Detection and isolation of brain tumors in cancer patients using neural network techniques in MRI images DOI Creative Commons
Mahdi Mir, Zaid Saad Madhi, Ali Hamid AbdulHussein

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Oct. 7, 2024

MRI imaging primarily focuses on the soft tissues of human body, typically performed prior to a patient's transfer surgical suite for medical procedure. However, utilizing images tumor diagnosis is time-consuming process. To address these challenges, new method automatic brain was developed, employing combination image segmentation, feature extraction, and classification techniques isolate specific region interest in an corresponding tumor. The proposed this study comprises five distinct steps. Firstly, pre-processing conducted, various filters enhance quality. Subsequently, thresholding applied facilitate segmentation. Following extraction performed, analyzing morphological structural properties images. Then, selection carried out using principal component analysis (PCA). Finally, artificial neural network (ANN). In total, 74 unique features were extracted from each image, resulting dataset 144 observations. Principal employed select top 8 most effective features. Artificial Neural Networks (ANNs) leverage comprehensive data selective knowledge. Consequently, approach evaluated compared with alternative methods, significant improvements precision, accuracy, F1 score. demonstrated notable increases 99.3%, 97.3%, 98.5% Sensitivity These findings highlight efficiency accurately segmenting classifying

Language: Английский

Artemisinin optimization based on malaria therapy: Algorithm and applications to medical image segmentation DOI

Yuan Chong,

Dong Zhao, Ali Asghar Heidari

et al.

Displays, Journal Year: 2024, Volume and Issue: 84, P. 102740 - 102740

Published: May 4, 2024

Language: Английский

Citations

40
Comparative analysis of vision transformers and convolutional neural networks in osteoporosis detection from X-ray images DOI Creative Commons

Ali Sarmadi,

Zahra Sadat Razavi,

André J. van Wijnen

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Aug. 3, 2024

Within the scope of this investigation, we carried out experiments to investigate potential Vision Transformer (ViT) in field medical image analysis. The diagnosis osteoporosis through inspection X-ray radio-images is a substantial classification problem that were able address with assistance models. In order provide basis for comparison, conducted parallel analysis which sought solve same by employing traditional convolutional neural networks (CNNs), are well-known and commonly used techniques solution categorization issues. findings our research led us conclude ViT capable achieving superior outcomes compared CNN. Furthermore, provided methods have access sufficient quantity training data, probability increases both arrive at more appropriate solutions critical

Language: Английский

Citations

20
A review of uncertainty quantification in medical image analysis: Probabilistic and non-probabilistic methods DOI Creative Commons
Ling Huang, Su Ruan, Yucheng Xing

et al.

Medical Image Analysis, Journal Year: 2024, Volume and Issue: 97, P. 103223 - 103223

Published: June 1, 2024

The comprehensive integration of machine learning healthcare models within clinical practice remains suboptimal, notwithstanding the proliferation high-performing solutions reported in literature. A predominant factor hindering widespread adoption pertains to an insufficiency evidence affirming reliability aforementioned models. Recently, uncertainty quantification methods have been proposed as a potential solution quantify and thus increase interpretability acceptability results. In this review, we offer overview prevailing inherent developed for various medical image tasks. Contrary earlier reviews that exclusively focused on probabilistic methods, review also explores non-probabilistic approaches, thereby furnishing more holistic survey research pertaining Analysis images with summary discussion applications corresponding evaluation protocols are presented, which focus specific challenges analysis. We highlight some future work at end. Generally, aims allow researchers from both technical backgrounds gain quick yet in-depth understanding analysis

Language: Английский

Citations

16
Progress and trends in neurological disorders research based on deep learning DOI
Muhammad Shahid Iqbal, Md Belal Bin Heyat, Saba Parveen

et al.

Computerized Medical Imaging and Graphics, Journal Year: 2024, Volume and Issue: 116, P. 102400 - 102400

Published: May 25, 2024

Language: Английский

Citations

9
Unveiling the potential of FOXO3 in lung cancer: From molecular insights to therapeutic prospects DOI Open Access

Mohammad Ebrahimnezhad,

Amir Valizadeh, Maryam Majidinia

et al.

Biomedicine & Pharmacotherapy, Journal Year: 2024, Volume and Issue: 176, P. 116833 - 116833

Published: June 5, 2024

Lung cancer poses a significant challenge regarding molecular heterogeneity, as it encompasses wide range of alterations and cancer-related pathways. Recent discoveries made feasible to thoroughly investigate the mechanisms underlying lung cancer, giving rise possibility novel therapeutic strategies relying on molecularly targeted drugs. In this context, forkhead box O3 (FOXO3), member transcription factors, has emerged crucial protein commonly dysregulated in cells. The regulation FOXO3 reacting external stimuli plays key role maintaining cellular homeostasis component machinery that determines whether cells will survive or dies. Indeed, various extrinsic cues regulate FOXO3, affecting its subcellular location transcriptional activity. These regulations are mediated by diverse signaling pathways, non-coding RNAs (ncRNAs), interactions eventually drive post-transcriptional modification FOXO3. Nevertheless, while is no doubt implicated numerous aspects unclear they act tumor suppressors, promotors, both based situation. However, serves an intriguing possible target therapeutics widely used anti-cancer chemo drugs can it. review, we describe summary recent findings clarify targeting activity might hold promise treatment.

