A Novel Deep Learning Architecture for Breast Cancer Segmentation and Classification Using Spatial and Temporal Features

Algorithms for intelligent systems, Год журнала: 2025, Номер unknown, С. 179 - 192

Опубликована: Янв. 1, 2025

Язык: Английский

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Malignancy pattern analysis of breast ultrasound images using clinical features and a graph convolutional network

Digital Health, Год журнала: 2024, Номер 10

Опубликована: Янв. 1, 2024

Early diagnosis of breast cancer can lead to effective treatment, possibly increase long-term survival rates, and improve quality life. The objective this study is present an automated analysis classification system for using clinical markers such as tumor shape, orientation, margin, surrounding tissue. novelty uniqueness the lie in approach considering medical features based on radiologists.

Язык: Английский

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Evaluating the effect of tissue stimulation at different frequencies on breast lesion classification based on nonlinear features using a novel radio frequency time series approach

Heliyon, Год журнала: 2024, Номер 10(13), С. e33133 - e33133

Опубликована: Июнь 20, 2024

ObjectiveRadio Frequency Time Series (RF TS) is a cutting-edge ultrasound approach in tissue typing. The RF TS does not provide dynamic insights into the propagation medium; when and probe are fixed. We previously proposed innovative RFTSDP method which data recorded while stimulating tissue. Applying stimulation can unveil mechanical characteristics of echo.Materials MethodsIn this study, an apparatus was developed to induce vibrations at different frequencies medium. Data were collected from four PVA phantoms simulating nonlinear behaviors healthy, fibroadenoma, cyst, cancerous breast tissues. Raw focused, raw, beamformed ultrafast under conditions no stimulation, constant force, various vibrational stimulations using Supersonic Imagine Aixplorer clinical/research imaging system. domain (TD), spectral, features extracted each TS. Support Vector Machine (SVM), Random Forest, Decision Tree algorithms employed for classification.ResultsThe optimal outcome achieved SVM classifier considering 19 applying vibration frequency 65 Hz. classification accuracy, specificity, precision 98.44 0.20%, 99.49 0.01%, 98.53 0.04%, respectively. RFTSDP, notable 24.45% improvement accuracy observed compared case fixed assessing raw focused data.ConclusionsExternal appropriate frequency, as applied incorporates beneficial information about medium its TS, improve characterization.

Язык: Английский

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Innovative breast cancer detection using a segmentation-guided ensemble classification framework

Biomedical Engineering Letters, Год журнала: 2024, Номер 15(1), С. 179 - 191

Опубликована: Окт. 18, 2024

Язык: Английский

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Detection of explosives in dustbins using deep transfer learning based multiclass classifiers

Applied Intelligence, Год журнала: 2024, Номер 54(2), С. 2314 - 2347

Опубликована: Янв. 1, 2024

Язык: Английский

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Spectrum is a Picture: Feasibility Study of Two-Dimensional Convolutional Neural Networks in Spectral Processing

Опубликована: Янв. 1, 2024

In spectral data processing, a spectrum is usually represented in form of numeric vector for further processing. The same approach has been used also treatment by convolutional neural networks (CNN), initially purposed, however, image Analyzing as two-dimensional picture rather than one-dimensional potentially can improve the accuracy regression and classification models. purpose this work was to test assumption. We explored potential 2D-CNN with two case studies: Mössbauer - predict parameters numerically using (.bmp) files spectra near-infrared biological tissues. compared performance CNN types input data: pictures vectors. results indicate that proposed be helpful certain cases.

Язык: Английский

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PalScDiff: A diffusion-based framework with progressive augmentation learning and semantic consistency for breast ultrasound tumor segmentation

Journal of Intelligent & Fuzzy Systems, Год журнала: 2024, Номер unknown, С. 1 - 15

Опубликована: Март 23, 2024

Background: Breast cancer diagnosis relies on accurate lesion segmentation in medical images. Automated computer-aided reduces clinician workload and improves efficiency, but existing image methods face challenges model performance generalization. Objective: This study aims to develop a generative framework using denoising diffusion for efficient breast Methods: We design novel framework, PalScDiff, that leverages probabilistic reconstruct the label distribution images, thereby enabling sampling of diverse, plausible outcomes. Specifically, with condition corresponding image, PalScDiff learns estimate masses region probability through step by step. Furthermore, we Progressive Augmentation Learning strategy incrementally handle irregular blurred tumors. Moreover, multi-round is employed achieve robust mass segmentation. Results: Our experimental results show outperforms established models such as U-Net transformer-based alternatives, achieving an accuracy 95.15%, precision 79.74%, Dice coefficient 77.61%, Intersection over Union (IOU) 81.51% . Conclusion: The proposed demonstrates promising capabilities cancer.

