Recognizing Gastrointestinal Malignancies on WCE and CCE Images by an Ensemble of Deep and Handcrafted Features with Entropy and PCA Based Features Optimization

Naz, Javeria; Sharif, Muhammad; Raza, Mudassar; Shah, Jamal Hussain; Yasmin, Mussarat; Kadry, Seifedine; Vimal, S.

doi:10.1007/s11063-021-10481-2

Cited by 15 publications

(7 citation statements)

References 50 publications

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“…The proposed approach is compared with the most recent methods in this section [ 1 , 3 , 19 , 33 , 35 , 42 , 43 , 56 , 57 ]. The authors in [ 3 ] proposed a method for gastrointestinal disease detection from WCE images, and they achieved a 98.40% accuracy.…”

Section: Discussion and Comparison With Existing Methodsmentioning

confidence: 99%

“…Guanghua Zhang et al [ 34 ] proposed a method for digestive tract tumor detection. In our previous study [ 35 ], a hybrid method was proposed for the classification of gastrointestinal diseases. The major steps involved in this methodology are pre-processing, texture features extraction, CNN features extraction, feature fusion, and classification.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Comparative Analysis of Optimization Algorithms for Gastrointestinal Abnormalities Recognition and Classification Based on Ensemble XcepNet23 and ResNet18 Features

Naz

Sharif

et al. 2023

Biomedicines

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Esophagitis, cancerous growths, bleeding, and ulcers are typical symptoms of gastrointestinal disorders, which account for a significant portion of human mortality. For both patients and doctors, traditional diagnostic methods can be exhausting. The major aim of this research is to propose a hybrid method that can accurately diagnose the gastrointestinal tract abnormalities and promote early treatment that will be helpful in reducing the death cases. The major phases of the proposed method are: Dataset Augmentation, Preprocessing, Features Engineering (Features Extraction, Fusion, Optimization), and Classification. Image enhancement is performed using hybrid contrast stretching algorithms. Deep Learning features are extracted through transfer learning from the ResNet18 model and the proposed XcepNet23 model. The obtained deep features are ensembled with the texture features. The ensemble feature vector is optimized using the Binary Dragonfly algorithm (BDA), Moth–Flame Optimization (MFO) algorithm, and Particle Swarm Optimization (PSO) algorithm. In this research, two datasets (Hybrid dataset and Kvasir-V1 dataset) consisting of five and eight classes, respectively, are utilized. Compared to the most recent methods, the accuracy achieved by the proposed method on both datasets was superior. The Q_SVM’s accuracies on the Hybrid dataset, which was 100%, and the Kvasir-V1 dataset, which was 99.24%, were both promising.

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Section: Discussion and Comparison With Existing Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Comparative Analysis of Optimization Algorithms for Gastrointestinal Abnormalities Recognition and Classification Based on Ensemble XcepNet23 and ResNet18 Features

Naz

Sharif

et al. 2023

Biomedicines

Self Cite

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“…A small number of authors have proposed a hybrid approach making use of both deep learning and machine learning techniques. For example, Naz et al [40] proposed a method in which they used filtering techniques to enhance the contrast of WCE images. Then, they performed feature extraction using a hybrid method consisting of LBP, SFTA, VGG16 and InceptionV3.…”

Section: Machine Learning-based Methodsmentioning

confidence: 99%

A Robust Approach for Ulcer Classification/Detection in WCE Images

Dahmouni,

Ait Abdelouahad,

Aderghal

et al. 2024

Int. J. Onl. Eng.

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Wireless Capsule Endoscopy (WCE) is a medical diagnostic technique recognized for its minimally invasive and painless nature for the patients. It uses remote imaging techniques to explore various segments of the gastrointestinal (GI) tract, particularly the hard-to-reach small intestine, making it an effective alternative to traditional endoscopic techniques. However, physicians face a significant challenge when it comes to analyzing a large number of endoscopic images due to the effort and time required. It is therefore imperative to implement aided-diagnostic systems capable of automatically detecting suspicious areas for subsequent medical assessment. In this paper, we present a novel approach to identify gastrointestinal tract abnormalities from WCE images, with a particular focus on ulcerated areas. Our approach involves the use of the Median Robust Extended Local Binary Pattern (MRELBP) descriptor, which effectively overcomes the challenges faced when WCE image acquisition, such as variations in illumination and contrast, rotation, and noise. Using machine learning algorithms, we conducted experiments on the extensive Kvasir-Capsule dataset, and subsequently compared our results with recent relevant studies. Noteworthy is the fact that our approach achieved an accuracy of 97.04% with the SVM (RBF) classifier and 96.77% with the RF classifier.

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“…It was reported that combining the features improves the accuracy of the classification compared to the independent application of handcrafted and deep features. Naz et al [ 33 ] proposed a hybrid method based on the texture features and deep features for automated detection of gastrointestinal diseases by using wireless capsule endoscopy images. Texture features were extracted by using SFTA and LBP texture analysis methods.…”

Section: Related Workmentioning

confidence: 99%

The classification of wheat yellow rust disease based on a combination of textural and deep features

HAYIT

Erbay

Varçın

et al. 2023

Multimed Tools Appl

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Yellow rust is a devastating disease that causes significant losses in wheat production worldwide and significantly affects wheat quality. It can be controlled by cultivating resistant cultivars, applying fungicides, and appropriate agricultural practices. The degree of precautions depends on the extent of the disease. Therefore, it is critical to detect the disease as early as possible. The disease causes deformations in the wheat leaf texture that reveals the severity of the disease. The gray-level co-occurrence matrix(GLCM) is a conventional texture feature descriptor extracted from gray-level images. However, numerous studies in the literature attempt to incorporate texture color with GLCM features to reveal hidden patterns that exist in color channels. On the other hand, recent advances in image analysis have led to the extraction of data-representative features so-called deep features. In particular, convolutional neural networks (CNNs) have the remarkable capability of recognizing patterns and show promising results for image classification when fed with image texture. Herein, the feasibility of using a combination of textural features and deep features to determine the severity of yellow rust disease in wheat was investigated. Textural features include both gray-level and color-level information. Also, pre-trained DenseNet was employed for deep features. The dataset, so-called Yellow-Rust-19, composed of wheat leaf images, was employed. Different classification models were developed using different color spaces such as RGB, HSV, and L*a*b, and two classification methods such as SVM and KNN. The combined model named CNN-CGLCM_HSV, where HSV and SVM were employed, with an accuracy of 92.4% outperformed the other models.

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Recognizing Gastrointestinal Malignancies on WCE and CCE Images by an Ensemble of Deep and Handcrafted Features with Entropy and PCA Based Features Optimization

Cited by 15 publications

References 50 publications

A Comparative Analysis of Optimization Algorithms for Gastrointestinal Abnormalities Recognition and Classification Based on Ensemble XcepNet23 and ResNet18 Features

A Comparative Analysis of Optimization Algorithms for Gastrointestinal Abnormalities Recognition and Classification Based on Ensemble XcepNet23 and ResNet18 Features

A Robust Approach for Ulcer Classification/Detection in WCE Images

The classification of wheat yellow rust disease based on a combination of textural and deep features

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