Classification of Colorectal Cancer Histology Images Using Image Reconstruction and Modified DenseNet

Sarkar, Tanmoy; Hazra, Animesh; Das, Nibaran

doi:10.1007/978-3-030-75529-4_20

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…In [22], a 92.083% accuracy is obtained by evaluating 108 different combinations of features and classifiers on the CRC-5000 dataset. In [23], an encoder unit of an autoencoder module and a modified DenseNet-121 architecture are used for the purpose approach. This approach, has an accuracy of 97.2% for the Zenodo-100K colorectal histopathology dataset.…”

Section: Classification Tasks In Colorectal Histopathology Researchmentioning

confidence: 99%

IL-MCAM: An interactive learning and multi-channel attention mechanism-based weakly supervised colorectal histopathology image classification approach

Chen,

Li,

et al. 2022

Preprint

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In recent years, colorectal cancer has become one of the most significant diseases that endanger human health. Deep learning methods are increasingly important for the classification of colorectal histopathology images. However, existing approaches focus more on end-to-end automatic classification using computers rather than human-computer interaction. In this paper, we propose an IL-MCAM framework. It is based on attention mechanisms and interactive learning. The proposed IL-MCAM framework includes two stages: automatic learning (AL) and interactivity learning (IL). In the AL stage, a multi-channel attention mechanism model containing three different attention mechanism channels and convolutional neural networks is used to extract multi-channel features for classification. In the IL stage, the proposed IL-MCAM framework continuously adds misclassified images to the training set in an interactive approach, which improves the classification ability of the MCAM model. We carried out a comparison experiment on our dataset and an extended experiment on the HE-NCT-CRC-100K dataset to verify the performance of the proposed IL-MCAM framework, achieving classification accuracies of 98.98% and 99.77%, respectively. In addition, we conducted an ablation experiment and an interchangeability experiment to verify the ability and interchangeability of the three channels. The experimental results show that the proposed IL-MCAM framework has excellent performance in the colorectal histopathological image classification tasks.

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Section: Classification Tasks In Colorectal Histopathology Researchmentioning

confidence: 99%

IL-MCAM: An interactive learning and multi-channel attention mechanism-based weakly supervised colorectal histopathology image classification approach

Chen,

Li,

et al. 2022

Preprint

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“…Women's mortality rates may drop if BC is detected early and treated appropriately [18]. Although a surgical biopsy can determine if a breast lump is malignant or benign, it is more expensive and time-consuming [35]. A breast biopsy is advised if the screening technique reveals that the patient is at risk of developing malignant tissue.…”

Section: Introductionmentioning

confidence: 99%

Classification of Breast Cancer using Ensemble Filter Feature Selection with Triplet Attention Based Efficient Net Classifier

Bangalore Nagaraj,

Shivanandamurthy Hiremath,

Matta Polnaya

2024

IAJIT

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In medical imaging, the effective detection and classification of Breast Cancer (BC) is a current research important task because of the still existing difficulty to distinguish abnormalities from normal breast tissues due to their subtle appearance and ambiguous margins and distinguish abnormalities from the normal breast. Moreover, BC detection based on an automated detection model is needed, because manual diagnosis faces problems due to cost and shortage of skilled manpower, and also takes a very long time. Using deep learning and ensemble feature selection techniques, in this paper, a novel framework is introduced for classifying BC from histopathology images. The five primary steps of the suggested framework are as follows: 1) to make the largest original dataset and then deep learning model with data augmentation to improve the learning. 2) The best features are selected by an Ensemble Filter Feature selection Method (EFFM) which combines the best feature subsets to produce the final feature subsets. 3) Then the pruned Convolution Neural Network (CNN) model is utilized to extract the optimal features. 4) Finally, the classification is done through the Triplet Attention based Efficient Network (TAENet) classifier. The suggested model produces a 98% accuracy rate after being trained and tested on two different histopathology imaging datasets including images from four different data cohorts. Subsequently, the suggested strategy outperforms the conventional ones since the ensemble filter habitually acquires the best features, and experimental results demonstrate the importance of the proposed approach

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Automated Grading of Oral Squamous Cell Carcinoma into Multiple Classes Using Deep Learning Methods

Musulin

Štifanić

Zulijani

et al. 2021

2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)

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Classification of Colorectal Cancer Histology Images Using Image Reconstruction and Modified DenseNet

Cited by 3 publications

References 16 publications

IL-MCAM: An interactive learning and multi-channel attention mechanism-based weakly supervised colorectal histopathology image classification approach

IL-MCAM: An interactive learning and multi-channel attention mechanism-based weakly supervised colorectal histopathology image classification approach

Classification of Breast Cancer using Ensemble Filter Feature Selection with Triplet Attention Based Efficient Net Classifier

Automated Grading of Oral Squamous Cell Carcinoma into Multiple Classes Using Deep Learning Methods

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