AI in Breast Cancer Imaging: An Update and Future Trends

Seminars in Nuclear Medicine, Год журнала: 2025, Номер unknown

Опубликована: Фев. 1, 2025

Язык: Английский

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An Optimization Numerical Spiking Neural Membrane System with Adaptive Multi-Mutation Operators for Brain Tumor Segmentation

International Journal of Neural Systems, Год журнала: 2024, Номер 34(08)

Опубликована: Март 28, 2024

Magnetic Resonance Imaging (MRI) is an important diagnostic technique for brain tumors due to its ability generate images without tissue damage or skull artifacts. Therefore, MRI are widely used achieve the segmentation of tumors. This paper first attempt discuss use optimization spiking neural P systems improve threshold tumor images. To be specific, a approach based on numerical with adaptive multi-mutation operators (ONSNPSamos) proposed segment More specifically, ONSNPSamo strategy introduced balance exploration and exploitation abilities. At same time, combining connectivity algorithms address problem. Our experimental results from CEC 2017 benchmarks (basic, shifted rotated, hybrid, composition function problems) demonstrate that better than close 12 algorithms. Furthermore, case studies BraTS 2019 show can more effectively most involved.

Язык: Английский

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Cloud and IoT based Smart Agent-driven Simulation of Human Gait for Detecting Muscles Disorder

Heliyon, Год журнала: 2025, Номер 11(2), С. e42119 - e42119

Опубликована: Янв. 1, 2025

Motion disorders affect a significant portion of the global population. While some symptoms can be managed with medications, these treatments often impact all muscles uniformly, not just affected ones, leading to potential side effects including involuntary movements, confusion, and decreased short-term memory. Currently, there is no dedicated application for differentiating healthy from abnormal ones. Existing analysis applications, designed other purposes, lack essential software engineering features such as user-friendly interface, infrastructure independence, usability learning ability, cloud computing capabilities, AI-based assistance. This research proposes computer-based methodology analyze human motion differentiate between unhealthy muscles. First, an IoT-based approach proposed digitize using smartphones instead hardly accessible wearable sensors markers. The data then simulated neuromusculoskeletal system. An agent-driven modeling method ensures naturalness, accuracy, interpretability simulation, incorporating neuromuscular details Henneman's size principle, action potentials, motor units, biomechanical principles. results are provided medical clinical experts aid in further investigation. Additionally, deep learning-based ensemble framework assist simulation results, offering both accuracy interpretability. A graphical interface enhances application's usability. Being fully cloud-based, infrastructure-independent accessed on smartphones, PCs, devices without installation. strategy only addresses current challenges treating but also paves way simulations by considering scientific computational requirements.

Язык: Английский

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Artificial intelligence in pathological anatomy: digitization of the calculation of the proliferation index (Ki-67) in breast carcinoma

Artificial Life and Robotics, Год журнала: 2024, Номер 29(1), С. 177 - 186

Опубликована: Янв. 6, 2024

Язык: Английский

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Fast and Efficient Lung Abnormality Identification With Explainable AI: A Comprehensive Framework for Chest CT Scan and X-Ray Images

IEEE Access, Год журнала: 2024, Номер 12, С. 31117 - 31135

Опубликована: Янв. 1, 2024

A novel automated multi-classification approach is proposed for the anticipation of lung abnormalities using chest X-ray and CT images. The study leverages a publicly accessible dataset with an insufficient unbalanced number images, addressing this issue by employing data augmentation DCGAN to balance dataset. Various preprocessing procedures are applied improve features reduce noise in pictures. As base model, vision trans-former convolution-based compact convolutional transformer (CCT) model utilized. To determine best configuration, ablation performed on original CCT scan image dimensions 32 x 32. Following that, trained evaluate performance entirely other modality. performances compared six pre-trained models 32x32 While traditional achieved modest performance, test accuracies ranging from 43% 77% 49% 73% requiring lengthy training times, suggested exceptionally well, obtaining 99.77% 95.37% X-ray, respectively short duration 10-12 40-42 seconds/epoch. Robustness demonstrated through progressive reduction findings indicating that maintains good even reduced An explainable AI technique Grad-CAM used explain model's judgment. Grad-CAM-based color visualization shown assessments help health specialists make quick, confident decisions. This deep learning techniques detect anomalies, it addressed challenges time computational complexity.

