GastroNet: A robust attention‐based deep learning and cosine similarity feature selection framework for gastrointestinal disease classification from endoscopic images

CAAI Transactions on Intelligence Technology, Год журнала: 2023, Номер unknown

Опубликована: Июнь 11, 2023

Abstract Diseases of the Gastrointestinal (GI) tract significantly affect quality human life and have a high fatality rate. Accurate diagnosis GI diseases plays pivotal role in healthcare systems. However, processing large amounts medical image data can be challenging for radiologists other professionals, increasing risk inaccurate assessments. Computer‐aided Diagnosis systems provide help to doctors rapid accurate diagnosis, thus resulting saving lives. Recently, many techniques are found literature that uses deep Convolutional Neural Network (CNN) models disease classification. they limitations their ability detect deformation‐invariant features lack robustness. The diseased region is highlighted, using attention‐based generation superimposition with original images. A lightweight CNN model employed get significant features. These further reduced Cosine similarity‐based technique. proposed framework assessed Kvasir dataset. To verify effectiveness framework, vast experiments conducted. overall accuracy 97.68%, 99.02% precision, 96.37% recall, an F‐measure 97.68% achieved 810 This reduction resulted classification time. robustness observed not only terms considerable improvement accuracy, but also precision as well F‐measure.

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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Interpretable deep learning architecture for gastrointestinal disease detection: A Tri-stage approach with PCA and XAI

Computers in Biology and Medicine, Год журнала: 2024, Номер 185, С. 109503 - 109503

Опубликована: Дек. 7, 2024

Язык: Английский

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GastroVRG: Enhancing early screening in gastrointestinal health via advanced transfer features

Intelligent Systems with Applications, Год журнала: 2024, Номер 23, С. 200399 - 200399

Опубликована: Июнь 20, 2024

The accurate classification of endoscopic images is a challenging yet critical task in medical diagnostics, which directly affects the treatment and management Gastrointestinal diseases. Misclassification can lead to incorrect plans, adversely affecting patient outcomes. To address this challenge, our research aimed develop reliable computational model improve accuracy classifying conditions esophagitis polyps. We focused on subset Kvasir v1 secondary dataset, comprising 2000 evenly distributed across two classes: polyp. goal was leverage strengths both Machine Learning(ML) Deep Learning(DL) create that not only predicts with high but also integrates seamlessly into clinical workflows. end, we introduced novel VRG-based ensemble image feature extraction technique, combining powers VGG, RF, GB models synthesize robust set conducive high-precision classification. approach demonstrated best-in-class performance achieving an outstanding 99.73% detecting practical implications these results are substantial, indicating method significantly diagnostic real-world settings, reduce rate misdiagnosis, contribute efficient effective patients, ultimately enhancing quality healthcare services. With successful application proposed controlled future work involves deploying environments expanding its broader spectrum multi-class datasets.

Язык: Английский

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A Convolutional Neural Network with Meta-feature Learning for Wireless Capsule Endoscopy Image Classification

Journal of Medical and Biological Engineering, Год журнала: 2023, Номер 43(4), С. 475 - 494

Опубликована: Авг. 1, 2023

Язык: Английский

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Multi-feature fusion and dandelion optimizer based model for automatically diagnosing the gastrointestinal diseases

PeerJ Computer Science, Год журнала: 2024, Номер 10, С. e1919 - e1919

Опубликована: Фев. 28, 2024

It is a known fact that gastrointestinal diseases are extremely common among the public. The most of these gastritis, reflux, and dyspepsia. Since symptoms similar, diagnosis can often be confused. Therefore, it great importance to make diagnoses faster more accurate by using computer-aided systems. in this article, new artificial intelligence-based hybrid method was developed classify images with high accuracy anatomical landmarks cause diseases, pathological findings polyps removed during endoscopy, which usually cancer. In proposed method, firstly trained InceptionV3 MobileNetV2 architectures used feature extraction performed two architectures. Then, features obtained from merged. Thanks merging process, different belonging same were brought together. However, contain irrelevant redundant may have negative impact on classification performance. Dandelion Optimizer (DO), one recent metaheuristic optimization algorithms, as selector select appropriate improve performance support vector machine (SVM) classifier. experimental study, also compared convolutional neural network (CNN) models found achieved better results. value model 93.88%.

