Accelerated and Precise Skin Cancer Detection through an Enhanced Machine Learning Pipeline for Improved Diagnostic Accuracy

Results in Engineering, Год журнала: 2025, Номер unknown, С. 104168 - 104168

Опубликована: Янв. 1, 2025

Язык: Английский

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A hybrid framework for colorectal cancer detection and U-Net segmentation using polynetDWTCADx

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Янв. 5, 2025

"PolynetDWTCADx" is a sophisticated hybrid model that was developed to identify and distinguish colorectal cancer. In this study, the CKHK-22 dataset, comprising 24 classes, served as introduction. The proposed method, which combines CNNs, DWTs, SVMs, enhances accuracy of feature extraction classification. study employs DWT optimize enhance two integrated CNN models before classifying them with SVM following systematic procedure. PolynetDWTCADx most effective we evaluated. It capable attaining moderate level recall, well an area under curve (AUC) during testing. testing 92.3%, training 95.0%. This demonstrates distinguishing between noncancerous cancerous lesions in colon. We can also employ semantic segmentation algorithms U-Net architecture accurately segment regions. assessed model's exceptional success segmenting providing precise delineation malignant tissues using its maximal IoU value 0.93, based on intersection over union (IoU) scores. When these techniques are added PolynetDWTCADx, they give doctors detailed visual information needed for diagnosis planning treatment. These very good at finding separating has potential recognition management cancer, underscores.

Язык: Английский

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Optimized deep learning model for comprehensive medical image analysis across multiple modalities

Neurocomputing, Год журнала: 2024, Номер 619, С. 129182 - 129182

Опубликована: Дек. 12, 2024

Язык: Английский

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Colorectal cancer detection with enhanced precision using a hybrid supervised and unsupervised learning approach

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Янв. 25, 2025

The current work introduces the hybrid ensemble framework for detection and segmentation of colorectal cancer. This will incorporate both supervised classification unsupervised clustering methods to present more understandable accurate diagnostic results. method entails several steps with CNN models: ADa-22 AD-22, transformer networks, an SVM classifier, all inbuilt. CVC ClinicDB dataset supports this process, containing 1650 colonoscopy images classified as polyps or non-polyps. best performance in ensembles was done by AD-22 + Transformer model, AUC 0.99, a training accuracy 99.50%, testing 99.00%. group also saw high 97.50% Polyps 99.30% Non-Polyps, together recall 97.80% 98.90% hence performing very well identifying cancerous healthy regions. proposed here uses K-means combination visualisation bounding boxes, thereby improving yielding silhouette score 0.73 cluster configuration. It discusses how combine feature interpretation challenges into medical imaging localization precise malignant A good balance between generalization shall be hyperparameter optimization-heavy learning rates; dropout rates overfitting suppressed effectively. schema treats deficiencies previous approaches, such incorporating CNN-based effective extraction, networks developing attention mechanisms, finally fine decision boundary support vector machine. Further, we refine process via purpose enhancing procedure. Such holistic framework, hence, further boosts results generating outcomes rigorous benchmarking detecting cancer higher reality towards clinical application feasibility.

Язык: Английский

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Advanced LSTM Neural Networks for Predicting Hospital Readmissions in Diabetic Patients

Advances in computational intelligence and robotics book series, Год журнала: 2025, Номер unknown, С. 209 - 230

Опубликована: Фев. 5, 2025

The development of the predictive model for forecasting hospital readmissions among diabetic patients represents a remarkable footstep in applying machine learning techniques. These are responsible enhancing healthcare delivery. Ethical considerations, such as transparent judgment and bias monitoring, must be precisely addressed to uphold fairness convince therapist. proposed utilizes Long Short-Term Memory (LSTM) neural networks. has indicated magnificent accuracy rate 83% during pilot testing, this results 39% reduction 30-day readmission with cost effective enhanced diagnosis solutions. Ongoing research efforts should enhance interpretability, explore new data sources, maintain relevance through evolving architectures methodologies. By addressing these multifaceted challenges comprehensive iterative approach, can potentially revolutionize chronic illness management, leading improved patient outcomes reduced operational costs within systems.

