Automated detection and forecasting of COVID-19 using deep learning techniques: A review

Neurocomputing, Год журнала: 2024, Номер 577, С. 127317 - 127317

Опубликована: Янв. 26, 2024

Язык: Английский

---

Enhancing EfficientNetv2 with global and efficient channel attention mechanisms for accurate MRI-Based brain tumor classification

Cluster Computing, Год журнала: 2024, Номер 27(8), С. 11187 - 11212

Опубликована: Май 20, 2024

Abstract The early and accurate diagnosis of brain tumors is critical for effective treatment planning, with Magnetic Resonance Imaging (MRI) serving as a key tool in the non-invasive examination such conditions. Despite advancements Computer-Aided Diagnosis (CADx) systems powered by deep learning, challenge accurately classifying from MRI scans persists due to high variability tumor appearances subtlety early-stage manifestations. This work introduces novel adaptation EfficientNetv2 architecture, enhanced Global Attention Mechanism (GAM) Efficient Channel (ECA), aimed at overcoming these hurdles. enhancement not only amplifies model’s ability focus on salient features within complex images but also significantly improves classification accuracy tumors. Our approach distinguishes itself meticulously integrating attention mechanisms that systematically enhance feature extraction, thereby achieving superior performance detecting broad spectrum Demonstrated through extensive experiments large public dataset, our model achieves an exceptional high-test 99.76%, setting new benchmark MRI-based classification. Moreover, incorporation Grad-CAM visualization techniques sheds light decision-making process, offering transparent interpretable insights are invaluable clinical assessment. By addressing limitations inherent previous models, this study advances field medical imaging analysis highlights pivotal role enhancing interpretability learning models diagnosis. research sets stage advanced CADx systems, patient care outcomes.

Язык: Английский

---

Exploring the efficacy of multi-flavored feature extraction with radiomics and deep features for prostate cancer grading on mpMRI

BMC Medical Imaging, Год журнала: 2023, Номер 23(1)

Опубликована: Ноя. 22, 2023

Abstract Background The purpose of this study is to investigate the use radiomics and deep features obtained from multiparametric magnetic resonance imaging (mpMRI) for grading prostate cancer. We propose a novel approach called multi-flavored feature extraction or tensor, which combines four mpMRI images using eight different fusion techniques create 52 datasets each patient. evaluate effectiveness in cancer compare it traditional methods. Methods used PROSTATEx-2 dataset consisting 111 patients’ T2W-transverse, T2W-sagittal, DWI, ADC images. merge T2W, images, namely Laplacian Pyramid, Ratio low-pass pyramid, Discrete Wavelet Transform, Dual-Tree Complex Curvelet Fusion, Weighted Principal Component Analysis. Prostate were manually segmented, extracted Pyradiomics library Python. also an Autoencoder extraction. five sets train classifiers: all features, linked with PCA, combination features. processed data, including balancing, standardization, correlation, Least Absolute Shrinkage Selection Operator (LASSO) regression. Finally, we nine classifiers classify Gleason grades. Results Our results show that SVM classifier PCA achieved most promising results, AUC 0.94 balanced accuracy 0.79. Logistic regression performed best when only 0.93 0.76. Gaussian Naive Bayes had lower performance compared other classifiers, while KNN high PCA. Random Forest well achieving Voting showed higher 2 highest performance, 0.95 0.78. Conclusion concludes proposed tensor can be effective method findings suggest may more than alone accurately classifying

Язык: Английский

---

A survey on deep learning models for detection of COVID-19

Neural Computing and Applications, Год журнала: 2023, Номер 35(23), С. 16945 - 16973

Опубликована: Май 27, 2023

Язык: Английский

---

Intelligent healthcare system for IoMT-integrated sonography: Leveraging multi-scale self-guided attention networks and dynamic self-distillation

Internet of Things, Год журнала: 2024, Номер 25, С. 101065 - 101065

Опубликована: Янв. 19, 2024

Язык: Английский

---

Advancing lung cancer diagnosis: Combining 3D auto-encoders and attention mechanisms for CT scan analysis

