Malaria parasitic detection using a new Deep Boosted and Ensemble Learning framework DOI Creative Commons
Hafiz M. Asif, Saddam Hussain Khan, Tahani Jaser Alahmadi

et al.

Complex & Intelligent Systems, Journal Year: 2024, Volume and Issue: 10(4), P. 4835 - 4851

Published: April 9, 2024

Abstract Malaria is a potentially fatal plasmodium parasite injected by female anopheles mosquitoes that infect red blood cells and cause millions of lifelong disability worldwide yearly. However, specialists’ manual screening in clinical practice laborious prone to error. Therefore, novel Deep Boosted Ensemble Learning (DBEL) framework, comprising the stacking new Boosted-BR-STM convolutional neural networks (CNN) ensemble ML classifiers, developed screen malaria images. The proposed based on dilated-convolutional block-based Split Transform Merge (STM) feature-map Squeezing–Boosting (SB) ideas. Moreover, STM block uses regional boundary operations learn parasite’s homogeneity, heterogeneity, with patterns. Furthermore, diverse boosted channels are attained employing Transfer Learning-based SB blocks at abstract, medium, conclusion levels minute intensity texture variation parasitic pattern. Additionally, enhance learning capacity foster more representation features, boosting final stage achieved through TL utilizing multipath residual learning. DBEL framework implicates prominent provides generated discriminative features classifiers. improves discrimination ability generalization deep feature spaces customized CNNs fed into classifiers for comparative analysis. outperforms existing techniques NIH dataset enhanced using discrete wavelet transform enrich space. Accuracy (98.50%), Sensitivity (0.9920), F-score (0.9850), AUC (0.9960), which suggests it be utilized screening.

Language: Английский

Brain tumor classification utilizing deep features derived from high-quality regions in MRI images DOI

Muhammad Aamir,

Ziaur Rahman, Waheed Ahmed Abro

et al.

Biomedical Signal Processing and Control, Journal Year: 2023, Volume and Issue: 85, P. 104988 - 104988

Published: May 9, 2023

Language: Английский

Citations

34
Dual Deep CNN for Tumor Brain Classification DOI Creative Commons
Aya M. Al‐Zoghby,

Esraa Mohamed K. Al-Awadly,

Ahmad Moawad

et al.

Diagnostics, Journal Year: 2023, Volume and Issue: 13(12), P. 2050 - 2050

Published: June 13, 2023

Brain tumor (BT) is a serious issue and potentially deadly disease that receives much attention. However, early detection identification of type location are crucial for effective treatment saving lives. Manual diagnoses time-consuming depend on radiologist experts; the increasing number new cases brain tumors makes it difficult to process massive large amounts data rapidly, as time critical factor in patients' Hence, artificial intelligence (AI) vital understanding its various types. Several studies proposed different techniques BT classification. These machine learning (ML) deep (DL). The ML-based method requires handcrafted or automatic feature extraction algorithms; however, DL becomes superior self-learning robust classification recognition tasks. This research focuses classifying three types using MRI imaging: meningioma, glioma, pituitary tumors. DCTN model depends dual convolutional neural networks with VGG-16 architecture concatenated custom CNN (convolutional networks) architecture. After conducting approximately 22 experiments architectures models, our reached 100% accuracy during training 99% testing. methodology obtained highest possible improvement over existing studies. solution provides revolution healthcare providers can be used future save human

Language: Английский

Citations

29
Brain tumor detection and screening using artificial intelligence techniques: Current trends and future perspectives DOI
U. Raghavendra, Anjan Gudigar,

Aritra Paul

et al.

