ArticlePeerJ. Computer science2021
GASTRO-CADx: a three stages framework for diagnosing gastrointestinal diseases.
Article in PeerJ. Computer science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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24 citing papers in PubMed.
- Gastrointestinal endoscopic image classification using a hybrid modified inception network and a customized vision transformer with tree growth feature selection.Scientific reports · 2026Article
- Enhanced gastrointestinal disease classification using a convvit hybrid model on endoscopic images.Physical and engineering sciences in medicine · 2025Article
- Application of deep learning-based convolutional neural networks in gastrointestinal disease endoscopic examination.World journal of gastroenterology · 2025Review
- CADxPolydetect: a clinically explainable hybrid deep learning system for multi-class colorectal lesion detection using augmented colonoscopy images.BMC medical informatics and decision making · 2025Article
- EndoNet: A Multiscale Deep Learning Framework for Multiple Gastrointestinal Disease Classification via Endoscopic Images.Diagnostics (Basel, Switzerland) · 2025Article
- Harnessing infrared thermography and multi-convolutional neural networks for early breast cancer detection.Scientific reports · 2025Article
- Hybrid deep learning framework based on EfficientViT for classification of gastrointestinal diseases.Scientific reports · 2025Article
- Colorectal cancer unmasked: A synergistic AI framework for Hyper-granular image dissection, precision segmentation, and automated diagnosis.BMC medical imaging · 2025Article
- A hybrid framework for colorectal cancer detection and U-Net segmentation using polynetDWTCADx.Scientific reports · 2025Article
- Exploring vision transformers and XGBoost as deep learning ensembles for transforming carcinoma recognition.Scientific reports · 2024Article
- Color-CADx: a deep learning approach for colorectal cancer classification through triple convolutional neural networks and discrete cosine transform.Scientific reports · 2024Article
- EnsemDeepCADx: Empowering Colorectal Cancer Diagnosis with Mixed-Dataset Features and Ensemble Fusion CNNs on Evidence-Based CKHK-22 Dataset.Bioengineering (Basel, Switzerland) · 2023Article
- RADIC:A tool for diagnosing COVID-19 from chest CT and X-ray scans using deep learning and quad-radiomics.Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society · 2023Article
- GabROP: Gabor Wavelets-Based CAD for Retinopathy of Prematurity Diagnosis via Convolutional Neural Networks.Diagnostics (Basel, Switzerland) · 2023Article
- A wavelet-based deep learning pipeline for efficient COVID-19 diagnosis via CT slices.Applied soft computing · 2022Article
- An Intelligent ECG-Based Tool for Diagnosing COVID-19 via Ensemble Deep Learning Techniques.Biosensors · 2022Article
- ECG-BiCoNet: An ECG-based pipeline for COVID-19 diagnosis using Bi-Layers of deep features integration.Computers in biology and medicine · 2022Article
- Dexterous Identification of Carcinoma through ColoRectalCADx with Dichotomous Fusion CNN and UNet Semantic Segmentation.Computational intelligence and neuroscience · 2022Article
- ColoRectalCADx: Expeditious Recognition of Colorectal Cancer with Integrated Convolutional Neural Networks and Visual Explanations Using Mixed Dataset Evidence.Computational and mathematical methods in medicine · 2022Article
- DIAROP: Automated Deep Learning-Based Diagnostic Tool for Retinopathy of Prematurity.Diagnostics (Basel, Switzerland) · 2021Article
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Abstract
Gastrointestinal (GI) diseases are common illnesses that affect the GI tract. Diagnosing these GI diseases is quite expensive, complicated, and challenging. A computer-aided diagnosis (CADx) system based on deep learning (DL) techniques could considerably lower the examination cost processes and increase the speed and quality of diagnosis. Therefore, this article proposes a CADx system called Gastro-CADx to classify several GI diseases using DL techniques. Gastro-CADx involves three progressive stages. Initially, four different CNNs are used as feature extractors to extract spatial features. Most of the related work based on DL approaches extracted spatial features only. However, in the following phase of Gastro-CADx, features extracted in the first stage are applied to the discrete wavelet transform (DWT) and the discrete cosine transform (DCT). DCT and DWT are used to extract temporal-frequency and spatial-frequency features. Additionally, a feature reduction procedure is performed in this stage. Finally, in the third stage of the Gastro-CADx, several combinations of features are fused in a concatenated manner to inspect the effect of feature combination on the output results of the CADx and select the best-fused feature set. Two datasets referred to as Dataset I and II are utilized to evaluate the performance of Gastro-CADx. Results indicated that Gastro-CADx has achieved an accuracy of 97.3% and 99.7% for Dataset I and II respectively. The results were compared with recent related works. The comparison showed that the proposed approach is capable of classifying GI diseases with higher accuracy compared to other work. Thus, it can be used to reduce medical complications, death-rates, in addition to the cost of treatment. It can also help gastroenterologists in producing more accurate diagnosis while lowering inspection time.
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