Biomedical Imaging Research Articles

Breast Cancer Tumour Masks Segmentation Using Modified U-Net with Channel Attention

Abstract: A modified U-Net for biomedical image segmentation with channel attention is developed for breast cancer image segmentation. A unique step in biomedical image analysis is segmentation because it determines the accuracy of image analysis algorithms. Algorithms for accurate and early breast cancer detection in women using mammograms depend highly on the accuracy of the segmentation stage. In this thesis, in order to improve on the segmentation ability of the traditional UNet architecture, it was modified by varying the fixed kernel sizes that it was originally designed for. In this model, progressively increasing kernel sizes of 3x3, 5x5, 7x7, 9x9 and 11x11 were used, which is different from the architecture of UNet that depended on fixed 3x3 kernel sizes for all convolutional layers. The first Convolutional block of the proposed model contains two Conv2D layers with 3x3 kernel sizes, ReLU (Rectified Linear Unit) activation function, and 16 channel dimensions. A maxpooling2D layer with pool size of (2x2) was placed to downsample the image size before feeding to the next convolutional block which used the same activation function, with 32 channel dimension and 5x5 kernel size. The next convolutional block used 64 channel dimensions with 7x7 kernel size, then a 9x9 kernel size Conv2D block with 128 channel dimensions was placed before the Conv2D with upsampling. The squeeze and excitation block was placed after this convolutional layer and in-between the two convolutional blocks with 11x11 kernel sizes, to reallocate the weight of the essential features to ensure that more discriminate features are learnt from the breast cancer images to improve the overall representational strength of the network through the performance of dynamic feature recalibration of the image channels. This significantly improved the performance of the segmentation by explicitly modeling the interdependencies between channels in the convolutional layers. The increasing kernel sizes enables the model to learn more discriminate features from the images. Jaccard similarity index, F1 score, Recall, Precision, F2 score, and accuracy results were used to compare the result of the proposed model with other models such as U-Net model and Baseline model. The results shows a better performance of 0.88 for Jaccard similarity index, 0.94 for F1 score, 0.93 for recall, 0.92 for precision, 0.92 for F2 score, and an accuracy score of 0.98%. The model is good for biomedical image segmentation.

Terahertz generation of Airy beam with polarization conversion in a reflective vanadium dioxide metasurface

Abstract Airy beam possesses a wide range of applications owing to its special properties of non-diffraction, self-accelerating, and self-healing. However, conventional Airy beam generators are not conducive to their miniaturization or integration. In this paper, a technique for generating Airy beam is introduced with polarization conversion within the frequency range of 0.9-1.2 THz based on a reflective vanadium dioxide (VO2) metasurface. Our design is composed of VO2 elliptical patch, SiO2 spacer, and gold substrate. When the metallic VO2 is involved, the proposed metasurface can efficiently generate cross-polarized Airy beam since amplitude and phase distributions of reflected wave are modulated simultaneously by rotating tilt angle of each VO2 patch. Conversely, when the insulating VO2 is involved, the metasurface does not generate Airy beam. In addition, we demonstrate broadband effect and autofocusing phenomenon of the metasurface. The proposed Airy beam generator may be of great interest for beam shaping, biomedical imaging, and laser fabrication.

Research on Human Lung Impedance Tomography Based on Soft Thresholding Image Segmentation and Reduced-Order Tikhonov Regularization

The lung is one of the most vital organs in the human body, and its condition is closely correlated with overall health. Electrical impedance tomography (EIT), as a biomedical imaging technique, often produces low-quality reconstructed images due to its inherent ill-posedness in solving the inverse problem. To address this issue, this paper proposes a soft-threshold region segmentation algorithm with a relaxation factor. This algorithm segments the reconstructed lung images into internal regions, edge regions, and background regions, resulting in clearer boundaries in the reconstructed images. This facilitates the intuitive identification of regions of interest by healthcare professionals. Additionally, this segmentation algorithm is suitably combined with a dimension-reduced Tikhonov regularization algorithm. By utilizing the joint capabilities of these algorithms, the partition points belonging to the background region can be excluded from the sought grayscale vector, thereby improving the ill-posedness of the image reconstruction process and enhancing the quality of image reconstruction. Finally, a 16-electrode human lung EIT simulation model is established for the thoracic region and verified through simulation. Experimental validation is conducted using a human lung tank simulation platform to further demonstrate the effectiveness of the proposed method.