Язык: Английский

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Efficient-gastro: optimized EfficientNet model for the detection of gastrointestinal disorders using transfer learning and wireless capsule endoscopy images

PeerJ Computer Science, Год журнала: 2024, Номер 10, С. e1902 - e1902

Опубликована: Март 11, 2024

Gastrointestinal diseases cause around two million deaths globally. Wireless capsule endoscopy is a recent advancement in medical imaging, but manual diagnosis challenging due to the large number of images generated. This has led research into computer-assisted methodologies for diagnosing these images. Endoscopy produces thousands frames each patient, making examination difficult, laborious, and error-prone. An automated approach essential speed up process, reduce costs, potentially save lives. study proposes transfer learning-based efficient deep learning methods detecting gastrointestinal disorders from multiple modalities, aiming detect with superior accuracy efforts costs experts. The Kvasir eight-class dataset was used experiment, where endoscopic were preprocessed enriched augmentation techniques. EfficientNet model optimized via fine tuning, compared most widely pre-trained models. model’s efficacy tested on another independent prove its robustness reliability.

Язык: Английский

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Explainable AI-driven model for gastrointestinal cancer classification

Frontiers in Medicine, Год журнала: 2024, Номер 11

Опубликована: Апрель 15, 2024

Although the detection procedure has been shown to be highly effective, there are several obstacles overcome in usage of AI-assisted cancer cell clinical settings. These issues stem mostly from failure identify underlying processes. Because diagnosis does not offer a clear decision-making process, doctors dubious about it. In this instance, advent Explainable Artificial Intelligence (XAI), which offers explanations for prediction models, solves AI black box issue. The SHapley Additive exPlanations (SHAP) approach, results interpretation model predictions, is main emphasis work. intermediate layer study was hybrid made up three Convolutional Neural Networks (CNNs) (InceptionV3, InceptionResNetV2, and VGG16) that combined their predictions. KvasirV2 dataset, comprises pathological symptoms associated cancer, used train model. Our yielded an accuracy 93.17% F1 score 97%. After training model, we use SHAP analyze images these groups provide explanation decision affects prediction.

Язык: Английский

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Localization and Classification of Gastrointestinal Tract Disorders Using Explainable AI from Endoscopic Images

Applied Sciences, Год журнала: 2023, Номер 13(15), С. 9031 - 9031

Опубликована: Авг. 7, 2023

Globally, gastrointestinal (GI) tract diseases are on the rise. If left untreated, people may die from these diseases. Early discovery and categorization of can reduce severity disease save lives. Automated procedures necessary, since manual detection laborious, time-consuming, prone to mistakes. In this work, we present an automated system for localization classification GI endoscopic images with help encoder–decoder-based model, XceptionNet, explainable artificial intelligence (AI). Data augmentation is performed at preprocessing stage, followed by segmentation using model. Later, contours drawn around diseased area based segmented regions. Finally, well-known classifiers, results generated various train-to-test ratios performance analysis. For segmentation, proposed model achieved 82.08% dice, 90.30% mIOU, 94.35% precision, 85.97% recall rate. The best performing classifier 98.32% accuracy, 96.13% recall, 99.68% precision softmax classifier. Comparison state-of-the-art techniques shows that well all reported metrics. We explain improvement in utilizing heat maps without technique.

Язык: Английский

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Multi-classification deep learning models for detection of ulcerative colitis, polyps, and dyed-lifted polyps using wireless capsule endoscopy images

Complex & Intelligent Systems, Год журнала: 2023, Номер 10(2), С. 2477 - 2497

Опубликована: Ноя. 24, 2023

Abstract Wireless capsule endoscopy (WCE) enables imaging and diagnostics of the gastrointestinal (GI) tract to be performed without any discomfort. Despite this, several characteristics, including efficacy, tolerance, safety, performance, make it difficult apply modify widely. The use automated WCE collect data perform analysis is essential for finding anomalies. Medical specialists need a significant amount time expertise examine generated by patient’s digestive tract. To address these challenges, computer vision-based solutions have been designed; nevertheless, they do not achieve an acceptable level accuracy, more advancements are required. Thus, in this study, we proposed four multi-classification deep learning (DL) models i.e., Vgg-19 + CNN, ResNet152V2, Gated Recurrent Unit (GRU) ResNet152V2 Bidirectional GRU (Bi-GRU) applied on different publicly available databases diagnosing ulcerative colitis, polyps, dyed-lifted polyps using images. our knowledge, only study that uses single DL model classification three GI diseases. We compared performance classifiers terms many parameters such as loss, Matthew's correlation coefficient (MCC), recall, precision, negative predictive value (NPV), positive (PPV), F1-score. results revealed CNN outperforms other classifying diseases achieved accuracy 99.45%. also with recent state-of-the-art has better improved accuracy.

Язык: Английский

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