A Hybrid Workflow of Residual Convolutional Transformer Encoder for Breast Cancer Classification Using Digital X-ray Mammograms

Biomedicines, Journal Year: 2022, Volume and Issue: 10(11), P. 2971 - 2971

Published: Nov. 18, 2022

Breast cancer, which attacks the glandular epithelium of breast, is second most common kind cancer in women after lung and it affects a significant number people worldwide. Based on advantages Residual Convolutional Network Transformer Encoder with Multiple Layer Perceptron (MLP), this study proposes novel hybrid deep learning Computer-Aided Diagnosis (CAD) system for breast lesions. While backbone residual network employed to create features, transformer utilized classify according self-attention mechanism. The proposed CAD has capability recognize two scenarios: Scenario A (Binary classification) B (Multi-classification). Data collection preprocessing, patch image creation splitting, artificial intelligence-based lesion identification are all components execution framework that applied consistently across both cases. effectiveness AI model compared against three separate models: custom CNN, VGG16, ResNet50. Two datasets, CBIS-DDSM DDSM, construct test system. Five-fold cross validation data used evaluate accuracy performance results. suggested achieves encouraging evaluation results, overall accuracies 100% 95.80% binary multiclass prediction challenges, respectively. experimental results reveal could identify benign malignant tissues significantly, important radiologists recommend further investigation abnormal mammograms provide optimal treatment plan.

Language: Английский

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Breast Cancer Classification through Meta-Learning Ensemble Technique Using Convolution Neural Networks

Diagnostics, Journal Year: 2023, Volume and Issue: 13(13), P. 2242 - 2242

Published: June 30, 2023

This study aims to develop an efficient and accurate breast cancer classification model using meta-learning approaches multiple convolutional neural networks. Breast Ultrasound Images (BUSI) dataset contains various types of lesions. The goal is classify these lesions as benign or malignant, which crucial for the early detection treatment cancer. problem that traditional machine learning deep often fail accurately images due their complex diverse nature. In this research, address problem, proposed used several advanced techniques, including ensemble technique, transfer learning, data augmentation. Meta-learning will optimize model's process, allowing it adapt new unseen datasets quickly. Transfer leverage pre-trained models such Inception, ResNet50, DenseNet121 enhance feature extraction ability. Data augmentation techniques be applied artificially generate training images, increasing size diversity dataset. Meta combine outputs CNNs, improving accuracy. work investigated by pre-processing BUSI first, then evaluating CNNs different architectures models. Then, a algorithm CNN. Additionally, evaluation results indicate highly effective with high Finally, performance compared state-of-the-art in other existing systems' accuracy, precision, recall, F1 score.

Language: Английский

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Classification Framework for Medical Diagnosis of Brain Tumor with an Effective Hybrid Transfer Learning Model

Diagnostics, Journal Year: 2022, Volume and Issue: 12(10), P. 2541 - 2541

Published: Oct. 20, 2022

Brain tumors (BTs) are deadly diseases that can strike people of every age, all over the world. Every year, thousands die brain tumors. Brain-related diagnoses require caution, and even smallest error in diagnosis have negative repercussions. Medical errors tumor common frequently result higher patient mortality rates. Magnetic resonance imaging (MRI) is widely used for evaluation detection. However, MRI generates large amounts data, making manual segmentation difficult laborious work, limiting use accurate measurements clinical practice. As a result, automated dependable methods required. Automatic early detection tasks computer vision due to their high spatial structural variability. Therefore, or treatment critical. Various traditional Machine learning (ML) techniques been detect various types The main issue with these models features were manually extracted. To address aforementioned insightful issues, this paper presents hybrid deep transfer (GN-AlexNet) model BT tri-classification (pituitary, meningioma, glioma). proposed combines GoogleNet architecture AlexNet by removing five layers adding ten model, which extracts classifies them automatically. On same CE-MRI dataset, was compared (VGG-16, AlexNet, SqeezNet, ResNet, MobileNet-V2) ML/DL. outperformed current terms accuracy sensitivity (accuracy 99.51% 98.90%).

Language: Английский

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A hybrid lightweight breast cancer classification framework using the histopathological images

Journal of Applied Biomedicine, Journal Year: 2023, Volume and Issue: 44(1), P. 31 - 54

Published: Dec. 22, 2023

Language: Английский

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An adaptive matrix-based evolutionary computation framework for EEG feature selection

Memetic Computing, Journal Year: 2025, Volume and Issue: 17(1)

Published: Jan. 13, 2025

Language: Английский

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BCNet: A Deep Learning Computer-Aided Diagnosis Framework for Human Peripheral Blood Cell Identification

Diagnostics, Journal Year: 2022, Volume and Issue: 12(11), P. 2815 - 2815

Published: Nov. 16, 2022

Blood cells carry important information that can be used to represent a person's current state of health. The identification different types blood in timely and precise manner is essential cutting the infection risks people face on daily basis. BCNet an artificial intelligence (AI)-based deep learning (DL) framework was proposed based capability transfer with convolutional neural network rapidly automatically identify eight-class scenario: Basophil, Eosinophil, Erythroblast, Immature Granulocytes, Lymphocyte, Monocyte, Neutrophil, Platelet. For purpose establishing dependability viability BCNet, exhaustive experiments consisting five-fold cross-validation tests are carried out. Using strategy, we conducted in-depth comprehensive BCNet's architecture test it three optimizers ADAM, RMSprop (RMSP), stochastic gradient descent (SGD). Meanwhile, performance directly compared using same dataset state-of-the-art models DensNet, ResNet, Inception, MobileNet. When employing optimizers, demonstrated better classification ADAM RMSP optimizers. best evaluation achieved optimizer terms 98.51% accuracy 96.24% F1-score. Compared baseline model, clearly improved prediction 1.94%, 3.33%, 1.65% RMSP, SGD, respectively. model outperformed AI DenseNet, MobileNet testing time single cell image by 10.98, 4.26, 2.03, 0.21 msec. In comparison most recent models, could able generate encouraging outcomes. It for advancement healthcare facilities have such recognition rate improving detection cells.