Cobalt(III) Halide Metal-Organic Frameworks Drive Catalytic Halogen Exchange.

The selective halogenation of complex (hetero)aromatic systems is a critical yet challenging transformation that is relevant to medicinal chemistry, agriculture, and biomedical imaging. However, current methods are limited by toxic reagents, expensive homogeneous second- and third-row transition metal catalysts, or poor substrate tolerance. Herein, we demonstrate that porous metal-organic frameworks (MOFs) containing terminal Co(III) halide sites represent a rare and general class of heterogeneous catalysts for the controlled installation of chlorine and fluorine centers into electron-deficient (hetero)aryl bromides using simple metal halide salts. Mechanistic studies support that these halogen exchange (halex) reactions proceed via redox-neutral nucleophilic aromatic substitution (SNAr) at the Co(III) sites. The MOF-based halex catalysts are recyclable, enable green halogenation with minimal waste generation, and facilitate halex in a continuous flow. Our findings represent the first example of SNAr catalysis using MOFs, expanding the lexicon of synthetic transformations enabled by these materials.

Automatic design of W-operators using membership functions: a case study in brain MRI segmentation

AbstractA W-operator is an image transformation that is locally defined inside a window W, invariant to translations. The automatic design of the W-operators consists of the design of functions, whose domain is a set of patterns or vectors obtained by translating a window through training images and the output of each vector is a class or label. The main difficulty to consider when designing W-operators is the generalization problem that occurs due to lack of training images. In this work, we propose the use of membership functions to solve the generalization problem in gray level images. Membership functions are defined from the training images to model regions that are often inaccurate due to ambiguous gray levels in the images. This proposal was applied to brain magnetic resonance image segmentation to test its performance in a field of interest in biomedical images. The experiments were carried out with different numbers of training and test images, windows sizes of $$3\times 3$$ 3 × 3 , $$5\times 5$$ 5 × 5 , $$7\times 7$$ 7 × 7 , $$11\times 11$$ 11 × 11 , and $$15\times 15$$ 15 × 15 , and images with noise levels at 0, 1, 3, 5, 7, and 9$$\%$$ % . To calculate the performance of each designed W-operator, the classification error, sensitivity, and specificity were used. From the experimental results, it was concluded that the best performance is achieved with a window of size $$3\times 3$$ 3 × 3 . In images with noise levels from 1 to 5$$\%$$ % , the classification error is less than 4$$\%$$ % and the sensitivity and specificity are greater than 94 and 98$$\%$$ % , respectively.

Role of Bridging Groups in Regulating the Luminescence and Charge Transfer Properties of Thermally Activated Delayed Fluorescence Molecules: A Theoretical Perspective.

Organic emitters with a simultaneous combination of aggregation-induced emission (AIE) and thermally activated delayed fluorescence (TADF) characteristics are in great demand due to their excellent comprehensive performances toward efficient organic light-emitting diodes (OLEDs), biomedical imaging, and the telecommunications field. However, the development of efficient AIE-TADF materials remains a substantial challenge. In this work, light-emitting properties of two AIE-TADF molecules with different bridging groups ICz-BP and ICz-DPS are theoretically investigated in the solid state with the combined quantum mechanics/molecular mechanics (QM/MM) method and the thermal vibration correlation function (TVCF) theory. The research indicates that the C═O bridging bond in ICz-BP is more favorable than the S═O bridging bond in ICz-DPS for enhancing the planarity of the acceptor, increasing conjugation, and thereby elevating the transition dipole moment density. Simultaneously, the stacking pattern of ICz-BP in the solid facilitates a reduction in energy gap between S1 and T1 (ΔEST), achieving rapid reverse intersystem crossing rate (kRISC). Furthermore, compared to toluene, the stacking patterns of ICz-BP and ICz-DPS in the solid effectively suppress the out-of-plane wagging vibration of the acceptor, thereby inhibiting the loss of nonradiative energy in the excited state and realizing aggregation-induced emission. Moreover, the charge transport properties of both electrons and holes in ICz-BP are found to be higher than the corresponding rates in ICz-DPS, attributed to the smaller internal reorganization energy of ICz-BP in the solid state. Additionally, the calculations reveal a more balanced charge transport characteristic in ICz-BP, contributing to efficient exciton recombination and emission and ultimately mitigating efficiency roll-off. Based on these computational results, we aim to unveil the relationship between molecular structure and light-emitting properties, aiding in the design and development of efficient AIE-TADF devices.

