Radiomics in breast cancer: Current advances and future directions
Breast cancer is a common disease that causes great health concerns to women worldwide.
During the diagnosis and treatment of breast cancer, medical imaging plays an essential …
During the diagnosis and treatment of breast cancer, medical imaging plays an essential …
Visualization and classification of mushroom species with multi-feature fusion of metaheuristics-based convolutional neural network model
Determining the correct mushroom species with the necessary ecological characteristics is
critical to continue mushroom production, which is essential in gastronomy. The mushroom …
critical to continue mushroom production, which is essential in gastronomy. The mushroom …
Hybrid Parallel Fuzzy CNN Paradigm: Unmasking Intricacies for Accurate Brain MRI Insights
The Hybrid Parallel Fuzzy CNN (HP-FCNN) is a ground-breaking method for medical image
analysis that combines the interpretive capacity of fuzzy logic with the capabilities of a …
analysis that combines the interpretive capacity of fuzzy logic with the capabilities of a …
Privacy-preserving AI for early diagnosis of thoracic diseases using IoTs: A federated learning approach with multi-headed self-attention for facilitating cross …
Our study recognized the crucial role of early diagnosis of pulmonary radiological
abnormalities such as pneumothorax, effusion, pneumonia, cardiomegaly, and COVID-19 …
abnormalities such as pneumothorax, effusion, pneumonia, cardiomegaly, and COVID-19 …
[HTML][HTML] Interpretability as Approximation: Understanding Black-Box Models by Decision Boundary
H Dong, B Liu, D Ye, G Liu - Electronics, 2024 - mdpi.com
Currently, interpretability methods focus more on less objective human-understandable
semantics. To objectify and standardize interpretability research, in this study, we provide …
semantics. To objectify and standardize interpretability research, in this study, we provide …
[HTML][HTML] Using lightweight method to detect landslide from satellite imagery
Accurate, rapid, and automated landslide detection is crucial for early warning, emergency
management, and landslide mechanism analysis. Increasingly general-purpose detection …
management, and landslide mechanism analysis. Increasingly general-purpose detection …
A Novel Interpretable Graph Convolutional Neural Network for Multimodal Brain Tumor Segmentation
Deep convolutional neural networks (CNNs) have revolutionized computer vision,
demonstrating remarkable performance in various tasks. However, their end-to-end learning …
demonstrating remarkable performance in various tasks. However, their end-to-end learning …
A Hybrid Deep Learning and Machine Learning-Based Approach to Classify Defects in Hot Rolled Steel Strips for Smart Manufacturing.
T Hussain, J Hong, J Seok - Computers, Materials & …, 2024 - search.ebscohost.com
Smart manufacturing is a process that optimizes factory performance and production quality
by utilizing various technologies including the Internet of Things (IoT) and artificial …
by utilizing various technologies including the Internet of Things (IoT) and artificial …
Illuminating the black box: An interpretable machine learning based on ensemble trees
YS Lee, SJ Yen, W Jiang, J Chen, CY Chang - Expert Systems with …, 2025 - Elsevier
Deep learning has achieved significant success in the analysis of unstructured data, but its
inherent black-box nature has led to numerous limitations in security-sensitive domains …
inherent black-box nature has led to numerous limitations in security-sensitive domains …
[PDF][PDF] Development of a chest X-ray machine learning convolutional neural network model on a budget and using artificial intelligence explainability techniques to …
SB Lee - JAMIA open, 2024 - academic.oup.com
Objective Machine learning (ML) will have a large impact on medicine and accessibility is
important. This study's model was used to explore various concepts including how varying …
important. This study's model was used to explore various concepts including how varying …