[HTML][HTML] The role of geometry in convolutional neural networks for medical imaging
Convolutional neural networks (CNNs) have played an important role in medical imaging—
from diagnostics to research to data integration. This has allowed clinicians to plan …
from diagnostics to research to data integration. This has allowed clinicians to plan …
What can machine vision do for lymphatic histopathology image analysis: a comprehensive review
Over the past 10 years, machine vision (MV) algorithms for image analysis have been
developing rapidly with computing power. At the same time, histopathological slices can be …
developing rapidly with computing power. At the same time, histopathological slices can be …
Hybrid models for classifying histological images: An association of deep features by transfer learning with ensemble classifier
The use of a convolutional neural network with transfer learning is a strategy that defines
high-level features, commonly explored to study patterns in medical images. These features …
high-level features, commonly explored to study patterns in medical images. These features …
Deep learning with multiresolution handcrafted features for brain MRI segmentation
The segmentation of magnetic resonance (MR) images is a crucial task for creating pseudo
computed tomography (CT) images which are used to achieve positron emission …
computed tomography (CT) images which are used to achieve positron emission …
[HTML][HTML] Fractal theory based identification model for surface crack of building structures
The existing building structures are mainly composed of concrete and masonry structures.
This paper proposes a Fractal Theory-based Method (FTM) designed to efficiently detect …
This paper proposes a Fractal Theory-based Method (FTM) designed to efficiently detect …
A soft-computing based approach to overlapped cells analysis in histopathology images with genetic algorithm
H Wu, KKY Pang, GKH Pang, RKH Au-Yeung - Applied Soft Computing, 2022 - Elsevier
Deep learning methods have shown great significance for medical image processing in
recent years, including radiography, magnetic resonance imaging, nuclear medicine, and …
recent years, including radiography, magnetic resonance imaging, nuclear medicine, and …
Classification of Multiple H&E Images via an Ensemble Computational Scheme
In this work, a computational scheme is proposed to identify the main combinations of
handcrafted descriptors and deep-learned features capable of classifying histological …
handcrafted descriptors and deep-learned features capable of classifying histological …
A novel convolutional neural network algorithm for histopathological lung cancer detection
N Faria, S Campelos, V Carvalho - Applied Sciences, 2023 - mdpi.com
Lung cancer is a leading cause of cancer-related deaths worldwide, and its diagnosis must
be carried out as soon as possible to increase the survival rate. The development of …
be carried out as soon as possible to increase the survival rate. The development of …
Neighbourhood component analysis and deep feature-based diagnosis model for middle ear otoscope images
Otitis media (OM), known as inflammation of the middle ear, is a condition especially seen in
children. To carry out a definitive diagnosis of the discomfort that manifests itself with various …
children. To carry out a definitive diagnosis of the discomfort that manifests itself with various …
An efficient galactic swarm optimization based fractal neural network model with dwt for malignant melanoma prediction
SP Karuppiah, A Sheeba, S Padmakala… - Neural Processing …, 2022 - Springer
The most aggressive and malignant type of skin cancer is melanoma. When input data is in
the form of images, image processing plays a vital role to detect and classifying cancer in the …
the form of images, image processing plays a vital role to detect and classifying cancer in the …