ArticleDiagnostics (Basel, Switzerland)2021
Multi-Input Dual-Stream Capsule Network for Improved Lung and Colon Cancer Classification.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed.
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- Efficient deep learning model for classifying lung cancer images using normalized stain agnostic feature method and FastAI-2.PeerJ. Computer science · 2025Article
- Lung and Colon Cancer Detection Using a Deep AI Model.Cancers · 2024Article
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- Predictive analytics of complex healthcare systems using deep learning based disease diagnosis model.Scientific reports · 2024Article
- Secure and Transparent Lung and Colon Cancer Classification Using Blockchain and Microsoft Azure.Advances in respiratory medicine · 2024Article
- Advanced Deep Learning Fusion Model for Early Multi-Classification of Lung and Colon Cancer Using Histopathological Images.Diagnostics (Basel, Switzerland) · 2024Article
- Cardiac Fibrosis Automated Diagnosis Based on FibrosisNet Network Using CMR Ischemic Cardiomyopathy.Diagnostics (Basel, Switzerland) · 2024Article
- Automated Lung and Colon Cancer Classification Using Histopathological Images.Biomedical engineering and computational biology · 2024Article
- A Novel Heteromorphous Convolutional Neural Network for Automated Assessment of Tumors in Colon and Lung Histopathology Images.Biomimetics (Basel, Switzerland) · 2023Article
- VGG16 Feature Extractor with Extreme Gradient Boost Classifier for Pancreas Cancer Prediction.Journal of imaging · 2023Article
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- An Explainable Classification Method Based on Complex Scaling in Histopathology Images for Lung and Colon Cancer.Diagnostics (Basel, Switzerland) · 2023Article
- Histopathological Analysis for Detecting Lung and Colon Cancer Malignancies Using Hybrid Systems with Fused Features.Bioengineering (Basel, Switzerland) · 2023Article
- Leveraging Marine Predators Algorithm with Deep Learning for Lung and Colon Cancer Diagnosis.Cancers · 2023Article
- Robustness Fine-Tuning Deep Learning Model for Cancers Diagnosis Based on Histopathology Image Analysis.Diagnostics (Basel, Switzerland) · 2023Article
- Neurodegenerative diseases-Caps: a capsule network based early screening system for the classification of neurodegenerative diseases.Cognitive neurodynamics · 2022Article
- A Framework for Lung and Colon Cancer Diagnosis via Lightweight Deep Learning Models and Transformation Methods.Diagnostics (Basel, Switzerland) · 2022Article
- A Hybrid 2D Gaussian Filter and Deep Learning Approach with Visualization of Class Activation for Automatic Lung and Colon Cancer Diagnosis.Technology in cancer research & treatmentArticle
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2 authors.
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Abstract
Lung and colon cancers are two of the most common causes of death and morbidity in humans. One of the most important aspects of appropriate treatment is the histopathological diagnosis of such cancers. As a result, the main goal of this study is to use a multi-input capsule network and digital histopathology images to build an enhanced computerized diagnosis system for detecting squamous cell carcinomas and adenocarcinomas of the lungs, as well as adenocarcinomas of the colon. Two convolutional layer blocks are used in the proposed multi-input capsule network. The CLB (Convolutional Layers Block) employs traditional convolutional layers, whereas the SCLB (Separable Convolutional Layers Block) employs separable convolutional layers. The CLB block takes unprocessed histopathology images as input, whereas the SCLB block takes uniquely pre-processed histopathological images. The pre-processing method uses color balancing, gamma correction, image sharpening, and multi-scale fusion as the major processes because histopathology slide images are typically red blue. All three channels (Red, Green, and Blue) are adequately compensated during the color balancing phase. The dual-input technique aids the model's ability to learn features more effectively. On the benchmark LC25000 dataset, the empirical analysis indicates a significant improvement in classification results. The proposed model provides cutting-edge performance in all classes, with 99.58% overall accuracy for lung and colon abnormalities based on histopathological images.
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