Language: Английский

Citations

6
A comprehensive review of lessons learned from quantum dots in cancer therapy DOI
Javad Mohammadi,

Ali Hheidari,

Sohrab Sardari

et al.

Biomedical Materials, Journal Year: 2024, Volume and Issue: 19(5), P. 052004 - 052004

Published: July 29, 2024

Quantum dots (QDs) are with exceptional physicochemical and biological properties, making them highly versatile for a wide range of applications in cancer therapy. One the key features QDs is their unique electronic structure, which gives functional attributes. Notably, photoluminescence can be strong adjustable, allowing to effectively used fluorescence based diagnosis such as biosensing bioimaging. In addition, demonstrate an impressive capacity loading cargo, ideal drug delivery applications. Moreover, ability absorb incident radiation positions promising candidates cancer-killing techniques like photodynamic The objective this comprehensive review present current overview recent advancements utilizing multifunctional innovative biomaterials. This focuses on elucidating biological, electronic, properties QDs, along discussing technical QD synthesis. Furthermore, it thoroughly explores progress made biosensing, bioimaging, therapy including necrosis, highlighting significant potential field treatment. addresses limitations associated provides valuable insights into future directions, thereby facilitating further field. By presenting well-structured overview, serves authoritative informative resource that guide research endeavors foster continued

Language: Английский

Citations

6
Diagnostic test accuracy of AI-assisted mammography for breast imaging: a narrative review DOI Creative Commons
Daksh Dave, Adnan Akhunzada, Nikola Ivković

et al.

PeerJ Computer Science, Journal Year: 2025, Volume and Issue: 11, P. e2476 - e2476

Published: Feb. 19, 2025

The integration of artificial intelligence into healthcare, particularly in mammography, holds immense potential for improving breast cancer diagnosis. Artificial (AI), with its ability to process vast amounts data and detect intricate patterns, offers a solution the limitations traditional including missed diagnoses false positives. This review focuses on diagnostic accuracy AI-assisted synthesizing findings from studies across different clinical settings algorithms. motivation this research lies addressing need enhanced tools screening, where early detection can significantly impact patient outcomes. Although AI models have shown promising improvements sensitivity specificity, challenges such as algorithmic bias, interpretability, generalizability diverse populations remain. concludes that while transformative collaborative efforts between radiologists, developers, policymakers are crucial ensuring ethical, reliable, inclusive practice.

Language: Английский

Citations

0
Liver Tumor Prediction using Attention-Guided Convolutional Neural Networks and Genomic Feature Analysis DOI Creative Commons

S. Edwin Raja,

J. Sutha,

P Elamparithi

et al.

MethodsX, Journal Year: 2025, Volume and Issue: unknown, P. 103276 - 103276

Published: March 1, 2025

The task of predicting liver tumors is critical as part medical image analysis and genomics area since diagnosis prognosis are important in making correct decisions. Silent characteristics interactions between genomic imaging features also the main sources challenges toward reliable predictions. To overcome these hurdles, this study presents two integrated approaches namely, - Attention-Guided Convolutional Neural Networks (AG-CNNs), Genomic Feature Analysis Module (GFAM). Spatial channel attention mechanisms AG-CNN enable accurate tumor segmentation from CT images while providing detailed morphological profiling. Evaluation with three control databases TCIA, LiTS, CRLM shows that our model produces more output than relevant literature an accuracy 94.5%, a Dice Similarity Coefficient 91.9%, F1-Score 96.2% for Dataset 3. More considerably, proposed methods outperform all other different datasets terms recall, precision, Specificity by up to 10 percent including CELM, CAGS, DM-ML, so on.•Utilization (AG-CNN) enhances region focus accuracy.•Integration (GFAM) identifies molecular markers subtype-specific classification.

Language: Английский

Citations

0
CMLCNet: medical image segmentation network based on convolution capsule encoder and multi-scale local co-occurrence DOI

Chendong Qin,

Yongxiong Wang, Jiapeng Zhang

et al.

Multimedia Systems, Journal Year: 2024, Volume and Issue: 30(4)

Published: July 26, 2024

Language: Английский

Citations

3
Heterogeneous Biomechanical/Mathematical Modeling of Spatial Prediction of Glioblastoma Progression Using Magnetic Resonance Imaging-based Finite Element Method DOI

Mohammad Reza Ghahramani,

Omid Bavi

Computer Methods and Programs in Biomedicine, Journal Year: 2024, Volume and Issue: 257, P. 108441 - 108441

Published: Sept. 24, 2024

Language: Английский

Citations

3