Exploring Machine Learning Approaches for Breast Cancer Prediction: A Comparative Analysis with ANOVA-Based Feature Selection DOI

Karima Boutahar,

Sara Laghmati, Hicham Moujahid

и другие.

Опубликована: Май 16, 2024

A widespread issue across the globe, breast cancer impacts women diverse regions and populations. Early detection remains crucial for improving treatment outcomes reducing mortality rates associated with disease. Advancements in technology, especially Machine Learning (ML), present promising opportunities to enhance accuracy effectiveness of methods. The research carried out this investigation involves a comparative analysis three ML models (DT, ANN, SVM), utilizing Wisconsin Diagnostic Breast Cancer (WDBC) dataset incorporating ANOVA feature selection. primary objective is evaluate these achieving precise timely detection. Through comprehensive assessment, which includes common metrics, our findings underscore superior performance SVM model, precision, recall, F1-score 98.59%. These results SVM's potential accurate early prediction using dataset. This contributes advancing understanding machine learning methodologies diagnosis, emphasizing significant role technology facilitating patient outcomes.

Язык: Английский

Brain tumor detection with integrating traditional and computational intelligence approaches across diverse imaging modalities - Challenges and future directions DOI
Amreen Batool,

Yung-Cheol Byun

Computers in Biology and Medicine, Год журнала: 2024, Номер 175, С. 108412 - 108412

Опубликована: Апрель 16, 2024

Язык: Английский

Процитировано

21
Optimizing ResNet50 Performance Using Stochastic Gradient Descent on MRI Images for Alzheimer's Disease Classification DOI Creative Commons
Mohamed Amine Mahjoubi, Driss Lamrani, Shawki Saleh

и другие.

Intelligence-Based Medicine, Год журнала: 2025, Номер 11, С. 100219 - 100219

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

3
An attention-fused architecture for brain tumor diagnosis DOI

Arash Hekmat,

Zuping Zhang, Saif Ur Rehman Khan

и другие.

Biomedical Signal Processing and Control, Год журнала: 2024, Номер 101, С. 107221 - 107221

Опубликована: Ноя. 20, 2024

Язык: Английский

Процитировано

16
Medical Image Classifications Using Convolutional Neural Networks: A Survey of Current Methods and Statistical Modeling of the Literature DOI Creative Commons

Foziya Ahmed Mohammed,

Kula Kekeba Tune, Beakal Gizachew Assefa

и другие.

Machine Learning and Knowledge Extraction, Год журнала: 2024, Номер 6(1), С. 699 - 736

Опубликована: Март 21, 2024

In this review, we compiled convolutional neural network (CNN) methods which have the potential to automate manual, costly and error-prone processing of medical images. We attempted provide a thorough survey improved architectures, popular frameworks, activation functions, ensemble techniques, hyperparameter optimizations, performance metrics, relevant datasets data preprocessing strategies that can be used design robust CNN models. also machine learning algorithms for statistical modeling current literature uncover latent topics, method gaps, prevalent themes future advancements. The results indicate temporal shift in favor designs, such as from use architecture CNN-transformer hybrid. insights point surge practitioners into imaging field, partly driven by COVID-19 challenge, catalyzed detecting diagnosing pathological conditions. This phenomenon likely contributed sharp increase number publications on CNNs imaging, both during after pandemic. Overall, existing has certain gaps scope with respect optimization architectures specifically imaging. Additionally, there is lack post hoc explainability models slow progress adopting low-resource review ends list open research questions been identified through recommendations potentially help set up more robust, reproducible experiments

Язык: Английский

Процитировано

15
Brain tumor classification and detection via hybrid alexnet-gru based on deep learning DOI

A. Priya,

V. Vasudevan

Biomedical Signal Processing and Control, Год журнала: 2023, Номер 89, С. 105716 - 105716

Опубликована: Ноя. 17, 2023

Язык: Английский

Процитировано

20
Advanced AI-driven approach for enhanced brain tumor detection from MRI images utilizing EfficientNetB2 with equalization and homomorphic filtering DOI Creative Commons

A. M. J. Zubair Rahman,

Muskan Gupta,

S. Aarathi

и другие.

BMC Medical Informatics and Decision Making, Год журнала: 2024, Номер 24(1)

Опубликована: Апрель 30, 2024

Brain tumors pose a significant medical challenge necessitating precise detection and diagnosis, especially in Magnetic resonance imaging(MRI). Current methodologies reliant on traditional image processing conventional machine learning encounter hurdles accurately discerning tumor regions within intricate MRI scans, often susceptible to noise varying quality. The advent of artificial intelligence (AI) has revolutionized various aspects healthcare, providing innovative solutions for diagnostics treatment strategies. This paper introduces novel AI-driven methodology brain from images, leveraging the EfficientNetB2 deep architecture. Our approach incorporates advanced preprocessing techniques, including cropping, equalization, application homomorphic filters, enhance quality data more accurate detection. proposed model exhibits substantial performance enhancement by demonstrating validation accuracies 99.83%, 99.75%, 99.2% BD-BrainTumor, Brain-tumor-detection, Brain-MRI-images-for-brain-tumor-detection datasets respectively, this research holds promise refined clinical patient care, fostering reliable identification images. All is available Github: https://github.com/muskan258/Brain-Tumor-Detection-from-MRI-Images-Utilizing-EfficientNetB2 ).