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 163, P. 107063 - 107063

Published: June 1, 2023

Language: Английский

Citations

28
Improving prediction of cervical cancer using KNN imputer and multi-model ensemble learning DOI Creative Commons
Turki Aljrees

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(1), P. e0295632 - e0295632

Published: Jan. 3, 2024

Cervical cancer is a leading cause of women's mortality, emphasizing the need for early diagnosis and effective treatment. In line with imperative intervention, automated identification cervical has emerged as promising avenue, leveraging machine learning techniques to enhance both speed accuracy diagnosis. However, an inherent challenge in development these systems presence missing values datasets commonly used detection. Missing data can significantly impact performance models, potentially inaccurate or unreliable results. This study addresses critical identification-handling datasets. The present novel approach that combines three models into stacked ensemble voting classifier, complemented by use KNN Imputer manage values. proposed model achieves remarkable results 0.9941, precision 0.98, recall 0.96, F1 score 0.97. examines distinct scenarios: one involving deletion values, another utilizing imputation, third employing PCA imputing research significant implications medical field, offering experts powerful tool more accurate therapy enhancing overall effectiveness testing procedures. By addressing challenges achieving high accuracy, this work represents valuable contribution detection, ultimately aiming reduce disease on health healthcare systems.

Language: Английский

Citations

11
Brain Tumor Detection and Classification Using Transfer Learning Models DOI Creative Commons

Vinod Kumar D,

G. Murali,

Kannan Sreerangan

et al.

Published: Feb. 28, 2024

Diagnosing brain tumors is a time-consuming process requiring radiologist expertise. With the growing patient population and increased data volume, conventional procedures have become expensive ineffective. Scholars explored algorithms for detecting classifying tumors, focusing on precision efficiency. Deep learning methodologies are being used to create automated systems that can diagnose or segment with efficiency, particularly in cancer classification. This approach facilitates transfer models medical imaging. The present study undertakes an evaluation of three foundational domain computer vision, namely AlexNet, VGG16, ResNet-50. VGG16 ResNet-50 demonstrated praiseworthy performance, thereby instigating amalgamation these into groundbreaking hybrid VGG16–ResNet-50 model. amalgamated model was subsequently implemented dataset, yielding remarkable accuracy 99.98%, sensitivity specificity 99.98% F1 score 99.98%. Based comparative analysis alternative models, it be deduced suggested framework exhibits commendable level dependability facilitating timely identification diverse cerebral neoplasms.

Language: Английский

Citations

11
Design and assessment of improved Convolutional Neural Network based brain tumor segmentation and classification system DOI Creative Commons
Alok Singh Chauhan, Jyoti Prakash Singh, Sumit Kumar

et al.

Journal of Integrated Science and Technology, Journal Year: 2024, Volume and Issue: 12(4)

Published: Feb. 8, 2024

Deep learning techniques have recently demonstrated promising outcomes in the segmentation of brain tumors from MRI images. Due to its capability handle high-resolution images and segment entire tumor region, U-Net model is one them frequently utilized. For analysis planning treatments, accurate using multi-contrast essential. models including U-Net, PSPNet, DeepLabV3+, ResNet50 encouraging tumors. Using BraTS 2018 dataset, we compare these this research. We evaluate a variety measures, Hausdorff Distance (HD), Absolute Volume Difference (AVD), Dice Similarity Coefficient (DSC), look into how data augmentation transfer approaches affect models' performance. The findings demonstrate that 3D performed best, with DSC 0.90, HD 10.69mm, AVD 11.15%. PSPNet achieved comparable performance, 0.89, 11.37mm, 12.24%. DeepLabV3+ lower DSCs 0.85 0.83, respectively. Based on discoveries analysis, suggested for utilizing URN:NBN:sciencein.jist.2024.v12.793

Language: Английский

Citations

9
Advances of Artificial Intelligence in Clinical Application and Scientific Research of Neuro-oncology: Current Knowledge and Future Perspectives DOI Creative Commons
Yihong Zhan, Yuanyue Hao, Xiang Wang

et al.