Язык: Английский

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EAH-Net: A Novel Ensemble Attention-Based Hybrid Architecture for Breast Cancer Diagnosis Utilizing Ultrasound Images

Опубликована: Окт. 28, 2024

Breast cancer is a complex and often fatal malignancy in women worldwide, requiring thorough medical examinations. Accurately detecting breast challenging due to its diverse forms, stages, symptoms, diagnostic techniques. With advancements artificial intelligence, an automated computerized method can potentially aid radiologists the early detection of cancer. This study presents novel robust deep neural network, EAH-Net, for diagnosis using ultrasound images. The EAH-Net architecture comprises ensemble attention module, modified UNet model that performs segmentation by isolating regions interest, hybrid approach classify cancers accurately. Besides, we employed explainable AI techniques highlight most significant regions, assisting making more informed decisions. proposed framework yields promising outcomes across Jaccard, Precision, Recall, Specificity, Dice metrics, averaging 89.26 ± 0.36, 91.79 1.13, 92.98 1.08, 99.38 0.35, 95.26 0.45 percents, respectively. classification demonstrates outstanding performance with accuracy 98.48 0.18%. Overall, offers reliable computer-aided solution diagnosis.

Язык: Английский

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Integrating VGG 19 U‐Net for Breast Thermogram Segmentation and Hybrid Enhancement With Optimized Classifier Selection: A Novel Approach to Breast Cancer Diagnosis

International Journal of Imaging Systems and Technology, Год журнала: 2024, Номер 34(6)

Опубликована: Ноя. 1, 2024

ABSTRACT Early diagnosis of breast cancer is essential for improving patient survival rates and reducing treatment costs. Despite thermogram images having high quality, doctors in developing countries often struggle with early due to difficulties interpreting subtle details. Implementing a Computer‐Aided Diagnosis (CAD) system can assist accurately analyzing these This article presents an innovative approach using thermal images. The proposed method enhances the quality clarity relevant features while preserving sharp curved edges through U‐Net‐based segmentation automatic selection ROI, advanced hybrid image enhancement techniques, machine learning classifier. Subjective analysis compares processed five conventional demonstrating efficiency method. quantitative further validates effectiveness against methods four measures. achieves superior performance PSNR 15.27 normal 14.31 malignant images, AMBE 6.594 7.46 SSIM 0.829 0.80 DSSIM 0.084 0.14 classification phase evaluates classifiers 13 from three categories. Random Forest (RF) classifier Discrete Wavelet Transform (DWT) based initially outperformed other but had limited performance, accuracy, sensitivity specificity 81.8%, 88.8%, 91%, respectively. To improve this, categories were normalized converted into two principal components Principal Component Analysis (PCA) train RF classifier, which then showed 97.7% 96.5% sensitivity, 98.2% specificity. dataset utilized this obtained Indira Gandhi Centre Atomic Research (IGCAR), Kalpakkam, India. entire model implemented Jupyter notebook.

Язык: Английский

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AI-powered precision: breast carcinoma diagnosis through digital proliferation index (Ki-67) assessment in pathological anatomy

Data Technologies and Applications, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 6, 2024

Purpose The primary objective of the study is to enhance accuracy and efficiency assessing proliferation index in cancer cells, specifically focusing on role Ki-67. purpose address limitations traditional visual assessments conducted by pathologists integrating AI technologies, particularly deep learning. By accurately computing percentage Ki-67-labeled research aims streamline diagnostic process, reduce subjectivity contribute advancement precision pathological anatomy. Design/methodology/approach employs a methodological approach that integrates Ki-67, non-histone nuclear protein, as vital biomarker for proliferative status cells. Given challenges associated with pathologists, including inter- intra-observer variability time-consuming efforts, adopts novel methodology leveraging artificial intelligence (AI) solutions. Deep learning applied precisely calculate process involves delineating tumor area at x40 magnification, enabling segmentation various cell types (positive, negative tumor-infiltrating lymphocytes). subsequent calculation enhances minimizes process. Findings Despite inherent errors, findings indicate model surpasses existing benchmarks, showcasing superior terms average error measurement. comparison diverse datasets benchmarking against pathologists’ diagnoses contributes empirical evidence support effectiveness AI-based These signify noteworthy methodologies reinforce potential technologies improving diagnostics within realm Originality/value field introducing an innovative combines Ki-67 improved precision. originality lies utilization labeled mitigating manual assessments. validation demonstrates its accuracy, highlighting value anatomy enhanced outcomes. represents significant stride original research, offering insights pursuit more precise diagnostics.

Язык: Английский

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