A comprehensive review of deep learning in colon cancer
Deep learning has emerged as a leading machine learning tool in object detection and has
attracted attention with its achievements in progressing medical image analysis …
attracted attention with its achievements in progressing medical image analysis …
A comprehensive review for breast histopathology image analysis using classical and deep neural networks
Breast cancer is one of the most common and deadliest cancers among women. Since
histopathological images contain sufficient phenotypic information, they play an …
histopathological images contain sufficient phenotypic information, they play an …
A convolution neural network with multi-level convolutional and attention learning for classification of cancer grades and tissue structures in colon histopathological …
A clinically comparable Convolutional Neural Network framework-based technique for
performing automated classification of cancer grades and tissue structures in hematoxylin …
performing automated classification of cancer grades and tissue structures in hematoxylin …
Fractal Neural Network: A new ensemble of fractal geometry and convolutional neural networks for the classification of histology images
Classification of histology images is a task that has been widely explored on recent
computer vision researches. The most studied approach for this task has been the …
computer vision researches. The most studied approach for this task has been the …
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 …
MTU: A multi-tasking U-net with hybrid convolutional learning and attention modules for cancer classification and gland Segmentation in Colon Histopathological …
A clinically comparable multi-tasking computerized deep U-Net-based model is
demonstrated in this paper. It intends to offer clinical gland morphometric information and …
demonstrated in this paper. It intends to offer clinical gland morphometric information and …
[HTML][HTML] A hybrid U-Net model with attention and advanced convolutional learning modules for simultaneous gland segmentation and cancer grade prediction in …
In this proposed research work, a computerized Hybrid U-Net model for supplying colon
glandular morphometric and cancer grade information is demonstrated. The solution is put …
glandular morphometric and cancer grade information is demonstrated. The solution is put …
Selection of cnn, haralick and fractal features based on evolutionary algorithms for classification of histological images
D Candelero, GF Roberto… - 2020 IEEE …, 2020 - ieeexplore.ieee.org
The analysis of histological image features for automatic detection of pathologies plays an
important role in medicine. Considering that, we proposed a method based on the …
important role in medicine. Considering that, we proposed a method based on the …
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 …
An ensemble of learned features and reshaping of fractal geometry-based descriptors for classification of histological images
Classification of histology images has been the focus of plenty researchers in computer
vision. Recently, the most common approaches for this task consist of applying deep …
vision. Recently, the most common approaches for this task consist of applying deep …