[HTML][HTML] The Effectiveness of Semi-Supervised Learning Techniques in Identifying Calcifications in X-ray Mammography and the Impact of Different Classification …

M Sakaida, T Yoshimura, M Tang, S Ichikawa… - Applied Sciences, 2024 - mdpi.com
Identifying calcifications in mammograms is crucial for early breast cancer detection, and
semi-supervised learning, which utilizes a small dataset for supervised learning combined …

Advanced AI-driven approach for enhanced brain tumor detection from MRI images utilizing EfficientNetB2 with equalization and homomorphic filtering

AMJ Zubair Rahman, M Gupta, S Aarathi… - BMC Medical Informatics …, 2024 - Springer
Brain tumors pose a significant medical challenge necessitating precise detection and
diagnosis, especially in Magnetic resonance imaging (MRI). Current methodologies reliant …

Enhancing brain tumor detection in MRI images through explainable AI using Grad-CAM with Resnet 50

M T. R, VK V, S Guluwadi - BMC Medical Imaging, 2024 - Springer
This study addresses the critical challenge of detecting brain tumors using MRI images, a
pivotal task in medical diagnostics that demands high accuracy and interpretability. While …

Optimizing Deep Learning Based Approach for Brain Tumor Segmentation in Magnetic Resonance Imaging (MRI) Scans

MDALM Hassan, MFH Fahim, R Jha… - 2024 IEEE AITU …, 2024 - ieeexplore.ieee.org
Several medical imaging domains, including identification, segmentation, classification, and
registration of imaging data, employ deep learning techniques. Brain tumors are one of the …

An Introductory Implementation of Breast Cancer Detection from Mammograms and Pixel Intensity with Efficient-Net Other Neural Nets

S Banerjee, H Kabir - bioRxiv, 2024 - biorxiv.org
In the world of civilized medical scientific progression, cancer has become a very serious
threat for the natural survival of human beings where breast cancer stays to be the second …