Language: Английский

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ETECADx: Ensemble Self-Attention Transformer Encoder for Breast Cancer Diagnosis Using Full-Field Digital X-ray Breast Images

Diagnostics, Journal Year: 2022, Volume and Issue: 13(1), P. 89 - 89

Published: Dec. 28, 2022

Early detection of breast cancer is an essential procedure to reduce the mortality rate among women. In this paper, a new AI-based computer-aided diagnosis (CAD) framework called ETECADx proposed by fusing benefits both ensemble transfer learning convolutional neural networks as well self-attention mechanism vision transformer encoder (ViT). The accurate and precious high-level deep features are generated via backbone network, while used diagnose probabilities in two approaches: Approach A (i.e., binary classification) B multi-classification). To build CAD system, benchmark public multi-class INbreast dataset used. Meanwhile, private real images collected annotated expert radiologists validate prediction performance framework. promising evaluation results achieved using mammograms with overall accuracies 98.58% 97.87% for approaches, respectively. Compared individual networks, model improves 6.6% 4.6% approaches. hybrid shows further improvement when ViT-based network 8.1% 6.2% diagnosis, For validation purposes images, system provides encouraging 97.16% 89.40% has capability predict lesions single mammogram average 0.048 s. Such could be useful helpful assist practical applications providing second supporting opinion distinguishing various malignancies.

Language: Английский

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SNSVM: SqueezeNet-Guided SVM for Breast Cancer Diagnosis

Computers, materials & continua/Computers, materials & continua (Print), Journal Year: 2023, Volume and Issue: 76(2), P. 2201 - 2216

Published: Jan. 1, 2023

Breast cancer is a major public health concern that affects women worldwide. It leading cause of cancer-related deaths among women, and early detection crucial for successful treatment. Unfortunately, breast can often go undetected until it has reached advanced stages, making more difficult to treat. Therefore, there pressing need accurate efficient diagnostic tools detect at an stage. The proposed approach utilizes SqueezeNet with fire modules complex bypass extract informative features from mammography images. extracted are then utilized train support vector machine (SVM) image classification. SqueezeNet-guided SVM model, known as SNSVM, achieved promising results, accuracy 94.10% sensitivity 94.30%. A 10-fold cross-validation was performed ensure the robustness mean standard deviation various performance indicators were calculated across multiple runs. This model also outperforms state-of-the-art models in all indicators, indicating its superior performance. demonstrates effectiveness diagnosis using makes tool diagnosis. may have significant implications reducing mortality rates.

Language: Английский

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A new univariate feature selection algorithm based on the best–worst multi-attribute decision-making method

Decision Analytics Journal, Journal Year: 2023, Volume and Issue: 7, P. 100240 - 100240

Published: May 4, 2023

With the extensive applicability of machine learning classification algorithms to a wide spectrum domains, feature selection (FS) becomes relevant data preprocessing technique due high dimensionality used in these domains. While efforts have been made study various filters for ranking features, scholars paid little attention developing unified framework that can be as an interface any filter. The development such would formalize understanding filter-based FS. This helps put same perspective when analyzing new FS algorithms. proposes based on best–worst multi-attribute decision-making method. proposed algorithm is compared two control groups: (a) no and (b) randomized algorithm. Furthermore, blocking variables are considered: (i) classifier (ii) training dataset. performance classifiers was measured using area under curve (AUC) receiver operating characteristics (ROC) curve. A three-way analysis variance (ANOVA) compare approach groups considering variables. paper offers several contributions literature. For one thing, it few works forward performing To best authors' knowledge, first provide empirical evidence about interaction between factors considered literature evaluating

Language: Английский

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5G-Based Telerobotic Ultrasound System Improves Access to Breast Examination in Rural and Remote Areas: A Prospective and Two-Scenario Study

Diagnostics, Journal Year: 2023, Volume and Issue: 13(3), P. 362 - 362

Published: Jan. 18, 2023

Ultrasound (US) plays an important role in the diagnosis and management of breast diseases; however, effective US screening is lacking rural remote areas. To alleviate this issue, we prospectively evaluated clinical availability 5G-based telerobotic technology for examinations areas.Between September 2020 March 2021, 63 patients underwent conventional a island (Scenario A), while 20 examination mobile car located county B) May 2021. The safety, duration, image quality, consistency, acceptability were assessed.In Scenario A, average duration procedure was longer than that (10.3 ± 3.3 min vs. 7.6 3.0 min, p = 0.017), but their imaging scores similar (4.86 4.90, 0.159). Two cases gynecomastia, one lactation mastitis, postoperative effusion diagnosed 32 nodules detected using two methods. There good interobserver agreement between features BI-RADS categories identical (ICC 0.795-1.000). In B, 65% US. Its 10.1 2.3 score 4.85. Overall, 90.4% willing to choose future, tele-sonologists satisfied with 85.5% examinations.The system feasible providing

Language: Английский

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