Diagnosis and Prognosis of Spinal Fractures and Neurovascular Outcomes with Biomedical Imaging and Machine Learning (P3-14.017)

Diagnosis and Prognosis of Spinal Fractures and Neurovascular Outcomes with Biomedical Imaging and Machine Learning (P3-14.017)

Complex transmission matrix retrieval for a highly scattering medium via regional phase differentiation

Accurately measuring the complex transmission matrix (CTM) of the scattering medium (SM) holds critical significance for applications in anti-scattering optical imaging, phototherapy, and optical neural networks. Non-interferometric approaches, utilizing phase retrieval algorithms, can robustly extract the CTM from the speckle patterns formed by multiple probing fields traversing the SM. However, in cases where an amplitude-type spatial light modulator is employed for probing field modulation, the absence of phase control frequently results in the convergence towards a local optimum, undermining the measurement accuracy. Here, we propose a high-accuracy CTM retrieval (CTMR) approach based on regional phase differentiation (RPD). It incorporates a sequence of additional phase masks into the probing fields, imposing a priori constraints on the phase retrieval algorithms. By distinguishing the variance of speckle patterns produced by different phase masks, the RPD-CTMR can effectively direct the algorithm towards a solution that closely approximates the CTM of the SM. We built a prototype of a digital micromirror device modulated RPD-CTMR. By accurately measuring the CTM of diffusers, we achieved an enhancement in the peak-to-background ratio of anti-scattering focusing by a factor of 3.6, alongside a reduction in the bit error rate of anti-scattering image transmission by a factor of 24. Our proposed approach aims to facilitate precise modulation of scattered optical fields, thereby fostering advancements in diverse fields including high-resolution microscopy, biomedical optical imaging, and optical communications.

Exceptional-point-enhanced phase sensing.

Optical sensors, crucial in diverse fields like gravitational wave detection, biomedical imaging, and structural health monitoring, rely on optical phase to convey valuable information. Enhancing sensitivity is important for detecting weak signals. Exceptional points (EPs), identified in non-Hermitian systems, offer great potential for advanced sensors, given their marked response to perturbations. However, strict physical requirements for operating a sensor at EPs limit broader applications. Here, we introduce an EP-enhanced sensing platform featuring plug-in external sensors separated from an EP control unit. EPs are achieved without modifying the sensor, solely through control-unit adjustments. This configuration converts and amplifies optical phase changes into quantifiable spectral features. By separating sensing and control functions, we expand the applicability of EP enhancement to various conventional sensors. As a proof-of-concept, we demonstrate a sixfold reduction in the detection limit of fiber-optic strain sensing using this configuration. This work establishes a universal platform for applying EP enhancement to diverse phase-dependent structures, promising ultrahigh-sensitivity sensing across various applications.

Are deep learning classification results obtained on CT scans fair and interpretable?

Following the great success of various deep learning methods in image and object classification, the biomedical image processing society is also overwhelmed with their applications to various automatic diagnosis cases. Unfortunately, most of the deep learning-based classification attempts in the literature solely focus on the aim of extreme accuracy scores, without considering interpretability, or patient-wise separation of training and test data. For example, most lung nodule classification papers using deep learning randomly shuffle data and split it into training, validation, and test sets, causing certain images from the Computed Tomography (CT) scan of a person to be in the training set, while other images of the same person to be in the validation or testing image sets. This can result in reporting misleading accuracy rates and the learning of irrelevant features, ultimately reducing the real-life usability of these models. When the deep neural networks trained on the traditional, unfair data shuffling method are challenged with new patient images, it is observed that the trained models perform poorly. In contrast, deep neural networks trained with strict patient-level separation maintain their accuracy rates even when new patient images are tested. Heat map visualizations of the activations of the deep neural networks trained with strict patient-level separation indicate a higher degree of focus on the relevant nodules. We argue that the research question posed in the title has a positive answer only if the deep neural networks are trained with images of patients that are strictly isolated from the validation and testing patient sets.

