Localization Task Research Articles

An Efficient Modified Black Widow Optimized Node Localization in Wireless Sensor Network

Wireless Sensor Network (WSN) is an encouraging technology in various applications like atmosphere monitoring, vehicular systems, and targeting devices. Localization in WSN is performed to identify the current position or coordinates of sensor nodes. The crucial task of localization is to find coordinates without manual configuration and expensive hardware like GPS. This work proposes a new localization algorithm using Modified Black Widow Optimization (MBWO). The proposed method involves an optimal selection of anchor nodes to reduce localization errors. Black Widow Optimization (BWO) is a naturally inspired optimization algorithm encouraged by the unique coupling behaviour of black widow spiders. The performance of black widow optimization is improved by levy flight distribution to escape from local minima. Experimental results show that the proposed MBWO-optimized localization achieves a higher localized node count with minimized localized error compared to other optimization algorithms.

To be global or personalized: Generalized federated learning with cooperative adaptation for data heterogeneity

As federated learning (FL) continues to advance, research has branched into two major directions: enhancing the individual global model and developing multiple personalized models. However, few studies took both orientations into consideration in parallel. The underlying reason lies in the objective heterogeneity between global and personalized models, where the former emphasizes generalization, while the latter focuses on specialization. The dilemma of sacrificing the personalized deviation in exchange for stable global enhancement, or forsaking the global model for personalization, is particularly acute in highly heterogeneous data scenarios (as a curse). To establish the coexistence of global and personalized models in highly heterogeneous data scenarios, we propose FedAKD, a generalized federated learning framework with adaptive knowledge distillation to optimizes both global and personalized models. Specifically, based on the similarity of predictive distributions over a reference dataset, two distinct adaptive weighting strategies are employed to match the divergent focuses of global and personalized optimization, leveraging data heterogeneity to foster cooperative adaptation and establish their coexistence (as a blessing). The client-side strategy through cluster-oriented optimization to facilitate the domain adaptation in local tasks. Meanwhile, the server-side strategy adjusts the global model to accommodate varying optimization directions from diverse implicit clusters. Experiments in label and feature shift settings demonstrate that our method outperforms state-of-the-art methods in both global and personalized performance with faster convergence [e.g. the accuracy improvements on the physical activity monitoring dataset (PAMAP2) for global and personalized models exceed 26.51% and 11.00%, respectively]. Furthermore, our method is equally applicable to real-world healthcare scenarios.

Examining the lateralization of electrophysiological correlates of auditory awareness.

The neurological basis for perceptual awareness remains unclear, and theories disagree as to whether sensory cortices per se generate awareness. Critically, neural activity in the sensory cortices is only a neural correlate of consciousness (NCC) if it closely matches the contents of perceptual awareness. Research in vision and touch suggest that contralateral activity in sensory cortices is an NCC. Similarly, research in hearing with two sound sources (left and right) presented over headphones also suggests that a candidate NCC called the auditory awareness negativity (AAN) matches perceived location of sound. The current study used 13 different sound sources presented over loudspeakers for natural localization cues and measured event-related potentials to a threshold stimulus in a sound localization task. Preregistered Bayesian mixed models provided moderate evidence against an overall AAN and very strong evidence against its lateralization. Because of issues regarding data quantity and quality, exploratory analyses with aggregated data from multiple loudspeakers were conducted. Results provided moderate evidence for an overall AAN and strong evidence against its lateralization. Nonetheless, the interpretations of these results remain inconclusive. Therefore, future research should reduce the number of conditions and/or test over several sessions to procure a sufficient amount of data. Taken at face value, the results may suggest issues with AAN as an NCC of auditory awareness, as it does not laterally map onto experiences in a free-field auditory environment, in contrast to the NCCs of vision and touch.

Facial feature point detection under large range of face deformations

Facial Feature Point Detection (FFPD) plays a significant role in several face analysis tasks such as feature extraction and classification. This paper presents a Fully Automatic FFPD system using the application of Random Forest Regression Voting in a Constrained Local Model (RFRV-CLM) framework. A global detector is used to find the approximate positions of the facial region and eye centers. A sequence of local RFRV-CLMs are used to locate a detailed set of points around the facial features. Both global and local models use Random Forest Regression to vote for optimal positions. The system is evaluated in the task of facial expression localization using five different facial expression databases of different characteristics including age, intensity, 6-basic expressions, 22 compound expressions, static and dynamic images, and deliberate and spontaneous expressions. Quantitative results of the evaluation of automatic point localization against manual points (ground truth) demonstrated that the results of the proposed approach are encouraging and outperform the results of alternative techniques tested on the same databases.