Язык: Английский

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Spectrum is a picture: Feasibility study of two-dimensional convolutional neural networks in spectral processing

Microchemical Journal, Год журнала: 2024, Номер 205, С. 111329 - 111329

Опубликована: Авг. 3, 2024

Язык: Английский

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A Dual Approach with Grad-CAM and Layer-Wise Relevance Propagation for CNN Models Explainability

Communications in computer and information science, Год журнала: 2025, Номер unknown, С. 116 - 129

Опубликована: Янв. 1, 2025

Язык: Английский

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Facial Recognition Algorithms: A Systematic Literature Review

Journal of Imaging, Год журнала: 2025, Номер 11(2), С. 58 - 58

Опубликована: Фев. 13, 2025

This systematic literature review aims to understand new developments and challenges in facial recognition technology. will provide an understanding of the system principles, performance metrics, applications technology various fields such as health, society, security from academic publications, conferences, industry news. A comprehensive approach was adopted technologies. It emphasizes most important techniques algorithm development, examines explores their fields. The mainly recent development deep learning techniques, especially CNNs, which greatly improved accuracy efficiency systems. findings reveal that there has been a noticeable evolution technology, with current use techniques. Nevertheless, it highlights challenges, including privacy concerns, ethical dilemmas, biases These factors highlight necessity using regulated manner. In conclusion, paper proposes several future research directions establish reliability systems reduce while building user confidence. considerations are key responsibly advancing by ensuring practices safeguarding privacy.

Язык: Английский

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Advancing Breast Cancer Diagnosis: Integrating Deep Transfer Learning and U-Net Segmentation for Precise Classification and Delineation of Ultrasound Images

Results in Engineering, Год журнала: 2025, Номер unknown, С. 105047 - 105047

Опубликована: Апрель 1, 2025

Язык: Английский

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Res-ECA-UNet++: an automatic segmentation model for ovarian tumor ultrasound images based on residual networks and channel attention mechanism

Frontiers in Medicine, Год журнала: 2025, Номер 12

Опубликована: Май 21, 2025

Objective Ultrasound imaging has emerged as the preferred modality for ovarian tumor screening due to its non-invasive nature and real-time dynamic capabilities. However, in many developing countries, ultrasound diagnosis remains dependent on specialist physicians, where shortage of skilled professionals relatively low accuracy manual diagnoses significantly constrain efficiency. Although deep learning achieved remarkable progress medical image segmentation recent years, existing methods still face challenges segmentation, including insufficient robustness, imprecise boundary delineation, dependence high-performance hardware facilities. This study proposes a learning-based automatic model, Res-ECA-UNet++, designed enhance while alleviating strain limited healthcare resources. Methods The Res-ECA-UNet++ model employs UNet++ fundamental architecture with ResNet34 serving backbone network. To effectively address vanishing gradient problem networks, residual modules are incorporated into skip connections between encoding decoding processes. integration enhances feature extraction efficiency improving stability generalization Furthermore, ECA-Net channel attention mechanism is introduced during downsampling phase. adaptively emphasizes region-related information through global recalibration, thereby recognition localization precision areas. Results Based clinical datasets tumors, experimental results demonstrate that achieves outstanding performance validation, Dice coefficient 95.63%, mean Intersection over Union (mIoU) 91.84%, 99.75%. Compared baseline UNet, improves these three metrics by 0.45, 4.42, 1.57%, respectively. Comparative analyses ROC curves AUC values further indicate exhibits superior enhanced capabilities test set. In terms computational efficiency, inference time meets requirements both high-end low-end hardware, demonstrating suitability deployment resource-constrained devices. Additionally, comparative experiments public OTU2D dataset validate model’s performance, highlighting strong potential practical applications. Conclusion proposed demonstrates exceptional robustness images, application. Its ability aid clinicians underscores broad prospects implementation. Future research will focus optimizing improve adaptability diverse pathological types characteristics, expanding diagnostic utility.

Язык: Английский

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