Efficient Gastrointestinal Disease Classification Using Pretrained Deep Convolutional Neural Network

Electronics, Год журнала: 2023, Номер 12(7), С. 1557 - 1557

Опубликована: Март 26, 2023

Gastrointestinal (GI) tract diseases are on the rise in world. These can have fatal consequences if not diagnosed initial stages. WCE (wireless capsule endoscopy) is advanced technology used to inspect gastrointestinal such as ulcerative-colitis, polyps, esophagitis, and ulcers. produces thousands of frames for a single patient’s procedure which manual examination tiresome, time-consuming, prone error; therefore, an automated needed. images suffer from low contrast increases inter-class intra-class similarity reduces anticipated performance. In this paper, efficient GI disease classification technique proposed utilizes optimized brightness-controlled contrast-enhancement method improve images. The applies genetic algorithm (GA) adjusting values brightness within image by modifying fitness function, improves overall quality This improvement reported using qualitative measures, peak signal noise ratio (PSNR), mean square error (MSE), visual information fidelity (VIF), index (SI), (IQI). As second step, data augmentation performed applying multiple transformations, then, transfer learning fine-tune modified pre-trained model Finally, disease, extracted features passed through machine-learning classifiers. To show efficacy performance, results original dataset well contrast-enhanced dataset. 15.26% accuracy, 13.3% precision, 16.77% recall rate, 15.18% F-measure. comparison with existing techniques shows that framework outperforms state-of-the-art techniques.

Язык: Английский

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Detection of various gastrointestinal tract diseases through a deep learning method with ensemble ELM and explainable AI

Expert Systems with Applications, Год журнала: 2024, Номер 256, С. 124908 - 124908

Опубликована: Июль 30, 2024

The rising prevalence of gastrointestinal (GI) tract disorders worldwide highlights the urgent need for precise diagnosis, as these diseases greatly affect human life and contribute to high mortality rates. Fast identification, accurate classification, efficient treatment approaches are essential addressing this critical health issue. Common side effects include abdominal pain, bloating, discomfort, which can be chronic debilitating. Nausea vomiting also frequent, leading difficulties in maintaining adequate nutrition hydration. current study intends develop a deep learning (DL)-based approach that automatically classifies GI diseases. For first time, GastroVision dataset with 8000 images 27 different was utilized work design computer-aided diagnosis (CAD) system. This presents novel lightweight feature extractor compact size minimum number layers named Parallel Depthwise Separable Convolutional Neural Network (PD-CNN) Pearson Correlation Coefficient (PCC) selector. Furthermore, robust classifier Ensemble Extreme Learning Machine (EELM), combined pseudo inverse ELM (ELM) L1 Regularized (RELM), has been proposed identify more precisely. A hybrid preprocessing technique, including scaling, normalization, image enhancement techniques such erosion, CLAHE, sharpening, Gaussian filtering, employed enhance representation improve classification performance. consists twenty-four only 0.815 million parameters 9.79 MB model size. PD-CNN-PCC-EELM extracts features, reduces computational overhead, achieves excellent performance on multiclass images. achieved highest precision, recall, f1, accuracy, ROC-AUC, AUC-PR values 88.12 ± 0.332 %, 87.75 0.348 87.12 0.324 98.89 92 respectively, while testing time 0.000001 s. comparative utilizes 10-fold cross-validation, ablation various state-of-the-art (SOTA) transfer (TL) models extractors. Then, PCC EELM integrated TL generate predictions, notably terms real-time processing capability; significantly outperforms other models. Moreover, explainable AI (XAI) methods, SHAP (Shapley Additive Explanations), heatmap, guided Grad-Cam (Gradient-weighted Class Activation Mapping), Grad-CAM, Saliency mapping, have explore interpretability decision-making capability model. Therefore, provides practical intelligence increasing confidence diagnosing real-world scenarios.

Язык: Английский

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Machine learning based small bowel video capsule endoscopy analysis: Challenges and opportunities

Future Generation Computer Systems, Год журнала: 2023, Номер 143, С. 191 - 214

Опубликована: Янв. 18, 2023

Video capsule endoscopy (VCE) is a revolutionary technology for the early diagnosis of gastric disorders. However, owing to high redundancy and subtle manifestation anomalies among thousands frames, manual construal VCE videos requires considerable patience, focus, time. The automatic analysis these using computational methods challenge as untamed in motion captures frames inaptly. Several machine learning (ML) methods, including recent deep convolutional neural networks approaches, have been adopted after evaluating their potential improving analysis. clinical impact yet be investigated. This survey aimed highlight gaps between existing ML-based research methodologies clinically significant rules recently established by gastroenterologists based on VCE. A framework interpreting raw into contextually relevant frame-level findings subsequently merging with meta-data obtain disease-level was formulated. Frame-level can more intelligible discriminative when organized taxonomical hierarchy. proposed hierarchy, which formulated pathological visual similarities, may yield better classification metrics setting inference classes at higher level than training classes. Mapping from frame disease structured form graph relevance inspired international consensus developed domain experts. Furthermore, summarization, classification, segmentation, detection, localization were critically evaluated compared aspects deemed clinicians. Numerous studies pertain single anomaly detection instead pragmatic approach setting. challenges opportunities associated delineated. focus maximizing power features corresponding various lesions help cope diverse mimicking nature different frames. Large multicenter datasets must created data sparsity, bias, class imbalance. Explainability, reliability, traceability, transparency are important an diagnostics system Existing ethical legal bindings narrow scope possibilities where ML potentially leveraged healthcare. Despite limitations, video will revolutionize practice, aiding clinicians rapid accurate diagnosis.

Язык: Английский

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Modified residual attention network for abnormalities segmentation and detection in WCE images

Soft Computing, Год журнала: 2024, Номер 28(9-10), С. 6923 - 6936

Опубликована: Янв. 14, 2024

Язык: Английский

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Video summarization via knowledge-aware multimodal deep networks

Knowledge-Based Systems, Год журнала: 2024, Номер 293, С. 111670 - 111670

Опубликована: Март 20, 2024

Язык: Английский

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Improvement of thermal comfort for underground space: Data enhancement using variational autoencoder

Building and Environment, Год журнала: 2021, Номер 207, С. 108457 - 108457

Опубликована: Окт. 22, 2021

Язык: Английский

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An interpretable deep neural network for colorectal polyp diagnosis under colonoscopy

Knowledge-Based Systems, Год журнала: 2021, Номер 234, С. 107568 - 107568

Опубликована: Окт. 6, 2021

Язык: Английский

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Multi-Temporal Granularity Concept Induction for semantically driven video summarization

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 127128 - 127128

Опубликована: Март 1, 2025

Язык: Английский

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Global–local spatio-temporal graph convolutional networks for video summarization

Computers & Electrical Engineering, Год журнала: 2024, Номер 118, С. 109445 - 109445

Опубликована: Июль 6, 2024

Язык: Английский

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A comprehensive study of automatic video summarization techniques

Artificial Intelligence Review, Год журнала: 2023, Номер 56(10), С. 11473 - 11633

Опубликована: Март 13, 2023

Язык: Английский

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