A Deep Learning with Metaheuristic Optimization-Driven Breast Cancer Segmentation and Classification Model using Mammogram Imaging

Engineering Technology & Applied Science Research, Год журнала: 2025, Номер 15(1), С. 20342 - 20347

Опубликована: Фев. 2, 2025

Cancer is the second leading cause of death globally, with Breast (BC) accounting for 20% new diagnoses, making it a major morbidity and mortality. Mammography effective BC detection, but lesion interpretation challenging, prompting development Computer-Aided Diagnosis (CAD) systems to assist in classification detection. Machine Learning (ML) Deep (DL) models are widely used disease diagnosis. Therefore, this study presents an Optimized Graph Convolutional Recurrent Neural Network based Segmentation Recognition Classification (OGCRNN-SBCRC) technique. In preparation phase, images masks annotated then classified as benign or malignant. To achieve this, Wiener Filter (WF)-based noise removal log transform-based contrast enhancement preprocessing. The OGCRNN-SBCRC technique utilizes UNet++ method segmentation RMSProp optimizer parameter tuning. addition, employs ConvNeXtTiny Convolution (CNN) approach feature extraction. For (GCRNN) model used. Finally, Aquila Optimizer (AO) employed hyperparameter tuning GCRNN approach. simulation analysis methodology, using image dataset, demonstrated superior performance accuracy 99.65%, surpassing existing models.

Язык: Английский

---

Integrating sparse graph convolution and capsule networks for superior breast cancer diagnosis

Evolving Systems, Год журнала: 2025, Номер 16(2)

Опубликована: Март 13, 2025

Язык: Английский

---

A convolution and transformer-based method with effective stain normalization for breast cancer detection from whole slide images

Biomedical Signal Processing and Control, Год журнала: 2025, Номер 110, С. 108138 - 108138

Опубликована: Май 26, 2025

Язык: Английский

---

Smart neural network and cognitive computing process for multi task nuclei detection segmentation and classification in breast cancer histopathology images

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

Опубликована: Май 26, 2025

Abstract The detection, segmentation, and differentiation of benign malignant nuclei from the histopathology images is a challenging task for early diagnosis breast cancer. Misinterpretation True Negative (TN) False Positive (FP) can generate incorrect results. proposed Cognitive Computing Process (CCP) detects segments using Deep U-Net with Spatial Attention Mechanisms (SAM) microns-per-pixel measurements to accurately locate assess density. To separate malignant, patches are introduced leverage model’s learning process. Smart Neural Network (SNN) models contain Convolutional (SCNN) (DCNN) reduce Proposed CCP SNN were evaluated BreakHis dataset, which contains 5547 samples at various magnifications (40×, 100×, 200×, 400×). These processed into patches, totaling 11,642, 9282, 9102, 9678 each 224 × pixels. model outperformed state-of-the-art UNet, Residual UNet (ResUNet), Long Short-Term Memory (CNN-LSTM) Dice coefficient 99.90%, an F1-score 99.04%, precision 99.80%, recall 99.76%. process began rate 0.01 decay 0.8, SCNN achieved false negative positive rates 0.04 0.05 low-density 400× 40× magnification, respectively. In contrast, recorded 0.02 0.01. For high-density FN FP 0.0 0.08, while DCNN reported 0.09 0.0. Networks high (77–99%), (75–99%), AUC 86–100%. combination improved accuracy over existing CNN like ResNet50, VGG19, DenseNet109, DenseNet201, VGG16. An ablation study showed p-value 0.00003 based on AUC, highlighting potential enhance automated cancer support clinical decision-making.

Язык: Английский

---

Advanced Analytical Methods for Multi-Spectral Transmission Imaging Optimization: Enhancing Breast Tissue Heterogeneity Detection and Tumor Screening with Hybrid Image Processing and Deep Learning

Analytical Methods, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 15, 2024

This paper combines SPM, M_D-FA, and DLNM to improve multi-spectral image quality classify heterogeneities. Results show significant accuracy enhancements, achieving 95.47% with VGG19 98.47% ResNet101 in breast tumor screening.

Язык: Английский

---