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

Biomedicines, Год журнала: 2022, Номер 10(11), С. 2971 - 2971

Опубликована: Ноя. 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.

Язык: Английский

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

Diagnostics, Год журнала: 2023, Номер 13(13), С. 2242 - 2242

Опубликована: Июнь 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.

Язык: Английский

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A hybrid lightweight breast cancer classification framework using the histopathological images

Journal of Applied Biomedicine, Год журнала: 2023, Номер 44(1), С. 31 - 54

Опубликована: Дек. 22, 2023

Язык: Английский

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

Memetic Computing, Год журнала: 2025, Номер 17(1)

Опубликована: Янв. 13, 2025

Язык: Английский

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

Diagnostics, Год журнала: 2022, Номер 12(10), С. 2541 - 2541

Опубликована: Окт. 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%).

Язык: Английский

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

Diagnostics, Год журнала: 2022, Номер 13(1), С. 89 - 89

Опубликована: Дек. 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.

Язык: Английский

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

Diagnostics, Год журнала: 2022, Номер 12(11), С. 2815 - 2815

Опубликована: Ноя. 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.

Язык: Английский

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SNSVM: SqueezeNet-Guided SVM for Breast Cancer Diagnosis

Computers, materials & continua/Computers, materials & continua (Print), Год журнала: 2023, Номер 76(2), С. 2201 - 2216

Опубликована: Янв. 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.

Язык: Английский

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

Decision Analytics Journal, Год журнала: 2023, Номер 7, С. 100240 - 100240

Опубликована: Май 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

Язык: Английский

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Analyzing Histological Images Using Hybrid Techniques for Early Detection of Multi-Class Breast Cancer Based on Fusion Features of CNN and Handcrafted

Diagnostics, Год журнала: 2023, Номер 13(10), С. 1753 - 1753

Опубликована: Май 17, 2023

Breast cancer is the second most common type of among women, and it can threaten women's lives if not diagnosed early. There are many methods for detecting breast cancer, but they cannot distinguish between benign malignant tumors. Therefore, a biopsy taken from patient's abnormal tissue an effective way to challenges facing pathologists experts in diagnosing including addition some medical fluids various colors, direction sample, small number doctors their differing opinions. Thus, artificial intelligence techniques solve these help clinicians resolve diagnostic differences. In this study, three techniques, each with systems, were developed diagnose multi binary classes datasets types 40× 400× factors. The first technique dataset using neural network (ANN) selected features VGG-19 ResNet-18. by ANN combined ResNet-18 before after principal component analysis (PCA). third analyzing hybrid features. handcrafted; handcrafted. handcrafted mixed extracted Fuzzy color histogram (FCH), local pattern (LBP), discrete wavelet transform (DWT) gray level co-occurrence matrix (GLCM) methods. With data set, reached precision 95.86%, accuracy 97.3%, sensitivity 96.75%, AUC 99.37%, specificity 99.81% images at magnification factor 400×. Whereas 99.74%, 99.7%, 100%, 99.85%, 100%

Язык: Английский

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