Exploring the Regulatory Mechanism of ZnS Materials under Stress and Re Doping: Insights from Density Functional Theory

The application prospects of ZnS stress luminescent materials cover many fields such as high‐precision structural monitoring, smart materials, biomedical imaging, and new sensor technologies, which bring broad application prospects and significance to them in the fields of engineering, medicine, and scientific research. In this article, the electronic structure and optical properties of ZnS materials are successfully regulated by applying pressure and doping rare earth metals (Re), and it is found that the regulation of the luminescence properties of ZnS is the result of stress and doping interactions. Specifically, when pressure is applied or Re metal doping, the lattice structure is deformed and the atomic spacing is adjusted, which affects the electronic energy level distribution and optical properties of ZnS materials. Computational analysis of density functional theory (DFT) reveals the microscopic mechanisms behind these changes, including changes in lattice parameters, adjustment of bond length, and changes in band structure. This study provides theoretical guidance for the design and synthesis of high‐performance and high‐stability ZnS light‐emitting materials, and is of great significance for expanding the application of ZnS in the field of lasers and sensors.

Oxygenation heterogeneity facilitates spatiotemporal flow pattern visualization inside human blood vessels using photoacoustic computed tomography

Hemodynamics can be explored through various biomedical imaging techniques. However, observing transient spatiotemporal variations in the saturation of oxygen (sO2) within human blood vessels proves challenging with conventional methods. In this study, we employed photoacoustic computed tomography (PACT) to reconstruct the evolving spatiotemporal patterns in a human vein. Through analysis of the multi-wavelength photoacoustic (PA) spectrum, we illustrated the dynamic distribution within blood vessels. Additionally, we computationally rendered the dynamic process of venous blood flowing into the major vein and entering a branching vessel. Notably, we successfully recovered, in real time, the parabolic wavefront profile of laminar flow inside a deep vein in vivo—a first-time achievement. While the study is preliminary, the demonstrated capability of dynamic sO2 imaging holds promise for new applications in biology and medicine.

METHOD OF GENERATIVE-ADVERSARIAL NETWORKS SEARCHING ARCHITECTURES FOR BIOMEDICAL IMAGES SYNTHESIS

Context. The article examines the problem of automatic design of architectures of generative-adversarial networks. Generativeadversarial networks are used for image synthesis. This is especially true for the synthesis of biomedical images – cytological and histological, which are used to make a diagnosis in oncology. The synthesized images are used to train convolutional neural networks. Convolutional neural networks are currently among the most accurate classifiers of biomedical images.
 Objective. The aim of the work is to develop an automatic method for searching for architectures of generative-adversarial networks based on a genetic algorithm.
 Method. The developed method consists of the stage of searching for the architecture of the generator with a fixed discriminator and the stage of searching for the architecture of the discriminator with the best generator.
 At the first stage, a fixed discriminator architecture is defined and a generator is searched for. Accordingly, after the first step, the architecture of the best generator is obtained, i.e. the model with the lowest FID value.
 At the second stage, the best generator architecture was used and a search for the discriminator architecture was carried out. At each cycle of the optimization algorithm, a population of discriminators is created. After the second step, the architecture of the generative-adversarial network is obtained.
 Results. Cytological images of breast cancer on the Zenodo platform were used to conduct the experiments. As a result of the study, an automatic method for searching for architectures of generatively adversarial networks has been developed. On the basis of computer experiments, the architecture of a generative adversarial network for the synthesis of cytological images was obtained. The total time of the experiment was ~39.5 GPU hours. As a result, 16,000 images were synthesized (4000 for each class). To assess the quality of synthesized images, the FID metric was used.The results of the experiments showed that the developed architecture is the best. The network’s FID value is 3.39. This result is the best compared to well-known generative adversarial networks.
 Conclusions. The article develops a method for searching for architectures of generative-adversarial networks for the problems of synthesis of biomedical images. In addition, a software module for the synthesis of biomedical images has been developed, which can be used to train CNN.

Photoluminescence tuning of terbium tris-1,1,1-trifluoro-5,5-dimethyl-2,4-hexanedione complexes: Synthesis, spectroscopic, thermal and electrochemical analyses

The set of four ternary coordination compounds of terbium were prepared with diketone 1,1,1-trifluoro-5,5-dimethyl-2,4-hexanedione (TFDH) and four different neutral ligands (L) having an overall formula [Tb(TFDH)3L]. Resulting compounds were characterized using different techniques including NUV absorption studies, photoluminescence (PL) spectroscopy, electrochemical and thermal analyses. PL spectra confirmed the green luminescence of synthesized complexes by giving characteristic dominant 5D4 → 7F5 peak around 545 nm. Electronic and optical energy gap values lying ∼3.5 eV reveals their potential to use in semiconductor materials. All the spectral results are in good harmony with each other and highlight their functionality in lighting, biomedical imaging and sensors etc.

