New techniques for efficiently k-NN algorithm for brain tumor detection

Saeed, Soobia; Abdullah, Afnizanfaizal; Jhanjhi, N. Z.; Naqvi, Mehmood; Nayyar, Anand

doi:10.1007/s11042-022-12271-x

Cited by 24 publications

(4 citation statements)

References 24 publications

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“…The best features from the data were then chosen for machine learning models using RFE. The most crucial models for classification, SVM [31], RF [32], and KNN [33], receive the best features that were chosen from the RFE. SVM performed quite well when choosing the best features from the RFE approach.…”

Section: Efficientnet-cnn+rfementioning

confidence: 99%

Brain tumor detection through novel feature selection using deep efficientNet-CNN-based features with supervised learning and data augmentation

Mujahid,

Rehman,

Alamri

et al. 2024

Phys. Scr.

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Brain tumors being ninth in terms of prevalence and one of the most frequently diagnosed malignant tumors, negatively impact millions of individuals. Identifying and classifying tumors from MRI used for health monitoring poses a challenge for radiologists, yet early detection could significantly enhance the chances of effective treatment. Researchers in the field of explainable AI are currently focused on developing sophisticated techniques to classify and diagnose brain diseases. This study presents a novel framework that enhances the interpretability of our proposed system for brain tumor detection by utilizing explainable AI techniques. To enhance interpretability, we integrate the optimized recursive feature elimination selection technique with support vector machines. This method effectively eliminates redundant features, identifies the most important ones, and enhances the efficiency of detecting tasks. Following that, the optimal recursive feature elimination (ORFE) features are combined using the supervised support vector machine (SVM) technique. While EfficientNet-CNN is a very useful feature extraction framework that extracts the most important features from a transparent model, we reduced the overall computational complexity through feature elimination and supervised models, and the Figshare dataset clearly demonstrated the efficacy of our model. This study achieved very exceptional results and reduced computational complexity as compared to a single CNN model. The experimental results indicate that the proposed SVM-RFE based technique accurately detects brain tumors with a 99.51% accuracy and a specificity score of 99.63\%. The proposed approach obtained an accuracy of 98.93% with a standard deviation of 0.032 using 10-fold cross-validation. Additionally, it produced an optimal ROC_AUC of 100% for cases including meningiomas and pituitary tumors.

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Section: Efficientnet-cnn+rfementioning

confidence: 99%

Brain tumor detection through novel feature selection using deep efficientNet-CNN-based features with supervised learning and data augmentation

Mujahid,

Rehman,

Alamri

et al. 2024

Phys. Scr.

View full text Add to dashboard Cite

show abstract

“…Using 4D image light field methods, Saeed et al 22 established a framework to distinguish cancer‐damaged regions from non‐tumor areas. They used a hybrid K‐Nearest Neighbor (k‐NN) approach in conjunction with the Laplace transform, Fast Fourier transform, and four‐dimensional MRI images.…”

Section: Related Workmentioning

confidence: 99%

Enhancing explainability in brain tumor detection: A novel DeepEBTDNet model with LIME on MRI images

Ullah,

Hassan,

Khan

et al. 2023

Int J Imaging Syst Tech

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Early detection of brain tumors is vital for improving patient survival rates, yet the manual analysis of the extensive 3D MRI images can be error‐prone and time‐consuming. This study introduces the Deep Explainable Brain Tumor Deep Network (DeepEBTDNet), a novel deep learning model for binary classification of brain MRIs as tumorous or normal. Employing sub‐image dualistic histogram equalization (DSIHE) for enhanced image quality, DeepEBTDNet utilizes 12 convolutional layers with leaky ReLU (LReLU) activation for feature extraction, followed by a fully connected classification layer. Transparency and interpretability are emphasized through the application of the Local Interpretable Model‐Agnostic Explanations (LIME) method to explain model predictions. Results demonstrate DeepEBTDNet's efficacy in brain tumor detection, even across datasets, achieving a validation accuracy of 98.96% and testing accuracy of 94.0%. This study underscores the importance of explainable AI in healthcare, facilitating precise diagnoses and transparent decision‐making for early brain tumor identification and improved patient outcomes.

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“…If a similar case has been found, the attribute with missing values will be filled with the value from the attribute value with a similar case. The K-NN algorithm can provide more robust and more sensitive predictions of missing values [12]. A k-neighbor of 10 to overcome missing values can minimize the error rate when doing classification [13].…”

Section: B Data Preprocessingmentioning

confidence: 99%

The Effect of Resampling on Classifier Performance: an Empirical Study

Pujianto,

Akbar,

Lassela

et al. 2022

Knowl. Eng. Data Sci.

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An imbalanced class on a dataset is a common classification problem. The effect of using imbalanced class datasets can cause a decrease in the performance of the classifier. Resampling is one of the solutions to this problem. This study used 100 datasets from 3 websites: UCI Machine Learning, Kaggle, and OpenML. Each dataset will go through 3 processing stages: the resampling process, the classification process, and the significance testing process between performance evaluation values of the combination of classifier and the resampling using paired t-test. The resampling used in the process is Random Undersampling, Random Oversampling, and SMOTE. The classifier used in the classification process is Naïve Bayes Classifier, Decision Tree, and Neural Network. The classification results in accuracy, precision, recall, and f-measure values are tested using paired t-tests to determine the significance of the classifier's performance from datasets that were not resampled and those that had applied the resampling. The paired t-test is also used to find a combination between the classifier and the resampling that gives significant results. This study obtained two results. The first result is that resampling on imbalanced class datasets can substantially affect the classifier's performance more than the classifier's performance from datasets that are not applied the resampling technique. The second result is that combining the Neural Network Algorithm without the resampling provides significance based on the accuracy value. Combining the Neural Network Algorithm with the SMOTE technique provides significant performance based on the amount of precision, recall, and f-measure.

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New techniques for efficiently k-NN algorithm for brain tumor detection

Cited by 24 publications

References 24 publications

Brain tumor detection through novel feature selection using deep efficientNet-CNN-based features with supervised learning and data augmentation

Brain tumor detection through novel feature selection using deep efficientNet-CNN-based features with supervised learning and data augmentation

Enhancing explainability in brain tumor detection: A novel DeepEBTDNet model with LIME on MRI images

The Effect of Resampling on Classifier Performance: an Empirical Study

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