Federated Many-Task Bayesian Optimization

Bayesian optimization is a powerful surrogate-assisted algorithm for solving expensive black-box optimization problems. While Bayesian optimization was developed for centralized optimization, the availability of massive distributed data has attracted increased interests in exploring federated Bayesian optimization that can use data on multiple clients without leaking the raw data. However, existing federated Bayesian optimization (FBO) approaches assume that either all clients jointly solve the same optimization task, or only one client solves one target optimization task by transferring knowledge from others in a federated way, making them unsuited for many real-world applications. In this paper, we consider FBO for the scenario where multiple related local black-box tasks associated with different clients are jointly optimized by sharing knowledge between tasks without leaking the data privacy. An efficient federated many-task Bayesian optimization framework is proposed to address not independent and identically distributed (non-IID) data while protecting the data privacy in the federated setting. A novel federated knowledge transfer paradigm is developed for dynamic many-task model aggregation according to a dissimilarity matrix. The dissimilarity is measured based on the rank of the predictions and only the hyperparameters in the local Gaussian process models are shared. In addition, a federated ensemble acquisition function is constructed by integrating the predictions of two surrogates using the global and local hyperparameters, respectively, to effectively search for the optimal solution. Experimental results show that our proposed method has reliable performance on both benchmark problems and a real machine learning problem also in the presence of non-IID data.

Deep reinforcement learning based resource provisioning for federated edge learning

With the rapid development of mobile internet technology and increasing concerns over data privacy, Federated Learning (FL) has emerged as a significant framework for training machine learning models. Given the advancements in technology, User Equipment (UE) can now process multiple computing tasks simultaneously, and since UEs can have multiple data sources that are suitable for various FL tasks, multiple tasks FL could be a promising way to respond to different application requests at the same time. However, running multiple FL tasks simultaneously could lead to a strain on the device’s computation resource and excessive energy consumption, especially the issue of energy consumption challenge. Due to factors such as limited battery capacity and device heterogeneity, UE may fail to efficiently complete the local training task, and some of them may become stragglers with high-quality data. Aiming at alleviating the energy consumption challenge in a multi-task FL environment, we design an automatic Multi-Task FL Deployment (MFLD) algorithm to reach the local balancing and energy consumption goals. The MFLD algorithm leverages Deep Reinforcement Learning (DRL) techniques to automatically select UEs and allocate the computation resources according to the task requirement. Extensive experiments validate our proposed approach and showed significant improvements in task deployment success rate and energy consumption cost.

Influence of global/local processing on perceived colour transparency.

Our cognitive processing is flexible and affected by global/local dominance in prior cognitive tasks. Similar to cognitive processing, perceptual processing, especially colour perception related to global/local processing, may be affected by prior global/local dominance; however, this possibility has not yet been assessed. Here, we examined whether prior tasks involving global/local processing influenced colour perception related to global/local processing. As colour perception is related to global/local processing, we focused on perceived colour transparency, in which a transparent layer is perceived in front of a background layer, even though these stimuli are physically in the same layer. When viewing the colour transparency stimulus, we expected that the perceived colour of a specific region, when focusing on only the local region, would differ from that when focusing on the whole image. In our study, the participants completed a global or local Navon task, followed by a colour-matching task that assessed how they saw colours using colour transparency stimuli. The degree of optical illusion (i.e., perceived colour transparency) after the global Navon task was greater than that after the local Navon task. Thus, prior global/local processing, a flexible mode of cognitive processing, influenced colour perception. This study provides new insight into perceptual flexibility, especially in colour perception.

