Sailfish Optimization with Deep Learning Based Oral Cancer Classification Model

Duhayyim, Mesfer Al; Malibari, Areej; Dhahbi, Sami; Nour, Mohamed K.; Al-Turaiki, Isra; Obayya, Marwa; Mohamed, Abdullah

doi:10.32604/csse.2023.030556

Cited by 15 publications

(7 citation statements)

References 18 publications

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“…The results of this study also revealed that AI has important applications in disease screening. AI can have a major role in the screening process, by supporting the screening process and staging, as categorized as the third theme entitled "screening" (43)(44)(45)(46)(47)(48). Screening is an important stage in decision making for disease management and future stages of intervention and treatment.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence Applications in Decision Making for Disease Management

Abdekhoda

Ranjbaran

2023

Preprint

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Background: Artificial intelligence (AI) has several potential applications in medicine, creating opportunities for reliable and evidence based decision making in disease management. Thus, the practical aspects of AI in decision-making should be identified. This study was conducted to identify AI applications in decision making for disease management. Method: This study was a systematic review using the PRISMA-ScR checklist. Data collection was carried out by searching the related keywords in WOS and Scopus in May 2023. Results: Regarding the AI applications in decision making for disease management, we found 80 sub-themes which were categorized into six themes, i.e. 1) Processing and managing data, 2) Characterization and analysis, 3) Prediction and risk stratification, 4) Screening, 5) Prognosis, and 6) Diagnosis. Conclusion: AI has considerable capability in disease treatment and would be an integral part of medicine in the future. This study clearly identified six main themes that addressed AI capability in decision making for disease management. The use of AI can help in making medical decisions with more trust and confidence and thus make medical interventions more accurate and effective.

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Section: Discussionmentioning

confidence: 99%

Artificial Intelligence Applications in Decision Making for Disease Management

Abdekhoda

Ranjbaran

2023

Preprint

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“…Other research is described in Ref. [10] also introduced a groundbreaking algorithm that skillfully employs the Apriori algorithm in conjunction with a multi-ensemble algorithm that merges AdaBoost and Random Forest. This innovative approach was carefully designed to improve the precision of the fetal health classification using CTG, directly addressing the challenges posed by ambiguous data classes often seen in pregnancy.…”

Section: A Existing Algorithmsmentioning

confidence: 99%

“…5% using normalization and random forest (Norm+RF). Duhayyim et al [10] utilized SMOTE+AdaBoost to achieve a remarkable accuracy of 99%, and Raihen & Akter [11] achieved an accuracy of 85.98% using bootstrap aggregating (Bagging). Additionally, Salini et al [12] documented a 93% accuracy with feature selection and Random Forest (FS+RF).…”

Section: E Proposed Algorithm Vs Previous Studies (Stage 3)mentioning

confidence: 99%

CFCM-SMOTE: A Robust Fetal Health Classification to Improve Precision Modeling in Multiclass Scenarios

Ilham,

Kindarto,

Fathurohman

et al. 2024

IJCDS

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The advent of cardiotocography (CTG) has radically transformed prenatal care, facilitating in-depth evaluations of fetal health. Despite this, the reliability of CTG is frequently undermined by data-related issues, such as outliers and class imbalanced data. To address these challenges, our study introduces an innovative integrated methodology that combines cluster-based fuzzy C-means (CFCM) with the synthetic minority oversampling technique (SMOTE) to improve the precision of classification of fetal health status classification in multiclass scenarios. We used a considerable dataset from the UCI Machine Learning Repository, employing CFCM to manage outliers and SMOTE to rectify class imbalanced data. This approach has significantly improved the performance of the classification algorithm, a fact that is corroborated by the comprehensive experimental validation that can be found in the study in Ref. [1]. We observed notable improvements in several evaluation metrics, including precision (PRC), sensitivity (SNS), specificity (SPC), F1 score (F1-S), and accuracy (ACC), surpassing the capabilities of prior methodologies. Specifically, the deployment of our algorithm amplified the precision (PRC: from 98.16% to 99.58%), sensitivity (SNS: from 95.82% to 100%), specificity (SPC: from 85.81% to 99.75%), F1 score (F1-Score: from 96.98% to 99.79%), and accuracy (ACC: from 94.20% to 99.84%) of the Classification and Regression Tree (CART) algorithm for the 'normal' class, while also improving the precision and accuracy of the Random Forest (RF) algorithm from PRC: 94.77% to 95.89% and ACC: 90.60% to 97.45%. These results confirm the potential of CFCM-SMOTE as a robust model for fetal health diagnostics and as a basic strategy for the development of predictive analyzes in prenatal healthcare.

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“…al. [50] introduced a diagnosis system for the detection of oral cancer as benign or malignant from white light images, where a fusion-based feature extractor was considered and passed into an extreme learning machine (ELM) for categorization. The performance was notable but efficacy could be further improved and the issue of overfitting also needs to be clarified.…”

Section: Introductionmentioning

confidence: 99%

Classification of Oral Cancer Into Pre-Cancerous Stages From White Light Images Using LightGBM Algorithm

Goswami,

Bhuyan,

Alfarhood

et al. 2024

IEEE Access

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Cancer is one of the foremost reasons of death worldwide, with nearly 10 million deaths noted in 2020. Globally oral cancer has the sixth rank when compared to other cancers. It is lethal because most cases are noticed at advanced stages, which can be prevented if screened for or treated early, leading to a significant decrease in the mortality rate due to oral cancer. Thus, it becomes necessary to detect oral cancer at its pre-cancerous stages. In this work, a method for classifying oral cavity lesions into benign and malignant for binary classification and their pre-cancerous stages for multi-class classification exploring five different color spaces extracting color and texture features classified using a machine learning technique Light Gradient Boosting Machine (LightGBM) is proposed. The overall performance is satisfactory, outperforming the state-of-art methods in this task, with a testing accuracy of 99.25%, precision of 99.18%, recall of 99.31%, f1-score of 99.24% and specificity of 99.31% for the binary classification, and testing accuracy of 98.88%, precision of 98.86%, recall of 97.92%, f1-score of 98.38% and specificity of 99.03% for multi-class classification. The proposed method used hand-crafted features and a machine-learning classifier, making it less time-consuming and using bare minimum resources.INDEX TERMS Binary and multi-class classification, color spaces, early detection, feature importance, oral cancer, white light images

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Sailfish Optimization with Deep Learning Based Oral Cancer Classification Model

Cited by 15 publications

References 18 publications

Artificial Intelligence Applications in Decision Making for Disease Management

Artificial Intelligence Applications in Decision Making for Disease Management

CFCM-SMOTE: A Robust Fetal Health Classification to Improve Precision Modeling in Multiclass Scenarios

Classification of Oral Cancer Into Pre-Cancerous Stages From White Light Images Using LightGBM Algorithm

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