Critical Reviews in Oncology/Hematology, Journal Year: 2025, Volume and Issue: unknown, P. 104682 - 104682

Published: March 1, 2025

Brain tumors refer to the abnormal growths that occur within brain's tissue, comprising both primary neoplasms and metastatic lesions. Timely detection, precise staging, suitable treatment, standardized management are of significant clinical importance for extending survival rates brain tumor patients. Artificial intelligence (AI), a discipline computer science, is leveraging its robust capacity information identification combination revolutionize traditional paradigms oncology care, offering substantial potential precision medicine. This article provides an overview current applications AI in tumors, encompassing technologies, their working mechanisms workflow, contributions diagnosis as well role scientific research, particularly drug innovation revealing microenvironment. Finally, paper addresses existing challenges, solutions, future application prospects. review aims enhance our understanding provide valuable insights forthcoming inquiries.

Language: Английский

Citations

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

Rim Missaoui,

Wided Hechkel, Wajdi Saadaoui

et al.

Sensors, Journal Year: 2025, Volume and Issue: 25(9), P. 2746 - 2746

Published: April 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.

Language: Английский

Citations

1
A Robust End-to-End Deep Learning-Based Approach for Effective and Reliable BTD Using MR Images DOI Creative Commons
Naeem Ullah, Muhammad Sohail Khan, Javed Ali Khan

et al.

Sensors, Journal Year: 2022, Volume and Issue: 22(19), P. 7575 - 7575

Published: Oct. 6, 2022

Detection of a brain tumor in the early stages is critical for clinical practice and survival rate. Brain tumors arise multiple shapes, sizes, features with various treatment options. Tumor detection manually challenging, time-consuming, prone to error. Magnetic resonance imaging (MRI) scans are mostly used due their non-invasive properties also avoid painful biopsy. MRI scanning one patient’s generates many 3D images from directions, making manual very difficult, error-prone, time-consuming. Therefore, there considerable need autonomous diagnostics tools detect accurately. In this research, we have presented novel TumorResnet deep learning (DL) model detection, i.e., binary classification. The TumorResNet employs 20 convolution layers leaky ReLU (LReLU) activation function feature map compute most distinctive features. Finally, three fully connected classification classify into normal tumorous. performance proposed architecture evaluated on standard Kaggle dataset (BTD), which contains MR images. achieved good accuracy 99.33% BTD. These experimental results, including cross-dataset setting, validate superiority over contemporary frameworks. This study offers an automated BTD method that aids diagnosis cancers. procedure has substantial impact improving options patient survival.

Language: Английский

Citations

30
A new deep boosted CNN and ensemble learning based IoT malware detection DOI Creative Commons
Saddam Hussain Khan, Tahani Jaser Alahmadi,

Wasi Ullah

et al.

Computers & Security, Journal Year: 2023, Volume and Issue: 133, P. 103385 - 103385

Published: July 7, 2023

Security issues are threatened in various types of networks, especially the Internet Things (IoT) environment that requires early detection. IoT is network real-time devices like home automation systems and can be controlled by open-source android devices, which an open ground for attackers. Attackers access credentials, initiate a different kind security breach, compromises control. Therefore, timely detecting increasing number sophisticated malware attacks challenge to ensure credibility protection. In this regard, we have developed new detection framework, Deep Squeezed-Boosted Ensemble Learning (DSBEL), comprised novel Boundary-Region Split-Transform-Merge (SB-BR-STM) CNN ensemble learning. The proposed STM block employs multi-path dilated convolutional, Boundary, regional operations capture homogenous heterogeneous global malicious patterns. Moreover, diverse feature maps achieved using transfer learning multi-path-based squeezing boosting at initial final levels learn minute pattern variations. Finally, boosted discriminative features extracted from deep SB-BR-STM provided classifiers (SVM, MLP, AdabooSTM1) improve hybrid generalization. performance analysis DSBEL framework against existing techniques been evaluated IOT_Malware dataset on standard measures. Evaluation results show progressive as 98.50% accuracy, 97.12% F1-Score, 91.91% MCC, 95.97 % Recall, 98.42 Precision. robust helpful activity suggests future strategies.

Language: Английский

Citations

23