Pineapple Detection with YOLOv7-Tiny Network Model Improved via Pruning and a Lightweight Backbone Sub-Network

High-complexity network models are challenging to execute on agricultural robots with limited computing capabilities in a large-scale pineapple planting environment in real time. Traditional module replacement often struggles to reduce model complexity while maintaining stable network accuracy effectively. This paper investigates a pineapple detection framework with a YOLOv7-tiny model improved via pruning and a lightweight backbone sub-network (the RGDP-YOLOv7-tiny model). The ReXNet network is designed to significantly reduce the number of parameters in the YOLOv7-tiny backbone network layer during the group-level pruning process. Meanwhile, to enhance the efficacy of the lightweight network, a GSConv network has been developed and integrated into the neck network, to further diminish the number of parameters. In addition, the detection network incorporates a decoupled head network aimed at separating the tasks of classification and localization, which can enhance the model’s convergence speed. The experimental results indicate that the network before pruning optimization achieved an improvement of 3.0% and 2.2%, in terms of mean average precision and F1 score, respectively. After pruning optimization, the RGDP-YOLOv7-tiny network was compressed to just 2.27 M in parameter count, 4.5 × 109 in computational complexity, and 5.0MB in model size, which were 37.8%, 34.1%, and 40.7% of the original YOLOv7-tiny network, respectively. Concurrently, the mean average precision and F1 score reached 87.9% and 87.4%, respectively, with increases of 0.8% and 1.3%. Ultimately, the model’s generalization performance was validated through heatmap visualization experiments. Overall, the proposed pineapple object detection framework can effectively enhance detection accuracy. In a large-scale fruit cultivation environment, especially under the constraints of hardware limitations and limited computational power in the real-time detection processes of agricultural robots, it facilitates the practical application of artificial intelligence algorithms in agricultural engineering.

Weakly Supervised Object Detection in Chest X-Rays with Differentiable ROI Proposal Networks and Soft ROI Pooling.

Weakly supervised object detection (WSup-OD) increases the usefulness and interpretability of image classification algorithms without requiring additional supervision. The successes of multiple instance learning in this task for natural images, however, do not translate well to medical images due to the very different characteristics of their objects (i.e. pathologies). In this work, we propose Weakly Supervised ROI Proposal Networks (WSRPN), a new method for generating bounding box proposals on the fly using a specialized region of interest-attention (ROI-attention) module. WSRPN integrates well with classic backbone-head classification algorithms and is end-to-end trainable with only image-label supervision. We experimentally demonstrate that our new method outperforms existing methods in the challenging task of disease localization in chest X-ray images. Code: https://anonymous.4open.science/r/WSRPN-DCA1.

Guiding attention in flow-based conceptual models through consistent flow and pattern visibility

A critical part of flow-based conceptual modeling, such as process modeling, is visualizing the logical and temporal sequence in which activities in a process should be completed. While there are established standards and recommendations, there is limited empirical research examining the influence of process model layout on model comprehension. To address this research gap, we conducted a controlled eye-tracking experiment with 70 participants comparing different layouts. The experimental results confirm that the visibility of control flow patterns is critical for assisting users with visual processing, particularly attentional allocation, when comprehending process models for both local comprehension tasks and tasks requiring cognitive integration of model components. In models with more directional changes, users’ visual attention is more drawn to irrelevant regions, but comprehension is less affected as long as patterns remain visible. Our findings not only elucidate how cognitive fit between a visual representation and a task can manifest itself and the perceptual benefits it brings, but they can also guide the automated layout of models in tools and complement practical process modeling guidelines.

Parkinson's image detection and classification based on deep learning

ObjectiveThere are two major issues in the MRI image diagnosis task for Parkinson's disease. Firstly, there are slight differences in MRI images between healthy individuals and Parkinson's patients, and the medical field has not yet established precise lesion localization standards, which poses a huge challenge for the effective prediction of Parkinson's disease through MRI images. Secondly, the early diagnosis of Parkinson's disease traditionally relies on the subjective judgment of doctors, which leads to insufficient accuracy and consistency. This article proposes an improved YOLOv5 detection algorithm based on deep learning for predicting and classifying Parkinson's images.MethodsThis article improves the YOLOv5s network as the basic framework. Firstly, the CA attention mechanism was introduced to enable the model to dynamically adjust attention based on local features of the image, significantly enhancing the sensitivity of the model to PD related small pathological features; Secondly, replace the dynamic full dimensional convolution module to optimize the multi-level extraction of image features; Finally, the coupling head strategy is adopted to improve the execution efficiency of classification and localization tasks separately.ResultsWe validated the effectiveness of the proposed method using a dataset of 582 MRI images from 108 patients. The results show that the proposed method achieves 0.961, 0.974, and 0.986 in Precision, Recall, and mAP, respectively, and the experimental results are superior to other algorithms.ConslusionThe improved model has achieved high accuracy and detection accuracy, and can accurately detect and recognize complex Parkinson's MRI images.SignificanceThis algorithm has shown good performance in the early diagnosis of Parkinson's disease and can provide clinical assistance for doctors in early diagnosis. It compensates for the limitations of traditional methods.

