On the analyses of medical images using traditional machine learning techniques and convolutional neural networks
Convolutional neural network (CNN) has shown dissuasive accomplishment on different
areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information …
areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information …
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 …
Dendrimer: An update on recent developments and future opportunities for the brain tumors diagnosis and treatment
A brain tumor is an uncontrolled cell proliferation, a mass of tissue composed of cells that
grow and divide abnormally and appear to be uncontrollable by the processes that normally …
grow and divide abnormally and appear to be uncontrollable by the processes that normally …
Lightweight EfficientNetB3 model based on depthwise separable convolutions for enhancing classification of leukemia white blood cell images
Acute lymphoblastic leukemia (ALL) is a type of leukemia cancer that arises due to the
excessive growth of immature white blood cells (WBCs) in the bone marrow. The ALL rate …
excessive growth of immature white blood cells (WBCs) in the bone marrow. The ALL rate …
[HTML][HTML] A precision-centric approach to overcoming data imbalance and non-IIDness in federated learning
Federated learning (FL) enables decentralized model training, but the distribution of data
across devices presents significant challenges to global model convergence. Existing …
across devices presents significant challenges to global model convergence. Existing …
Development of hybrid models based on deep learning and optimized machine learning algorithms for brain tumor Multi-Classification
Accurate classification of magnetic resonance imaging (MRI) images of brain tumors is
crucial for early diagnosis and effective treatment in clinical studies. In these studies, many …
crucial for early diagnosis and effective treatment in clinical studies. In these studies, many …
Improved Multiclass Brain Tumor Detection via Customized Pretrained EfficientNetB7 Model
A brain tumor considered the deadliest disease in the world. Patients with misdiagnoses and
insufficient treatment have a lower chance of surviving for life. However, for diagnosing the …
insufficient treatment have a lower chance of surviving for life. However, for diagnosing the …
Towards Improving Breast Cancer Classification using an Adaptive Voting Ensemble Learning Algorithm
Over the past decade, breast cancer has been the most common type of cancer in women.
Different methods were proposed for breast cancer detection. These methods mainly classify …
Different methods were proposed for breast cancer detection. These methods mainly classify …
Explainable Deep Learning Approach for Multi-Class Brain Magnetic Resonance Imaging Tumor Classification and Localization Using Gradient-Weighted Class …
Brain tumors (BT) present a considerable global health concern because of their high
mortality rates across diverse age groups. A delay in diagnosing BT can lead to death …
mortality rates across diverse age groups. A delay in diagnosing BT can lead to death …
RU-Net2+: A deep learning algorithm for accurate brain tumor segmentation and survival rate prediction
R Zaitoon, H Syed - IEEE Access, 2023 - ieeexplore.ieee.org
Brain tumors present a significant medical concern, posing challenges in both diagnosis and
treatment. Deep learning has emerged as an evolving technique for automating the …
treatment. Deep learning has emerged as an evolving technique for automating the …