Journal of X-Ray Science and Technology, Год журнала: 2025, Номер unknown

Опубликована: Янв. 28, 2025

Objective The goal of this study is to assess the effectiveness a hybrid deep learning model that combines 3D Auto-encoders with attention mechanisms detect lung cancer early from CT scan images. aims improve diagnostic accuracy, sensitivity, and specificity by focusing on key features in scans. Materials methods A was developed feature extraction using Auto-encoder networks mechanisms. First, tested without attention, selection techniques such as RFE, LASSO, ANOVA. This followed evaluation several classifiers: SVM, RF, GBM, MLP, LightGBM, XGBoost, Stacking, Voting. model's performance evaluated based F1-Score, AUC-ROC. After that, were added help focus important areas scans, re-assessed. Results achieved an accuracy 93% sensitivity 89%. When added, improved across all metrics. For example, SVM increased 94%, 91%, AUC-ROC 0.96. Random Forest (RF) also showed improvements, rising 94% 0.93. final overall 93.4%, 90.2%, 94.1%. These results highlight role identifying most relevant for accurate classification. Conclusions proposed model, especially addition mechanisms, significantly improves detection cancer. By mechanism helps reduce false negatives boosts accuracy. approach has great potential use clinical applications, particularly early-stage

Язык: Английский

---

Multi-class deep learning architecture for classifying lung diseases from chest X-Ray and CT images

Scientific Reports, Год журнала: 2023, Номер 13(1)

Опубликована: Ноя. 8, 2023

Abstract Medical imaging is considered a suitable alternative testing method for the detection of lung diseases. Many researchers have been working to develop various methods that aided in prevention To better understand condition disease infection, chest X-Ray and CT scans are utilized check disease’s spread throughout lungs. This study proposes an automated system multi diseases scans. A customized convolutional neural network (CNN) two pre-trained deep learning models with new image enhancement model proposed classification. The comprises main steps: pre-processing, algorithm developed pre-processing step using k-symbol Lerch transcendent functions which images based on pixel probability. While, classification step, CNN architecture Alex Net, VGG16Net developed. approach was tested publicly available datasets (CT, dataset), results showed accuracy, sensitivity, specificity 98.60%, 98.40%, 98.50% dataset, respectively, 98.80%, 98.50%, 98.40% respectively. Overall, obtained highlight advantages as first processing.

Язык: Английский

---

Opportunistic AI for enhanced cardiovascular disease risk stratification using abdominal CT

Computerized Medical Imaging and Graphics, Год журнала: 2025, Номер 120, С. 102493 - 102493

Опубликована: Янв. 20, 2025

Язык: Английский

---

FL-W3S: Cross-domain federated learning for weakly supervised semantic segmentation of white blood cells

International Journal of Medical Informatics, Год журнала: 2025, Номер 195, С. 105806 - 105806

Опубликована: Янв. 23, 2025

Segmentation models for clinical data experience severe performance degradation when trained on a single client from one domain and distributed to other clients different domain. Federated Learning (FL) provides solution by enabling multi-party collaborative learning without compromising the confidentiality of clients' private data. In this paper, we propose cross-domain FL method Weakly Supervised Semantic (FL-W3S) white blood cells in microscopic images. We perform model training multiple with distributions obtain global aggregated using only image-level class labels semantic segmentation cells. A multi-class token transformer learns relationship between patch tokens during generates class-specific localization maps mask predictions. To rectify maps, use patch-level pairwise affinity obtained patch-to-patch attention. evaluate proposed two datasets domains. Our experimental results show that datasets, there is 2.56% 1.39% increase over existing state-of-the-art methods. The combination federated while preserving privacy, alongside cell techniques precise identification, enhances diagnostic accuracy personalized treatment strategies applications, particularly hematology pathology. More specifically, it involves isolating smear further analysis such as automated counting, morphological analysis, classification, disease diagnosis monitoring.

Язык: Английский

---

Capsule network approach for monkeypox (CAPSMON) detection and subclassification in medical imaging system

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

Опубликована: Янв. 26, 2025

In response to the pressing need for detection of Monkeypox caused by virus (MPXV), this study introduces Enhanced Spatial-Awareness Capsule Network (ESACN), a architecture designed precise multi-class classification dermatological images. Addressing shortcomings traditional Machine Learning and Deep models, our ESACN model utilizes dynamic routing spatial hierarchy capabilities CapsNets differentiate complex patterns such as those seen in monkeypox, chickenpox, measles, normal skin presentations. CapsNets' inherent ability recognize process crucial relationships within images outperforms conventional CNNs, particularly tasks that require distinction visually similar classes. Our model's superior performance, demonstrated through rigorous evaluation, exhibits significant improvements accuracy, precision, recall, F1 score, even with limited data. The results highlight potential reliable tool enhancing diagnostic accuracy medical settings. case study, was applied dataset comprising 659 across four classes: 178 Monkeypox, 171 Chickenpox, 80 Measles, 230 Normal conditions. This underscores effectiveness real-world applications, providing robust accurate could greatly aid early diagnosis treatment planning clinical environments.

Язык: Английский

---