Multi-agent DRL for edge computing: A real-time proportional compute offloading

In the Industrial Internet of Things, devices with limited computing power and energy storage often rely on offloading tasks to edge servers for processing. However, existing methods are plagued by the high cost of device communication and unstable training processes. Consequently, Deep reinforcement learning (DRL) has emerged as a promising solution to tackle the computation offloading problem. In this paper, we propose a framework called multi-agent twin delayed shared deep deterministic policy gradient algorithm (MASTD3) based on DRL. Firstly, we formulate the task offloading conundrum as a long-term optimization problem, which aids in mitigating the challenge of deciding between local or remote task execution by a device, leading to more effective task offloading management. Secondly, we enhance MASTD3 by introducing a priority experience replay buffer mechanism and a model sample replay buffer mechanism, thus improving sample utilization and overcoming the cold-start problem associated with long-term optimization. Moreover, we refine the actor-critic structure, enabling all agents to share the same critic network. This modification accelerates convergence speed during the training process and reduces computational costs during runtime. Finally, experimental results demonstrate that MASTD3 effectively addresses the proportional offloading problem, which is optimized by 44.32%, 29.26%, and 17.47% compared to DDPQN, MADDPG, and FLoadNet.

Resource efficient stabilization for local tasks despite unknown capacity links

Self-stabilizing protocols enable distributed systems to recover correct behavior starting from any arbitrary configuration. In particular, when processors communicate by message passing and the communication links are unbounded, fake messages may be placed in communication links by an adversary. When the number of such fake messages is unknown, self-stabilization may require huge resources:•generic solutions (a.k.a. data link protocols) require unbounded resources, which makes them unrealistic to deploy,•specific solutions (e.g., census or tree construction) require O(nlog⁡n) or O(Δlog⁡n) bits of memory per node, where n denotes the network size and Δ its maximum degree, which may prevent scalability.We investigate the possibility of resource-efficient self-stabilizing protocols in this context.Specifically, we present self-stabilizing protocols for (Δ+1)-coloring and maximal independent set construction in any n-node graph, under the asynchronous message-passing model. The problems of (Δ+1)-coloring and maximal independent set construction are widely regarded as benchmark problems for evaluating local algorithms. Our protocols offer many desirable features. They are deterministic, converge in O(kΔn2log⁡n) message exchanges, where k is the (unknown) initial number of (possibly corrupted) messages in a communication link. They use messages of O(log⁡log⁡n+log⁡Δ) bits with a memory of O(Δlog⁡Δ+log⁡log⁡n) bits at each node. The resource consumption of our protocols is thus almost oblivious to the number of nodes, enabling scalability. Moreover, a striking property of our protocols is that the nodes do not need to know the number, or any bound on the number of messages initially present in each communication link of the initial (potentially corrupted) network configuration. This permits our protocols to handle any future network with unknown message capacity communication links.A key building block of our coloring and maximal independent set schemes is an algorithm to obtain an acyclic orientation of graph edges, that is of independent interest, and can serve as a useful tool for solving other tasks in this challenging setting.

Effects of Robotic Postural Stand Training with Epidural Stimulation on Sitting Postural Control in Individuals with Spinal Cord Injury: A Pilot Study.

