Modified Salp Swarm Algorithm With Deep Learning Based Gastrointestinal Tract Disease Classification on Endoscopic Images

Obayya, Marwa; Al‐Wesabi, Fahd N.; Maashi, Mashael; Mohamed, Abdullah; Hamza, Manar Ahmed; Drar, Suhanda; Yaseen, Ishfaq; Alsaid, Mohamed Ibrahim

doi:10.1109/access.2023.3256084

Cited by 16 publications

(9 citation statements)

References 31 publications

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“…Table 1 and Fig. 3 inspect an overall throughput (THRO) result of the HSO-RSAUAVC system with different approaches [16]. The outcome shows that the HSO-RSAUAVC system achieves enhanced performance.…”

Section: Resultsmentioning

confidence: 97%

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Design of Hybrid Snake Optimizer Based Route Selection Approach for Unmanned Aerial Vehicles Communication

Alkahtani,

Mahgoub,

Alotaibi

et al. 2024

IEEE Access

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The effective usage of energy becomes crucial for the successful deployment and operation of unmanned aerial vehicles (UAVs) in different applications, such as surveillance, transportation, and communication networks. The increasing demand for UAVs in different industries such as agriculture, logistics, and emergency response has led to the development of more sophisticated and advanced UAVs. However, the limited onboard energy resource of UAVs poses a major problem for their long-term operation and endurance. In addition, artificial intelligence (AI) and machine learning (ML) could allow UAVs to make more informed and intelligent decisions regarding their operations, resulting in sustainable and more energyefficient UAV deployment. This article designs a Hybrid Snake Optimizer-based Route Selection Approach for Unmanned Aerial Vehicles Communication (HSO-RSAUAVC) technique. The goal of the HSO-RSAUAVC technique is to explore and select optimal routes for UAV communication. In the presented HSO-RSAUAVC technique, the SO algorithm is integrated with Bernoulli Chaotic Mapping and Levy flight (LF) for enhanced performance. In addition, the HSO-RSAUAVC method derives a fitness function including residual energy (RE), distance, and UAV degree. By incorporating the HSO-RSAUAVC technique, we can dynamically adapt UAV paths to overcome obstacles, decrease communication interference, and optimize energy utilization. To validate the performance of the proposed model, a series of simulations were performed. The comparative result analysis illustrates the better performance of the HSO-RSAUAVC technique in improving the performance and reliability of UAV communication.INDEX TERMS Unmanned aerial vehicles; Routing; Snake optimizer; Energy efficiency; Fitness function I. INTRODUCTIONRecently, with the fast growth of unmanned aerial vehicle (UAV) technology, UAVs have been extensively utilized in numerous domains [1]. Various kinds of UAVs can support people to accomplish some comparatively risky, impossible, and urgent tasks, like map reconstruction, ocean exploration, environmental analysis, aerial photography, and material distribution [2]. However, the existing UAVs are inadequately intelligent for performing difficult activities, and still major of them require people's real-time control [3]. A single UAV could only execute moderately easy tasks, nonetheless, the UAV set could effectively perform several laborious and complex tasks after acceptable task planning [4]. The task distribution issue is identical to the combinatorial optimizer decision issue for many UAVs. It is an integration method developed to satisfy UAV efficiency and limitations. The

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Section: Resultsmentioning

confidence: 97%

“…This route planner is less-weighted hence this swift guiding model for real-time needs. Alymani et al [16] present a novel technique called Dispersal Foraging Strategy with Cuckoo Search Optimization-based Path Planning (DFSCSOPP). In this work, the optimum route recognition for data transfer is achieved in UAV networking.…”

Section: Related Workmentioning

confidence: 99%

Design of Hybrid Snake Optimizer Based Route Selection Approach for Unmanned Aerial Vehicles Communication

Alkahtani,

Mahgoub,

Alotaibi

et al. 2024

IEEE Access

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“…Deep Belief Network with Extreme Learning Machine (DBN-ELM) was utilized for GIT categorization. The accuracy of the suggested approaches was 98.03% ( 51 ). A unique approach for the automated identification and localization of gastrointestinal (GI) abnormalities in endoscopic video frame sequences is presented in this work ( 52 ).…”

Section: Related Workmentioning

confidence: 99%

Explainable AI-driven model for gastrointestinal cancer classification

Binzagr

2024

Front. Med.

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

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“…The use of machine-learning methods also makes it possible to identify the pathology of the genitourinary system [1]. Image segmentation based on machine-learning models and neural networks makes it possible to identify and classify diseases of the gastrointestinal tract [10][11][12][13]. In terms of identification studies, possible heart diseases based on clinical data in different patients were considered in [14], but their application in practice relies on the complexity of the data and the presence of correlations between them.…”

Section: Related Workmentioning

confidence: 99%

“…The listed methods have a high resource intensity in terms of the applied software and hardware. Summarizing the results [1][2][3][4][5][6][7][8][9][10][11][12][13][14], it is advisable to note that with almost all the methods, there is no possibility of automating the detection of pathology or deviations in the functioning of a particular organ (Table 1). In the table, a «+» sign indicates the presence of this property from the column header in the method from the first column in this source, «−» its absence.…”

Section: Related Workmentioning

confidence: 99%

Method for Detecting Pathology of Internal Organs Using Bioelectrography

Shichkina,

Fatkieva,

Sychev

et al. 2024

Diagnostics

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This article considers the possibility of using the bioelectrography method to identify the pathology of internal organs. It is shown that with the currently existing methods, there is no possibility of the automatic detection of diseases or abnormalities in the functioning of a particular organ, or of the definition of combined pathology. It has been revealed that the use of various classifiers makes it possible to expand the field of pathology and choose the most optimal method for determining a particular disease. Based on this, a method for detecting the pathology of internal organs is developed, as well as a software package that allows the detection of diseases of the internal organs based on the bioelectrography results. Machine-learning models such as logistic regression, decision tree, random forest, xgboost, KNN, SVM and HyperTab are used for this purpose. HyperTab, logistic regression and xgboost turn out to be the best among them for this task, achieving a performance according to the f1-score metric in the order of 60–70%. The use of the developed method will, in practice, allow us to switch to combining various machine-learning models for the identification of certain diseases, as well as for the identification of combined pathology, which will help solve the problem of detecting pathology during screening studies and lead to a reduction in the burden on the staff of medical institutions.

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Modified Salp Swarm Algorithm With Deep Learning Based Gastrointestinal Tract Disease Classification on Endoscopic Images

Cited by 16 publications

References 31 publications

Design of Hybrid Snake Optimizer Based Route Selection Approach for Unmanned Aerial Vehicles Communication

Design of Hybrid Snake Optimizer Based Route Selection Approach for Unmanned Aerial Vehicles Communication

Explainable AI-driven model for gastrointestinal cancer classification

Method for Detecting Pathology of Internal Organs Using Bioelectrography

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