tumors, researchers …
automated diagnostic method. In order to precisely categorize brain tumors, researchers …
A survey on deep learning in COVID-19 diagnosis
According to the World Health Organization statistics, as of 25 October 2022, there have
been 625,248,843 confirmed cases of COVID-19, including 65,622,281 deaths worldwide …
been 625,248,843 confirmed cases of COVID-19, including 65,622,281 deaths worldwide …
Brain tumor detection using VGG19 model on adadelta and SGD optimizer
In both grown-ups and juvenile, brain tumors are the tenth most predominant cause of death
rate. There are many different sorts of tumors, and each one has extremely slim odds of …
rate. There are many different sorts of tumors, and each one has extremely slim odds of …
Dual adaption based evolutionary algorithm for optimized the smart healthcare communication service of the Internet of Things in smart city
SP Singh, W Viriyasitavat, S Juneja, H Alshahrani… - Physical …, 2022 - Elsevier
Abstract The Internet of Things (IoT) is a revolutionary technique of sharing data for smart
devices that generates huge amounts of data from smart healthcare systems. Therefore …
devices that generates huge amounts of data from smart healthcare systems. Therefore …
A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images
Detecting brain tumours is complex due to the natural variation in their location, shape, and
intensity in images. While having accurate detection and segmentation of brain tumours …
intensity in images. While having accurate detection and segmentation of brain tumours …