Circuit design of a novel front readout circuit for SiC neutron detector with leakage current compensation

Radiation detectors have broad application prospects in environmental radiation monitoring, radiation source identification, biomedical imaging, space exploration, astrophysics, other radiation detection and imaging fields. Among the third generation semiconductors, the properties of 4H-SiC are more suitable for making high-temperature and strong radiation neutron detectors. SiC materials have become a current research hotspot. In this paper a special readout integrated circuit is designed for the extremely weak current pulse signal output by SiC trench neutron detector. A leakage current compensation circuit is designed for the influence of leakage current on charge sensitive amplifier(CSA) output baseline. The noise of CSA output is analyzed and the noise minimization is carried out. The circuit can quickly respond to the input signal and extract the energy and time information. The circuit is designed based on the DB Hitek 0.18μm CMOS process model. The layout design is achieved and the post-simulation is completed. Simulated results show that the equivalent input charge range of the system is about 2fC ∼ 20fC. The charge conversion gain is 88mV/fC. The nonlinear error is within 1.6 %. Moreover, the equivalent noise charge is 22e-. The circuit has the advantages of high gain, high linearity and low noise. Therefore, the circuit can effectively adapt to the characteristics of the charge signal output from SiC trench neutron detectors and amplify the output charge signal.

Grey Level Differences Matrix for Alcoholic EEG Signal Classification

Electroencephalogram (EEG) signals can provide information on abnormalities in a person's brain and characterize brain activity. Brain injury or diseases can manifest as brain disorders. Trauma or the use of specific chemicals or medications, such as alcohol, can result in brain damage. Previous research has demonstrated variations in the patterns of EEG signals between alcohol-using and non-drinking people. Various techniques, including wavelet and entropy, have been developed to detect alcoholic EEG using event-related potential (ERP) testing. This work proposes a feature extraction technique based on texture analysis for the classification of alcohol EEG signals because ERP-measured EEG often involves many channels.  An NxM image is thought to be equivalent to an EEG signal with N channels and a recording duration of M samples. The NxM matrix is formed by channelizing the N-channel EEG signal in this investigation. Normalization is then used to get a matrix value of 0-255 or an 8-bit image in the following step. Five features are measured in four directions, and the Grey Level Difference Matrix (GLDM) approach is utilized for feature extraction. Using five grey-level difference matrix (GLDM) features and linear discriminant analysis as a classifier, the maximum accuracy was achieved at 73.3%. Image processing can still be used to increase accuracy even though the final product is less accurate than the earlier technique. The suggested approach can still be adjusted to work with biomedical signals or image processing techniques like the Grey Level Co-occurrence Matrix (GLCM).

Bio-Medical Image Segmentation And Detection For Brain Tumour And Skin Lesions Diseases Through U-NET

Abstract: The research concentrates on enhancing the precise identification and localization of diseases within medical images, a pivotal component of medical imaging analysis crucial for diagnoses and treatment planning. Employing the U-Net architecture, renowned for its effectiveness in biomedical image segmentation, the study targets the detection of brain tumors and skin lesions in MRI or CT scans. Remarkably, the proposed method not only identifies diseases but also provides intricate details regarding their dimensions and spatial arrangement. Extensive experimental validation showcases the method's superiority over existing approaches, underscoring its potential for holistic learning in medical image analysis.

Enhanced contrast imaging with polyamide 6/Fe(OH)3 nanofibrous scaffolds: A focus on high T1 relaxivity

