Jul 2026· Journal of Innovative Image Processing· Vol 8, pp. 1266-1289· 0 citations· 25 references
TL;DR
A threefold DL model, called Channel-Spatial Context Gating ResNet (CSCG-ResNet), called Channel-Spatial Context Gating ResNet (CSCG-ResNet), is proposed for efficient feature extraction, and a modified Mountain Gazelle Optimizer (MGO) is proposed for feature optimization.
Abstract
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.
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.
Muhammad Faqih, O. Q. Aziz, Ajib Hanani· Jurnal Ilmu Komputer dan Inf...· 0 citations
Due to its late identification and challenging diagnosis, lung cancer continues to be one of the top causes of death for cancer patients globally, positioning it as one of the most critical concerns. Timely identification of cancerous nodules is essential for enhancing the patient’s survival likelihood CT image analysis by hand is not very productive and significantly relies on a specialist’s expertise. In this study, we offer an autonomous lung cancer classification method based on explainable deep learning. The popular DenseNet121 network serves as the foundation for our deep learning model, which is enhanced by the Convolutional Block Attention Module (CBAM). To improve feature extraction of significant spatial and channel properties of input data, attention techniques are added. Furthermore, our method is interpretable because the Grad-CAM technique makes it possible to explain the choices made by a machine learning system. A database of CT scans, comprising 4,598 pictures categorized by large cell carcinoma, adenocarcinoma, and healthy lungs, was utilized. Our evaluations show the model’s effectiveness with an accuracy rate of 94.6\%.
S. Jegadeesan, S. Matheswaran, R. Palanivelrajan· International Conference on...· 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
The improved YOLOv5s-based deep learning model performed better in automated detection, and the LCSA × ASFF combination improved localization accuracy while maintaining recall through the synergy between local contrast enhancement and adaptive scale fusion.
Zhipeng Sun, Jinghui Chen, Lianxin Xie et al.· Frontiers in Medicine· 0 citations
Spinal diseases are common and widely impactful health issues in modern society. With the advancement of computer vision and medical image analysis, image-based automatic recognition and classification of spinal diseases have become research hotspots. However, existing methods often show limited performance in recognizing spinal diseases from complex or low-quality X-ray images. Their performance is easily affected by noise and exposure variations, leading to the extraction of pseudo-features unrelated to the disease. In addition, discrepancies among data sources and imaging conditions result in poor model generalization, making it difficult to adapt to cross-domain variations in clinical applications. To address these challenges, this study proposes an Information Bottleneck-based Optimal Transport Network (IBOTSpine) for automated diagnosis of spinal diseases. The IBOTSpine model introduces an information bottleneck-constrained feature extraction module that effectively captures disease-relevant structural information while suppressing irrelevant noise. Moreover, by incorporating an optimal transport mechanism, the model learns domain-invariant features, thereby reducing the distribution discrepancy between training and testing data and enhancing robustness and generalization across multi-source datasets. Specifically, the model employs a Swin Transformer as the backbone network and jointly optimizes the information bottleneck and optimal transport losses to achieve synergistic improvement in feature extraction, domain adaptation, and classification performance. Experimental results on real spinal X-ray dataset demonstrate that the proposed model outperforms existing methods in classification accuracy, generalization capability, and feature discriminability, validating its effectiveness and application potential in intelligent spinal image diagnosis.
Minghao Shao, Haocheng Xu, Linli Li et al.· npj Digital Medicine· 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
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