A Two-Tier Framework Based on GoogLeNet and YOLOv3 Models for Tumor Detection in MRI

Article Anatomy & Morphology

Microscopic brain tumor detection and classification using3D CNNand feature selection architecture

Amjad Rehman et al.

Summary: Brain tumors are a deadly form of cancer that has caused many deaths in recent years. This study proposes a new deep learning-based method for accurately assessing brain tumors, achieving high accuracy rates in experiments.

MICROSCOPY RESEARCH AND TECHNIQUE (2021)

添加到收藏夹

Article Neurosciences

Differential Deep Convolutional Neural Network Model for Brain Tumor Classification

Isselmou Abd El Kader et al.

Summary: The paper proposes a differential deep convolutional neural network model for classifying different types of brain tumors, including abnormal and normal magnetic resonance images. The model can analyze pixel directional patterns of images using contrast calculations and has the ability to classify a large database of images with high accuracy and without technical problems.

BRAIN SCIENCES (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

An improved framework for brain tumor analysis using MRI based on YOLOv2 and convolutional neural network

Muhammad Irfan Sharif et al.

Summary: The proposed technique utilizes a combination of methods for the detection and classification of abnormal cells in the brain, including lesion enhancement, feature extraction, localization and segmentation. By extracting optimized features and using advanced algorithms and models, successful localization, segmentation, and classification of brain tumor regions have been achieved.

COMPLEX & INTELLIGENT SYSTEMS (2021)

添加到收藏夹

Article Chemistry, Analytical

MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers

Jaeyong Kang et al.

Summary: The study proposed a method for brain tumor classification using deep features and machine learning classifiers, adopting the concept of transfer learning and pre-trained deep convolutional neural networks. Experimental results demonstrated that an ensemble of deep features can significantly improve performance, with support vector machine outperforming other classifiers on large datasets.

SENSORS (2021)

添加到收藏夹

Article Computer Science, Information Systems

Artificial Intelligence-Based Semantic Segmentation of Ocular Regions for Biometrics and Healthcare Applications

Rizwan Ali Naqvi et al.

Summary: This paper proposes a lightweight outer residual encoder-decoder network for accurate segmentation of multiple eye regions in unconstrained scenarios. The ORED-Net utilizes high-frequency information flow from the outer residual encoder-decoder deep convolutional neural network to determine the true boundaries of the eye regions from inferior-quality images. The proposed model achieves mean intersection over union scores of 89.25 and 85.12 on the challenging SBVPI and UBIRIS.v2 datasets, respectively, outperforming previous state-of-the-art models.

CMC-COMPUTERS MATERIALS & CONTINUA (2021)

添加到收藏夹

Article Medicine, Research & Experimental