Язык: Английский

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Bone tumors: a systematic review of prevalence, risk determinants, and survival patterns

BMC Cancer, Год журнала: 2025, Номер 25(1)

Опубликована: Фев. 21, 2025

Язык: Английский

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Advanced Liver Tumour Detection Using Optimized YOLOv8 Modules

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2025, Номер 11(2)

Опубликована: Апрель 4, 2025

Health fraternity is invariably challenged with early diagnosis, detection, identification, classification, treatment and convalescence of globally prevalent life-threatening fatal diseases as liver cancer. The detection cancer through medical image processing technique so challenging that an iota deviation conspicuous among healthy tissues, benign tumour malignant tissues a matter wake up call. This work entailed introduction novel, optimized YOLOv8-based model for harnessing the strengths transformer-based feature extraction, global attention mechanisms, advanced aggregation techniques. was subjected to rigorous performance relevant methods messages parameters time again repeated refinements. Eventually, it concluded proposed surpasses all models in extant now terms precision, recall, means average precision (mAP). ascertained by inference drawn from model’s achievement attaining 95.34% 96.49% 97.31% [email protected]. In regard excels differentiating normal cases, tumours, tumours. These innovations represent significant step toward improving accuracy automated diagnosis systems, potential revolutionize clinical workflows enhance patient outcomes.

Язык: Английский

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Deep learning-based computational approach for predicting ncRNAs-disease associations in metaplastic breast cancer diagnosis

BMC Cancer, Год журнала: 2025, Номер 25(1)

Опубликована: Май 6, 2025

Non-coding RNAs (ncRNAs) play a crucial role in breast cancer progression, necessitating advanced computational approaches for precise disease classification. This study introduces Deep Reinforcement Learning (DRL)-based framework predicting ncRNA-disease associations metaplastic (MBC) using multi-dimensional descriptor system (ncRNADS) integrating 550 sequence-based features and 1,150 target gene descriptors (miRDB score ≥ 90). The model achieved 96.20% accuracy, 96.48% precision, 96.10% recall, 96.29% F1-score, outperforming traditional classifiers such as support vector machines (SVM) neural networks. Feature selection optimization reduced dimensionality by 42.5% (4,430 to 2,545 features) while maintaining high demonstrating efficiency. External validation confirmed specificity subtypes (87-96.5% accuracy) minimal cross-reactivity with unrelated diseases like Alzheimer's (8-9% accuracy), ensuring robustness. SHAP analysis identified key sequence motifs (e.g., "UUG") structural free energy (ΔG = - 12.3 kcal/mol) critical predictors, validated PCA (82% variance) t-SNE clustering. Survival TCGA data revealed prognostic significance MALAT1, HOTAIR, NEAT1 (associated poor survival, HR 1.76-2.71) GAS5 (protective effect, 0.60). DRL demonstrated rapid training (0.08 s/epoch) cloud deployment compatibility, underscoring its scalability large-scale applications. These findings establish ncRNA-driven classification cornerstone precision oncology, enabling patient stratification, survival prediction, therapeutic identification MBC.

Язык: Английский

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Enhanced Lung Cancer Survival Prediction Using Semi-Supervised Pseudo-Labeling and Learning from Diverse PET/CT Datasets

Cancers, Год журнала: 2025, Номер 17(2), С. 285 - 285

Опубликована: Янв. 17, 2025

This study explores a semi-supervised learning (SSL), pseudo-labeled strategy using diverse datasets such as head and neck cancer (HNCa) to enhance lung (LCa) survival outcome predictions, analyzing handcrafted deep radiomic features (HRF/DRF) from PET/CT scans with hybrid machine systems (HMLSs). We collected 199 LCa patients both PET CT images, obtained TCIA our local database, alongside 408 HNCa images TCIA. extracted 215 HRFs 1024 DRFs by PySERA 3D autoencoder, respectively, within the ViSERA 1.0.0 software, segmented primary tumors. The supervised (SL) employed an HMLS-PCA connected six classifiers on DRFs. SSL expanded adding cases (labeled Random Forest algorithm) cases, same HMLS techniques. Furthermore, principal component analysis (PCA) linked four prediction algorithms were utilized in hazard ratio analysis. outperformed SL method (p << 0.001), achieving average accuracy of 0.85 ± 0.05 PCA + Multi-Layer Perceptron (MLP), compared 0.69 0.06 for Light Gradient Boosting (LGB). Additionally, Component-wise Survival Analysis DRFs, CT, had C-index 0.80, log rank p-value 0.001, confirmed external testing. Shifting strategies, particularly contexts limited data points, enabling or alone, can significantly achieve high predictive performance.

Язык: Английский

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Multimodal marvels of deep learning in medical diagnosis using image, speech, and text: A comprehensive review of COVID-19 detection

AI Open, Год журнала: 2025, Номер unknown

Опубликована: Янв. 1, 2025

Язык: Английский

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