(1) Background. High-level spinal cord injury (SCI) disrupts trunk control, leading to an impaired performance of upright postural tasks in sitting and standing. We previously showed that a novel robotic postural stand training with spinal cord epidural stimulation targeted at facilitating standing (Stand-scES) largely improved standing trunk control in individuals with high-level motor complete SCI. Here, we aimed at assessing the effects of robotic postural stand training with Stand-scES on sitting postural control in the same population. (2) Methods. Individuals with cervical (n = 5) or high-thoracic (n = 1) motor complete SCI underwent approximately 80 sessions (1 h/day; 5 days/week) of robotic postural stand training with Stand-scES, which was performed with free hands (i.e., without using handlebars) and included periods of standing with steady trunk control, self-initiated trunk and arm movements, and trunk perturbations. Sitting postural control was assessed on a standard therapy mat, with and without scES targeted at facilitating sitting (Sit-scES), before and after robotic postural stand training. Independent sit time and trunk center of mass (CM) displacement were assessed during a 5 min time window to evaluate steady sitting control. Self-initiated antero-posterior and medial-lateral trunk movements were also attempted from a sitting position, with the goal of covering the largest distance in the respective cardinal directions. Finally, the four Neuromuscular Recovery Scale items focused on sitting trunk control (Sit, Sit-up, Trunk extension in sitting, Reverse sit-up) were assessed. (3) Results. In summary, neither statistically significant differences nor large Effect Size were promoted by robotic postural stand training for the sitting outcomes considered for analysis. (4) Conclusions. The findings of the present study, together with previous observations, may suggest that robotic postural stand training with Stand-scES promoted trunk motor learning that was posture- and/or task-specific and, by itself, was not sufficient to significantly impact sitting postural control.

A high-speed YOLO detection model for steel surface defects with the channel residual convolution and fusion-distribution

Abstract Accurately and efficiently detecting steel surface defects is a critical step in steel manufacturing. However, the compromise between the detection speed and accuracy remains a major challenge, especially for steel surface defects with large variations in the scale. To address the issue, an improved you only look once (YOLO) based detection model is proposed through the reinforcement of its backbone and neck. Firstly, for the reduction of the redundant parameters and also the improvement of the characterization ability of the model, an effective channel residual structure is adopted to construct a channel residual convolution module and channel residual cross stage partial module as components of the backbone network, respectively. They realize the extraction of both the shallow feature and multi-scale feature simultaneously under a small number of convolutional parameters. Secondly, in the neck of YOLO, a fusion-distribution strategy is employed, which extracts and fuses multi-scale feature maps from the backbone network to provide global information, and then distributes global information into local features of different branches through an inject attention mechanism, thus enhancing the feature gap between different branches. Then, a model called CRFD-YOLO is derived for the steel surface defect detection and localization for the situations where both speed and accuracy are demanding. Finally, extensive experimental validations are conducted to evaluate the performance of CRFD-YOLO. The validation results indicate that CRFD-YOLO achieves a satisfactory detection performance with a mean average precision of 81.3% on the NEU-DET and 71.1% on the GC10-DET. Additionally, CRFD-YOLO achieves a speed of 161 frames per second, giving a great potential in real-time detection and localization tasks.

A Multitask Learning-Based Vision Transformer for Plant Disease Localization and Classification

Plant disease detection is a critical task in agriculture, essential for ensuring crop health and productivity. Traditional methods in this context are often labor-intensive and prone to errors, highlighting the need for automated solutions. While computer vision-based solutions have been successfully deployed in recent years for plant disease identification and localization tasks, these often operate independently, leading to suboptimal performance. It is essential to develop an integrated solution combining these two tasks for improved efficiency and accuracy. This research proposes the innovative Plant Disease Localization and Classification model based on Vision Transformer (PDLC-ViT), which integrates co-scale, co-attention, and cross-attention mechanisms and a ViT, within a Multi-Task Learning (MTL) framework. The model was trained and evaluated on the Plant Village dataset. Key hyperparameters, including learning rate, batch size, dropout ratio, and regularization factor, were optimized through a thorough grid search. Early stopping based on validation loss was employed to prevent overfitting. The PDLC-ViT model demonstrated significant improvements in plant disease localization and classification tasks. The integration of co-scale, co-attention, and cross-attention mechanisms allowed the model to capture multi-scale dependencies and enhance feature learning, leading to superior performance compared to existing models. The PDLC-ViT model evaluated on two public datasets achieved an accuracy of 99.97%, a Mean Average Precision (MAP) of 99.18%, and a Mean Average Recall (MAR) of 99.11%. These results underscore the model's exceptional precision and recall, highlighting its robustness and reliability in detecting and classifying plant diseases. The PDLC-ViT model sets a new benchmark in plant disease detection, offering a reliable and advanced tool for agricultural applications. Its ability to integrate localization and classification tasks within an MTL framework promotes timely and accurate disease management, contributing to sustainable agriculture and food security.

A novel behavioral paradigm using mice to study predictive postural control.