Язык: Английский

Процитировано

9
FlexiCombFE: A flexible, combination-based feature engineering framework for brain tumor detection DOI Creative Commons
Ilknur Tuncer, Abdul Hafeez‐Baig,

Prabal Datta Barua

и другие.

Biomedical Signal Processing and Control, Год журнала: 2025, Номер 104, С. 107538 - 107538

Опубликована: Янв. 26, 2025

Язык: Английский

Процитировано

1
Advanced Deep Learning and Machine Learning Techniques for MRI Brain Tumor Analysis: A Review DOI Creative Commons

Rim Missaoui,

Wided Hechkel, Wajdi Saadaoui

и другие.

Sensors, Год журнала: 2025, Номер 25(9), С. 2746 - 2746

Опубликована: Апрель 26, 2025

A brain tumor is the result of abnormal growth cells in central nervous system (CNS), widely considered as a complex and diverse clinical entity that difficult to diagnose cure. In this study, we focus on current advances medical imaging, particularly magnetic resonance imaging (MRI), how machine learning (ML) deep (DL) algorithms might be combined with assessments improve diagnosis. Due its superior contrast resolution safety compared other methods, MRI highlighted preferred modality for tumors. The challenges related analysis different processes including detection, segmentation, classification, survival prediction are addressed along ML/DL approaches significantly these steps. We systematically analyzed 107 studies (2018–2024) employing ML, DL, hybrid models across publicly available datasets such BraTS, TCIA, Figshare. light recent developments analysis, many have been proposed accurately obtain ontological characteristics tumors, enhancing diagnostic precision personalized therapeutic strategies.

Язык: Английский

Процитировано

1
Enhancing multiclass brain tumor diagnosis using SVM and innovative feature extraction techniques DOI Creative Commons
Mustafa Basthikodi,

M. Chaithrashree,

B. M. Ahamed Shafeeq

и другие.

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Окт. 29, 2024

Abstract In the field of medical imaging, accurately classifying brain tumors remains a significant challenge because visual similarities among different tumor types. This research addresses multiclass categorization by employing Support Vector Machine (SVM) as core classification algorithm and analyzing its performance in conjunction with feature extraction techniques such Histogram Oriented Gradients (HOG) Local Binary Pattern (LBP), well dimensionality reduction technique, Principal Component Analysis (PCA). The study utilizes dataset sourced from Kaggle, comprising MRI images classified into four classes, captured various anatomical planes. Initially, SVM model alone attained an accuracy(acc_val) 86.57% on unseen test data, establishing baseline for performance. To enhance this, PCA was incorporated reduction, which improved acc_val to 94.20%, demonstrating effectiveness reducing mitigating overfitting enhancing generalization. Further gains were realized applying techniques—HOG LBP—in SVM, resulting 95.95%. most substantial improvement observed when combining both HOG, LBP, PCA, achieving impressive 96.03%, along F1 score(F1_val) 96.00%, precision(prec_val) 96.02%, recall(rec_val) 96.03%. approach will not only improves but also efficacy computation, making it robust effective method prediction.

Язык: Английский

Процитировано

6
An Integrated Multimodal Deep Learning Framework for Accurate Skin Disease Classification DOI Open Access
Soufiane Hamida, Driss Lamrani, Mohammed Amine Bouqentar

и другие.

International Journal of Online and Biomedical Engineering (iJOE), Год журнала: 2024, Номер 20(02), С. 78 - 94

Опубликована: Фев. 14, 2024

In order to effectively treat skin diseases, an accurate and prompt diagnosis is required. this article, a novel method for classifying disorders using multimodal classifier presented. The proposed utilizes multiple information sources enhance the accuracy of disease classification. It incorporates images lesions patient-specific data. simultaneously classifies diseases by combining image structured data inputs. effectiveness was evaluated ISIC 2018 dataset, which includes clinical seven categories diseases. results indicate that model outperforms conventional single-modal single-task classifiers, achieving 98.66% classification 94.40% addition, we compare performance with other methodologies, demonstrating its superiority. Despite yielding promising results, has limitations in terms requirements generalizability. Future research directions include incorporating additional sources, investigating genetic integration, applying various medical conditions. This study illustrates potential integrating techniques transfer learning deep neural networks cutaneous

Язык: Английский

Процитировано

5