Combining CNN features with voting classifiers for optimizing performance of brain tumor classification
Simple Summary This study presents a hybrid model for brain tumor detection. Contrary to
manual featur extraction, features extracted from a convolutional neural network are used to …
manual featur extraction, features extracted from a convolutional neural network are used to …
Improving brain tumor classification: an approach integrating pre-trained CNN models and machine learning algorithms
Accurate detection of brain tumors is crucial for enhancing patient outcomes, yet the
interpretation of Magnetic Resonance Imaging (MRI) scans poses significant challenges …
interpretation of Magnetic Resonance Imaging (MRI) scans poses significant challenges …
Enhancing prediction of brain tumor classification using images and numerical data features
Brain tumors, along with other diseases that harm the neurological system, are a significant
contributor to global mortality. Early diagnosis plays a crucial role in effectively treating brain …
contributor to global mortality. Early diagnosis plays a crucial role in effectively treating brain …
Comparative Study on Architecture of Deep Neural Networks for Segmentation of Brain Tumor using Magnetic Resonance Images
The state-of-the-art works for the segmentation of brain tumor using the images acquired by
Magnetic Resonance Imaging (MRI) with their performances are analyzed in this …
Magnetic Resonance Imaging (MRI) with their performances are analyzed in this …
[PDF][PDF] Semantic Segmentation and YOLO Detector over Aerial Vehicle Images.
Intelligent vehicle tracking and detection are crucial tasks in the realm of highway
management. However, vehicles come in a range of sizes, which is challenging to detect …
management. However, vehicles come in a range of sizes, which is challenging to detect …
Brain Tumor Detection Based on Deep Features Concatenation and Machine Learning Classifiers With Genetic Selection
The development of brain tumors is often a result of cellular abnormalities, making it a
leading factor contributing to mortality among both adults and children on a global scale …
leading factor contributing to mortality among both adults and children on a global scale …
Efficient Skip Connections-Based Residual Network (ESRNet) for Brain Tumor Classification
Brain tumors pose a complex and urgent challenge in medical diagnostics, requiring precise
and timely classification due to their diverse characteristics and potentially life-threatening …
and timely classification due to their diverse characteristics and potentially life-threatening …
[HTML][HTML] Enhancing Cancerous Gene Selection and Classification for High-Dimensional Microarray Data Using a Novel Hybrid Filter and Differential Evolutionary …
Background: In recent years, microarray datasets have been used to store information about
human genes and methods used to express the genes in order to successfully diagnose …
human genes and methods used to express the genes in order to successfully diagnose …
Segmentation and classification of brain tumour using LRIFCM and LSTM
KS Neetha, DL Narayan - Multimedia Tools and Applications, 2024 - Springer
Brain tumour is an abnormal growth of cells in the brain, and is a harmful and life-
threatening disease worldwide. The rapid development of tumour cells increases the illness …
threatening disease worldwide. The rapid development of tumour cells increases the illness …
MultiModNet: An automated multimodal network for brain tumor volume determination and grading with three-dimensional u-net and deformable voxel fusion
T Jeslin, T Thanya - Biomedical Signal Processing and Control, 2025 - Elsevier
Abstract Magnetic Resonance Imaging (MRI) is essential for non-inasive brain tumor
detection, but accurately grading tumors is difficult due to variability in tumor types, sizes …
detection, but accurately grading tumors is difficult due to variability in tumor types, sizes …