Fig 9 -

Grad-CAM map value of KVASIR dataset frames (a-d) Original and (e-h) Output.</p

Classifiers and feature combination with achieved accuracy over KVASIR and NERTHUS datasets.

Classifiers and feature combination with achieved accuracy over KVASIR and NERTHUS datasets.</p

Fig 5 -

Preprocessed images (a) original images (b) Enhanced images.</p

Confusion matrix of QSVM with best outcomes with 1000 features.

Confusion matrix of QSVM with best outcomes with 1000 features.</p

The architecture of the InceptionNetV3 Model (redrawn with a new style from [60]).

The architecture of the InceptionNetV3 Model (redrawn with a new style from [60]).</p

Evaluation of the model with 200, 500, and 1000 features using the NERTHUS dataset.

Evaluation of the model with 200, 500, and 1000 features using the NERTHUS dataset.</p

Comparison with existing approaches.

Computer-aided classification of diseases of the gastrointestinal tract (GIT) has become a crucial area of research. Medical science and artificial intelligence have helped medical experts find GIT diseases through endoscopic procedures. Wired endoscopy is a controlled procedure that helps the medical expert in disease diagnosis. Manual screening of the endoscopic frames is a challenging and time taking task for medical experts that also increases the missed rate of the GIT disease. An early diagnosis of GIT disease can save human beings from fatal diseases. An automatic deep feature learning-based system is proposed for GIT disease classification. The adaptive gamma correction and weighting distribution (AGCWD) preprocessing procedure is the first stage of the proposed work that is used for enhancing the intensity of the frames. The deep features are extracted from the frames by deep learning models including InceptionNetV3 and GITNet. Ant Colony Optimization (ACO) procedure is employed for feature optimization. Optimized features are fused serially. The classification operation is performed by variants of support vector machine (SVM) classifiers, including the Cubic SVM (CSVM), Coarse Gaussian SVM (CGSVM), Quadratic SVM (QSVM), and Linear SVM (LSVM) classifiers. The intended model is assessed on two challenging datasets including KVASIR and NERTHUS that consist of eight and four classes respectively. The intended model outperforms as compared with existing methods by achieving an accuracy of 99.32% over the KVASIR dataset and 99.89% accuracy using the NERTHUS dataset.</div

The detail of the KVASIR and NERTHUS datasets.

Computer-aided classification of diseases of the gastrointestinal tract (GIT) has become a crucial area of research. Medical science and artificial intelligence have helped medical experts find GIT diseases through endoscopic procedures. Wired endoscopy is a controlled procedure that helps the medical expert in disease diagnosis. Manual screening of the endoscopic frames is a challenging and time taking task for medical experts that also increases the missed rate of the GIT disease. An early diagnosis of GIT disease can save human beings from fatal diseases. An automatic deep feature learning-based system is proposed for GIT disease classification. The adaptive gamma correction and weighting distribution (AGCWD) preprocessing procedure is the first stage of the proposed work that is used for enhancing the intensity of the frames. The deep features are extracted from the frames by deep learning models including InceptionNetV3 and GITNet. Ant Colony Optimization (ACO) procedure is employed for feature optimization. Optimized features are fused serially. The classification operation is performed by variants of support vector machine (SVM) classifiers, including the Cubic SVM (CSVM), Coarse Gaussian SVM (CGSVM), Quadratic SVM (QSVM), and Linear SVM (LSVM) classifiers. The intended model is assessed on two challenging datasets including KVASIR and NERTHUS that consist of eight and four classes respectively. The intended model outperforms as compared with existing methods by achieving an accuracy of 99.32% over the KVASIR dataset and 99.89% accuracy using the NERTHUS dataset.</div

Proposed deep model for GIT disease classification using KVASIR dataset.

Proposed deep model for GIT disease classification using KVASIR dataset.</p

The detailed layered information of GITNet Architecture.

The detailed layered information of GITNet Architecture.</p