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

Computers in Biology and Medicine, Journal Year: 2022, Volume and Issue: 143, P. 105265 - 105265

Published: Jan. 31, 2022

Language: Английский

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Automated detection and classification of leukemia on a subject-independent test dataset using deep transfer learning supported by Grad-CAM visualization

Biomedical Signal Processing and Control, Journal Year: 2023, Volume and Issue: 83, P. 104722 - 104722

Published: Feb. 22, 2023

Language: Английский

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An Efficient Deep Learning Approach for Colon Cancer Detection

Applied Sciences, Journal Year: 2022, Volume and Issue: 12(17), P. 8450 - 8450

Published: Aug. 24, 2022

Colon cancer is the second most common cause of death in women and third men. Therefore, early detection this can lead to lower infection rates. In research, we propose a new lightweight deep learning approach based on Convolutional Neural Network (CNN) for efficient colon detection. our method, input histopathological images are normalized before feeding them into CNN model, then performed. The efficiency proposed system analyzed with publicly available database compared state-of-the-art existing methods result analysis demonstrates that model provides higher accuracy 99.50%, which considered best majority other approaches. Because high result, computationally efficient.

Language: Английский

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CRCCN-Net: Automated framework for classification of colorectal tissue using histopathological images

Biomedical Signal Processing and Control, Journal Year: 2022, Volume and Issue: 79, P. 104172 - 104172

Published: Sept. 28, 2022

Language: Английский

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A convolution neural network with multi-level convolutional and attention learning for classification of cancer grades and tissue structures in colon histopathological images

Computers in Biology and Medicine, Journal Year: 2022, Volume and Issue: 147, P. 105680 - 105680

Published: June 2, 2022

Language: Английский

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Improving machine learning recognition of colorectal cancer using 3D GLCM applied to different color spaces

Multimedia Tools and Applications, Journal Year: 2022, Volume and Issue: 81(8), P. 10839 - 10860

Published: Feb. 16, 2022

Language: Английский

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CAD systems for colorectal cancer from WSI are still not ready for clinical acceptance

Scientific Reports, Journal Year: 2021, Volume and Issue: 11(1)

Published: July 13, 2021

Most oncological cases can be detected by imaging techniques, but diagnosis is based on pathological assessment of tissue samples. In recent years, the pathology field has evolved to a digital era where samples are digitised and evaluated screen. As result, opened up many research opportunities, allowing development more advanced image processing as well artificial intelligence (AI) methodologies. Nevertheless, despite colorectal cancer (CRC) being second deadliest type worldwide, with increasing incidence rates, application AI for CRC diagnosis, particularly whole-slide images (WSI), still young field. this review, we analyse some relevant works published particular task highlight limitations that hinder these in clinical practice. We also empirically investigate feasibility using weakly annotated datasets support computer-aided systems from WSI. Our study underscores need large use an appropriate learning methodology gain most benefit partially datasets. The WSI dataset used study, containing 1,133 biopsy polypectomy samples, available upon reasonable request.

Language: Английский

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Ensemble of adapted convolutional neural networks (CNN) methods for classifying colon histopathological images

PeerJ Computer Science, Journal Year: 2022, Volume and Issue: 8, P. e1031 - e1031

Published: July 5, 2022

Deep convolutional neural networks (CNN) manifest the potential for computer-aided diagnosis systems (CADs) by learning features directly from images rather than using traditional feature extraction methods. Nevertheless, due to limited sample sizes and heterogeneity in tumor presentation medical images, CNN models suffer training issues, including scratch, which leads overfitting. Alternatively, a pre-trained network's transfer (TL) is used derive knowledge image datasets that were designed non-medical activations, alleviating need large datasets. This study proposes two ensemble techniques: E-CNN (product rule) (majority voting). These techniques are based on adaptation of pretrained classify colon cancer histopathology into various classes. In these ensembles, individuals are, initially, constructed adapting DenseNet121, MobileNetV2, InceptionV3, VGG16 models. The block-wise fine-tuning policy, set dense dropout layers joined explore variation histology images. Then, models' decisions fused via product rule majority voting aggregation proposed model was validated against standard most recent works publicly available benchmark histopathological datasets: Stoean (357 images) Kather colorectal (5,000 images). results 97.20% 91.28% accurate, respectively. achieved outperformed state-of-the-art studies confirmed E-CNNs could be extended applications.

Language: Английский

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Tunicate swarm algorithm with deep convolutional neural network-driven colorectal cancer classification from histopathological imaging data

Electronic Research Archive, Journal Year: 2023, Volume and Issue: 31(5), P. 2793 - 2812

Published: Jan. 1, 2023

<abstract> <p>Colorectal cancer (CRC) is one of the most popular cancers among both men and women, with increasing incidence. The enhanced analytical load data from pathology laboratory, integrated described intra- inter-variabilities through calculation biomarkers, has prompted quest for robust machine-based approaches in combination routine practice. In histopathology, deep learning (DL) techniques have been applied at large due to their potential supporting analysis forecasting medically appropriate molecular phenotypes microsatellite instability. Considering this background, current research work presents a metaheuristics technique convolutional neural network-based colorectal classification based on histopathological imaging (MDCNN-C3HI). presented MDCNN-C3HI majorly examines images (CRC). At initial stage, applies bilateral filtering approach get rid noise. Then, proposed uses an capsule network Adam optimizer extraction feature vectors. For CRC classification, DL modified classifier, whereas tunicate swarm algorithm used fine-tune its hyperparameters. To demonstrate performance wide range experiments was conducted. outcomes extensive experimentation procedure confirmed superior over other existing techniques, achieving maximum accuracy 99.45%, sensitivity 99.45% specificity 99.45%.</p> </abstract>

Language: Английский

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A Multimodal Transfer Learning Approach Using PubMedCLIP for Medical Image Classification

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 75496 - 75507

Published: Jan. 1, 2024

Language: Английский

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