IEEE

Supervised Machine Learning for Brain Tumor Progression Prediction on Limited and Imbalanced Data: An Exploratory Study

Abstract: Accurately predicting glioma progression remains a significant clinical challenge due to the disease's heterogeneity, unpredictable progression patterns, and the limitations of imaging-based ...

Developing personalized vaccines for cancer via machine learning

Yale researchers have developed a machine learning model, called Immunostruct, that can help scientists create more ...
IEEE

YOLO-DCS: A Brain Tumor Detection Algorithm Based on Dynamic Self-Attention Mechanism and Adaptive Fusion*

Abstract: To address the issues of high missed detection rates for small objects and interference from complex backgrounds in brain tumor detection, this paper proposes an improved model named ...
IEEE

CNN vs. Transformer-Based Models for Deepfake Detection: A Comparative Analysis

Abstract: As deepfake technology has rapidly progressed, it has become a concern for media authenticity, cybersecurity, and digital forensics. In this work, we compare and contrast CNNs and ...
IEEE

Accurate Detection and Classification of Brain Tumor Using Cnn and Extreme Learning Machine

Abstract: Abnormal cell growth in the brain can lead to the development of brains, with gliomas being particularly dangerous due to their low survival rates and irregular, complex boundaries. These ...
IEEE

BreNet: Attention-Enhanced Multi-Scale CNN Framework for Breast Cancer Classification in Histopathological Images

Abstract: Breast cancer remains a leading cause of mortality among women worldwide, emphasizing the critical importance of early and accurate detection in improving patient outcomes and treatment ...
IEEE

Brain Cancer MRI Classification using CNN Architectures with Grid Search Enhanced with Coordinate Ascent Optimization

Abstract: Magnetic resonance imaging (MRI) is an important tool for brain cancer diagnosis and classification. Combined with modern convolutional neural network (CNN) technology, it can effectively ...
IEEE

Deep Learning for Multi-Class DDoS Detection in SDNs: A CNN-LSTM Based Approach

Abstract: Recently Distributed Denial of Service (DDoS) attacks have increased extensively, taking about 35% of all cyber threats among which the attack characteristic rises around 300% within the ...
IEEE

Explainable Brain Tumor Classification Using Grad-CAM and a Transfer Learning Approach

Abstract: The complex structure and indistinguishable visual differences of brain tumors poses a significant challenge in medical diagnostics. This study presents a novel deep-learning based approach ...
IEEE

Multi Classification of Brain Tumor Using CNN with GRAD-CAM

Abstract: Convolutional Neural Networks (CNNs) have become the most effective neural network architecture for image classification, especially in the field of medical imaging. Brain tumor detection ...
IEEE

Classifying Daily Brain Training Habits using Machine Learning

Abstract: This study examines the application of machine learning to classify individuals based on their brain training duration, addressing the challenge of analyzing complex behavioral patterns and ...
IEEE

Explainable Brain Tumor Classification using CNN and GradCAM

Abstract: One of the most critical neurological conditions is Brain tumors, timely and correct diagnosis is needed for effective treatment. Advances in neuroimaging technology such as MRI, limitations ...