Classifying brain tumors on magnetic resonance imaging by using convolutional neural networks
MA Gómez-Guzmán, L Jiménez-Beristaín… - Electronics, 2023 - mdpi.com
The study of neuroimaging is a very important tool in the diagnosis of central nervous system
tumors. This paper presents the evaluation of seven deep convolutional neural network …
tumors. This paper presents the evaluation of seven deep convolutional neural network …
Intelligent hybrid deep learning model for breast cancer detection
Breast cancer (BC) is a type of tumor that develops in the breast cells and is one of the most
common cancers in women. Women are also at risk from BC, the second most life …
common cancers in women. Women are also at risk from BC, the second most life …
[HTML][HTML] Robust clinical applicable CNN and U-Net based algorithm for MRI classification and segmentation for brain tumor
Early diagnosis of brain tumors is critical for enhancing patient prognosis and treatment
options, while accurate classification and segmentation of brain tumors are vital for …
options, while accurate classification and segmentation of brain tumors are vital for …
PatchResNet: multiple patch division–based deep feature fusion framework for brain tumor classification using MRI images
Modern computer vision algorithms are based on convolutional neural networks (CNNs),
and both end-to-end learning and transfer learning modes have been used with CNN for …
and both end-to-end learning and transfer learning modes have been used with CNN for …
CerCan· Net: Cervical cancer classification model via multi-layer feature ensembles of lightweight CNNs and transfer learning
O Attallah - Expert Systems with Applications, 2023 - Elsevier
Cervical cancer ranks among the most prevalent causes of fatality in women around the
world. Early diagnosis is essential for treating cervical cancer using pap smear slides, but it …
world. Early diagnosis is essential for treating cervical cancer using pap smear slides, but it …
Parameter adaptive unit-linking pulse coupled neural network based MRI–PET/SPECT image fusion
Medical image fusion has many applications to healthcare that is accomplished by
extracting and then combining the complementary information from multiple medical images …
extracting and then combining the complementary information from multiple medical images …
Multimodal brain tumor segmentation and classification from MRI scans based on optimized DeepLabV3+ and interpreted networks information fusion empowered …
MS Ullah, MA Khan, HM Albarakati… - Computers in Biology …, 2024 - Elsevier
Explainable artificial intelligence (XAI) aims to offer machine learning (ML) methods that
enable people to comprehend, properly trust, and create more explainable models. In …
enable people to comprehend, properly trust, and create more explainable models. In …
Healthcare As a Service (HAAS): CNN-based cloud computing model for ubiquitous access to lung cancer diagnosis
The field of automated lung cancer diagnosis using Computed Tomography (CT) scans has
been significantly advanced by the precise predictions offered by Convolutional Neural …
been significantly advanced by the precise predictions offered by Convolutional Neural …
Novel insights in spatial epidemiology utilizing explainable AI (XAI) and remote sensing
The COVID-19 pandemic has affected many aspects of human life around the world, due to
its tremendous outcomes on public health and socio-economic activities. Policy makers …
its tremendous outcomes on public health and socio-economic activities. Policy makers …
Brain MRI analysis using deep neural network for medical of internet things applications
Researchers are increasingly interested in leveraging the Internet of Things in medical and
healthcare systems to provide better solutions such as remote health monitoring, personal …
healthcare systems to provide better solutions such as remote health monitoring, personal …