Nanofibers serve as widely employed tissue engineering scaffolds in diverse biomedical applications. When implanted in vivo, it is crucial for tissue engineering scaffolds to be visualizable, enabling the monitoring of their shape, position, and performance. This capability facilitates the effective assessment of implant deformations, displacements, degradations, and functionalities. However, in many biomedical imaging techniques such as magnetic resonance imaging (MRI), the contrast of tissue engineering scaffolds is often inadequate. MRI is particularly notable for its effectiveness in imaging soft tissues. Previous endeavors to enhance the contrast of tissue engineering scaffolds in MRI have involved the use of negative contrast agents (CAs). Nonetheless, negative CAs can result in artifacts, thus favoring the preference for positive CAs due to their ability to generate clearer boundaries. In this study, we successfully prepared composite polyamide 6 nanofibrous scaffolds with ultrafine dispersion Fe(OH)3 nanoparticles using electrospinning and in-situ growth techniques. The relaxation properties of the magnetic nanofibrous scaffolds confirmed the successful production of scaffolds suitable for positive imaging. In vitro cell seeding experiments demonstrated the efficient proliferation and adhesion of endothelial cells and fibroblasts. In vivo studies further revealed the biocompatibility and functionality of the scaffolds. These findings indicate that the prepared PA6/Fe(OH)3 composite nanofibrous scaffolds can enable straightforward, safe, and efficient in vivo positive contrast MRI monitoring, thereby playing a pivotal role in the integration of diagnosis and treatment within tissue engineering scaffolds.

Multi-scale nested UNet with transformer for colorectal polyp segmentation.

Polyp detection and localization are essential tasks for colonoscopy. U-shape network based convolutional neural networks have achieved remarkable segmentation performance for biomedical images, but lack of long-range dependencies modeling limits their receptivefields. Our goal was to develop and test a novel architecture for polyp segmentation, which takes advantage of learning local information with long-range dependenciesmodeling. A novel architecture combining with multi-scale nested UNet structure integrated transformer for polyp segmentation was developed. The proposed network takes advantage of both CNN and transformer to extract distinct feature information. The transformer layer is embedded between the encoder and decoder of a U-shape net to learn explicit global context and long-range semantic information. To address the challenging of variant polyp sizes, a MSFF unit was proposed to fuse features with multipleresolution. Four public datasets and one in-house dataset were used to train and test the model performance. Ablation study was also conducted to verify each component of the model. For dataset Kvasir-SEG and CVC-ClinicDB, the proposed model achieved mean dice score of 0.942 and 0.950 respectively, which were more accurate than the other methods. To show the generalization of different methods, we processed two cross dataset validations, the proposed model achieved the highest mean dice score. The results demonstrate that the proposed network has powerful learning and generalization capability, significantly improving segmentation accuracy and outperforming state-of-the-artmethods. The proposed model produced more accurate polyp segmentation than current methods on four different public and one in-house datasets. Its capability of polyps segmentation in different sizes shows the potential clinicalapplication.

Optimized Federated Learning Algorithm for Breast Cancer Detection Using the Marine Predators Algorithm

Breast cancer stands as the leading cause of cancer-related fatalities among women worldwide, necessitating effective detection methods. However, the current landscape of breast cancer detection poses several challenges, including high costs, complexity, and suboptimal accuracy rates. In response, this paper presents a pioneering approach to breast cancer detection leveraging federated learning, a paradigm that allows model training across distributed data sources without centralized data sharing. Central to this novel detection model is the integration of a Convolutional Neural Network (CNN) architecture and a meta-heuristic optimization algorithm known as the marine predator's algorithm (MPA). CNNs have emerged as powerful tools for biomedical image analysis, enabling automatic feature extraction crucial for detecting abnormalities in medical images. However, optimizing CNNs for detection tasks requires meticulous tuning of hyperparameters across different layers, a process impractical to perform manually due to its complexity and time-intensive nature. To address this challenge, the paper proposes the utilization of the MPA as an optimization algorithm within the federated learning framework. The MPA, inspired by the foraging behaviour of marine predators, enhances the exploration-exploitation trade-off to prevent the model from getting stuck in local optima during training. By incorporating the MPA into the CNN architecture, the proposed model achieves remarkable testing accuracy, surpassing the performance of traditional DCNN models. During testing, the proposed model attained an impressive accuracy rate of 98.32%, showcasing its superior performance compared to the DCNN model, which achieved a 95.95% accuracy. Moreover, the proposed approach outperforms existing algorithms, achieving an accuracy rate of 98%. These results underscore the effectiveness of the federated learning-based approach in enhancing breast cancer detection accuracy. the proposed breast cancer detection model represents a significant advancement in the field, offering a highly accurate and efficient solution to overcome the challenges associated with traditional detection methods. By leveraging federated learning and innovative optimization techniques, this model holds promise for improving early detection rates, ultimately contributing to better patient outcomes in the fight against breast cancer