The ability to anticipate disturbances and adjust one's posture accordingly-known as "predictive postural control"-is crucial for preventing falls and for advancing robotics. Human postural studies often face limitations with measurement tools and sample sizes, hindering insight into underlying neural mechanisms. To address these limitations, we developed a postural perturbation task for freely moving mice, modeled after those used in human studies. Using a dynamic platform, we delivered reproducible perturbations with or without preceding auditory cues and quantified how the auditory cue affects postural responses to perturbations. Our work provides validation of a new postural control model, which opens the door to the types of neural population recordings and circuit manipulation that are currently possible only in mice.

Deep-learning-assisted triboelectric whisker for near field perception and online state estimation of underwater vehicle

Agile near field perception remains a challenge for underwater vehicles, which could significantly enhance their capability of online state estimation. Harbor seals have evolved a whisker array that can accurately measure and identify environmental information in their surroundings. Inspired by the "smart" whiskers of harbor seals, the study has designed a deep learning-assisted bionic underwater triboelectric whisker sensor (UTWS) to passively perceive diverse hydrodynamic flow fields, including omni-directional steady flow and wakes induced by bluff body upstream. The device mainly comprised an elliptical whisker shaft with a high-aspect-ratio of 0.403, four flexible triboelectric sensing units mimicking the nerve in the follicular sinus complex, and a flexible corrugated joint imitating the surface skin of marine organisms' cheeks. The UTWS demonstrated impressive advantages of rapid response times of 21 ms, high sensitivity of 1.16 V/m.s−1, high signal-to-noise-ratio of 61.66 dB. By implementing deep learning analytics to process the multi-channel signals, the underwater vehicle equipped with the UTWS can accomplish online velocity estimation proficiently, with an approximate root mean square error of approximately 0.093 in the verification case. Thus, this UTWS-based, deep-learning-assisted perception could become a promising tool for integration with underwater vehicles in the local navigation tasks.

A-star algorithm vs. Q-learning algorithm for path planning: A comparative analysis

In robotics research, the core focus is on path planning, as it's essential for robots to navigate according to human objectives to be valuable. This paper provides an in-depth analysis of the commonly used A-Star algorithm and Q-learning algorithm in mobile robot path planning. It specifically examines the advantages and disadvantages of both the A-Star algorithm and the Q-learning algorithm, highlighting their roles in the advancement of robotic intelligence. The A-star algorithm is an algorithm with a heuristic function to guide path selection direction. Also, the A-star algorithm is guaranteed to identify the most efficient path in terms of traffic cost when facing static obstacles. However, its performance degrades when dealing with trap nodes. Specifically, the algorithm occupies a large amount of memory and takes a long computational time. In contrast, the Q-learning algorithm is effective for learning continuous actions with the same network data structure for local path planning tasks.

Breast Cancer Detection and Localizing the Mass Area Using Deep Learning

Breast cancer presents a substantial health obstacle since it is the most widespread invasive cancer and the second most common cause of death in women. Prompt identification is essential for effective intervention, rendering breast cancer screening a critical component of healthcare. Although mammography is frequently employed for screening purposes, the manual diagnosis performed by pathologists can be laborious and susceptible to mistakes. Regrettably, the majority of research prioritizes mass classification over mass localization, resulting in an uneven distribution of attention. In response to this problem, we suggest a groundbreaking approach that seeks to identify and pinpoint cancers in breast mammography pictures. This will allow medical experts to identify tumors more quickly and with greater precision. This paper presents a complex deep convolutional neural network design that incorporates advanced deep learning techniques such as U-Net and YOLO. The objective is to enable automatic detection and localization of breast lesions in mammography pictures. To assess the effectiveness of our model, we carried out a thorough review that included a range of performance criteria. We specifically evaluated the accuracy, precision, recall, F1-score, ROC curve, and R-squared error using the publicly available MIAS dataset. Our model performed exceptionally well, with an accuracy rate of 93.0% and an AUC (area under the curve) of 98.6% for the detection job. Moreover, for the localization task, our model achieved a remarkably high R-squared value of 97%. These findings highlight that deep learning can boost the efficiency and accuracy of diagnosing breast cancer. The automation of breast lesion detection and classification offered by our proposed method bears substantial benefits. By alleviating the workload burden on pathologists, it facilitates expedited and accurate breast cancer screening processes. As a result, the proposed approach holds promise for improving healthcare outcomes and bolstering the overall effectiveness of breast cancer detection and diagnosis.