Jul 2026· Jurnal Ilmu Komputer dan Informasi· 0 citations· 20 references
TL;DR
The proposed framework establishes a reliable and lightweight baseline for automated gastrointestinal disease detection and demonstrates that ConvNeXt-Tiny effectively captures disease-relevant visual patterns in endoscopic images while maintaining consistent performance across varying training conditions.
Abstract
Early and accurate detection of gastrointestinal abnormalities, such as gastroesophageal reflux disease (GERD) and intestinal polyps, is essential for preventing severe clinical complications. However, manual interpretation of endoscopic images is often constrained by inter-observer variability and time limitations. This study proposes a ConvNeXt-Tiny-based deep learning framework for multi-class classification of gastrointestinal endoscopic images. Experiments were conducted using the GastroEndoNet v3 dataset, which contains 4,006 images categorized into four classes: GERD, GERD Normal, Polyp, and Polyp Normal. A total of twelve experimental scenarios were designed to systematically evaluate the effects of dataset-provided augmentation, ImageNet-based normalization, and batch size on model performance. The optimal configuration, combining augmentation, normalization, and a batch size of 64, achieved a test accuracy of 99.75% and a macro-averaged F1- score of 0.9977, indicating stable convergence and strong generalization on unseen data. The results demonstrate that ConvNeXt-Tiny effectively captures disease-relevant visual patterns in endoscopic images while maintaining consistent performance across varying training conditions. Comparative evaluation with a transformer-based baseline further indicates that modern convolutional architectures remain competitive for gastrointestinal image classification tasks. The proposed framework establishes a reliable and lightweight baseline for automated gastrointestinal disease detection. Extensions to video-based endoscopy would require incorporating temporal information across consecutive frames, which is beyond the scope of the current image-based study.
This system is recommended as an initial endoscopic image classification aid, with further development involving a random-image class and an examination history database.
Gunawan, Muhtar, Lili Ruhyana et al.· Jurnal Teknologi Informatika...· 0 citations
The results suggest that running both models in parallel on the same input image could provide simultaneous classification and segmentation outputs, offering more comprehensive diagnostic information compared to single-task approaches.
Rahman Ardi Saputra, Suhendro Yusuf Irianto, Egi Safitri· Jurnal Nasional Pendidikan T...· 0 citations
An attention enhanced deep learning framework using ConvNeXt V2 for robust multi-class classification of colonoscopic images that demonstrates the effectiveness of modern convolutional architectures with embedded attention mechanisms in improving diagnostic performance in the analysis of colonoscopic images.
Xiaosheng Jin, Lu-Xi Chen, Liwei Xue et al.· Frontiers in Oncology· 0 citations
Background: Deep-learning systems for endoscopic image classification are commonly evaluated with random data splits, which may overestimate performance under acquisition shift; uncertainty-aware selective prediction may improve reliability by allowing a model to abstain on uncertain cases. Methods: We evaluated binary abnormality detection in two endoscopic imaging domains: laryngeal contact-endoscopy narrow-band imaging (CE-NBI; 210 patients, patient-level) and gastrointestinal endoscopy (HyperKvasir; 6746 images, image-level, predominantly white-light). ImageNet-pretrained ResNet-50 deep ensembles were assessed under a resolution-defined acquisition-shift stress test; for the gastrointestinal data, a random stratified split was additionally used as an in-distribution reference. We evaluated discrimination, calibration, decision-curve analysis, and entropy-based selective prediction. Results: In the gastrointestinal dataset, random-split evaluation produced high performance (AUROC 0.994, 95% CI 0.990–0.996; AUPRC 0.991). Under resolution shift on the same data, performance fell to AUROC 0.723 (95% CI 0.710–0.736; AUPRC 0.690; sensitivity 0.417; specificity 0.874); the two intervals do not overlap. Selective prediction improved reliability among retained cases: under resolution shift, accuracy rose from 0.683 at full coverage to 0.852 (95% CI 0.835–0.871) at 25% coverage (balanced accuracy 0.646 → 0.798). Predictive entropy was significantly higher for incorrect than for correct predictions in both regimes (Mann–Whitney p = 7.1 × 10−77 with rank-biserial |r| = 0.30 under shift). In the laryngeal cohort, no statistically significant differences were detected among four architectures (ROC-AUC 0.844–0.901; all pairwise DeLong p > 0.05). Conclusions: Random-split evaluation substantially overestimated performance relative to a resolution-defined acquisition-shift stress test, and entropy-based selective prediction improved reliability by identifying a high-confidence subset for automated prediction while deferring the remainder to human review. Target-domain recalibration substantially restores calibration under shift (ECE 0.172 → 0.036 with temperature scaling; → 0.017 with isotonic regression) but does not recover discrimination; selective prediction is complementary, mitigating residual confident-wrong predictions. An encoder-transfer experiment showed asymmetric cross-domain utility; features learned on the larger gastrointestinal cohort transferred to the laryngeal cohort (AUROC 0.80 vs. in-domain 0.89), whereas the reverse direction did not transfer (0.53 vs. 0.72). Prospective multi-center validation remains required before clinical deployment. All code, fold definitions, random seeds, and a reproducible protocol are publicly released.
Behnam Kiani Kalejahi, S. Khan, Murodbek Akhrorov et al.· Biomedicines· 0 citations
Gastrointestinal (GI) tract diseases occur due to abnormalities affecting different regions of the digestive system. Diagnosing GI tract diseases typically involves methods such as endoscopy, imaging examinations, biopsy analysis, and clinical assessments. The severity and stage of the disease play a crucial role in determining the appropriate treatment plan. Medical imaging techniques are used to identify abnormal regions and assess the progression of diseases. The categorization of GI tract diseases, including ulcerative lesions, polyps, bleeding regions, inflammatory conditions, and normal tissues, is crucial for accurate diagnosis and effective treatment planning. Recently, Artificial Intelligence (AI)-based Deep Learning (DL) models have received greater attention due to their accuracy and flexibility. In this work, a threefold DL model is suggested to classify GI tract disease types. An improved ResNet, called Channel-Spatial Context Gating ResNet (CSCG-ResNet), is proposed for efficient feature extraction. Then, a modified Mountain Gazelle Optimizer (MGO) is proposed for feature optimization. Finally, the Quantum-inspired TabNet is proposed for multi-class categorization. The performance of the model is validated using the Kvasir dataset. The model achieves an overall accuracy of 96.75% when compared to previously proposed models.
Harshitha E.S., N. G., S. B et al.· Journal of Innovative Image...· 0 citations
This proposed model uniquely combines- Local convolutional features via ResNet-50, Global contextual features via Vision Transformer, and Handcrafted clinical texture descriptors (GLCM + LBP) and addresses the core limitations of single-architecture models that tend to either underfit local texture patterns or miss long-range spatial dependencies.
S. Dhole, C. More, Anuradha S. Nigade et al.· International Journal of Ele...· 0 citations