Traditional machine learning algorithms for breast cancer image classification with optimized deep features
For breast cancer diagnosis, computer-aided classification of histopathological images is of
critical importance for correct and early diagnosis. Transfer learning approaches for feature …
critical importance for correct and early diagnosis. Transfer learning approaches for feature …
Deep learning approaches for brain tumor detection and classification using mri images (2020 to 2024): A systematic review
S Bouhafra, H El Bahi - Journal of Imaging Informatics in Medicine, 2024 - Springer
Brain tumor is a type of disease caused by uncontrolled cell proliferation in the brain leading
to serious health issues such as memory loss and motor impairment. Therefore, early …
to serious health issues such as memory loss and motor impairment. Therefore, early …
Predicting biochemical oxygen demand in wastewater treatment plant using advance extreme learning machine optimized by Bat algorithm
Wastewater quality modelling plays a vital role in planning and management of wastewater
treatment plants (WWTP). This paper develops a new hybrid machine learning model based …
treatment plants (WWTP). This paper develops a new hybrid machine learning model based …
A hybrid bat and grey wolf optimizer for gene selection in cancer classification
DNA microarray is a technique in which a chip containing numerous DNA codes is used for
the expression estimation of an extensive number of genes simultaneously. These genes …
the expression estimation of an extensive number of genes simultaneously. These genes …
Deep learning-integrated MRI brain tumor analysis: feature extraction, segmentation, and Survival Prediction using Replicator and volumetric networks
The most prevalent form of malignant tumors that originate in the brain are known as
gliomas. In order to diagnose, treat, and identify risk factors, it is crucial to have precise and …
gliomas. In order to diagnose, treat, and identify risk factors, it is crucial to have precise and …
Comparative Study of Metaheuristic Optimization of Convolutional Neural Networks Applied to Face Mask Classification
The preventive measures taken to curb the spread of COVID-19 have emphasized the
importance of wearing face masks to prevent potential infection with serious diseases during …
importance of wearing face masks to prevent potential infection with serious diseases during …
Comment on–“Predictors of early and late postoperative seizures in meningioma patients: a systematic review and meta-analysis”
H Chellapandia, S Jeyachandran - Neurosurgical Review, 2024 - Springer
The article titled “Predictors of Early and Late Postoperative Seizures in Meningioma
Patients: A Systematic Review and Meta-Analysis” provides a comprehensive evaluation of …
Patients: A Systematic Review and Meta-Analysis” provides a comprehensive evaluation of …
[HTML][HTML] Towards secure IoT networks: A comprehensive study of metaheuristic algorithms in conjunction with CNN using a self-generated dataset
Abstract The Internet of Things (IoT) has radically reformed various sectors and industries,
enabling unprecedented levels of connectivity and automation. However, the surge in the …
enabling unprecedented levels of connectivity and automation. However, the surge in the …
[HTML][HTML] Optimization of CART Models Using Metaheuristics for Predicting Peach Firmness
The current advancements in the field of machine learning can have an important
application in agriculture and global food security. Machine learning has considerable …
application in agriculture and global food security. Machine learning has considerable …
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 …