Sparse Autoencoder Research Articles

Enhancing performance of end-to-end communication system using Attention Mechanism-based Sparse Autoencoder over Rayleigh fading channel

Deep learning has revolutionized communication systems by introducing innovative approaches to address channel impairments through end-to-end models. Autoencoders, a type of deep learning architecture, are adept at learning compact data representations. However, conventional autoencoders in end-to-end models can suffer from overfitting, which limits their effectiveness in noisy communication environments. To address this issue, we propose a Sparse Autoencoder-based (SAE) model that enforces sparsity and promotes the extraction of robust features. Despite its effectiveness, the SAE model may still lack the ability to focus on the most relevant features of the input data. To overcome this limitation, we further introduce an Attention Mechanism-based Sparse Autoencoder (ASA) model. This model integrates the feature extraction capabilities of a sparse autoencoder with an attention mechanism that selectively highlights informative features of the signal. Through simulations, we demonstrate that both proposed models significantly improve M-PSK and M-QAM communication system performance. When trained at 7 dB, both proposed models exhibit significant performance improvements at higher testing average SNRs. Our results show that the SAE model outperforms the conventional Maximum Likelihood Detection (MLD) model and baseline autoencoder systems but suffers from error floor issues. The SAE model suffers from an error floor at average SNRs beyond 16 dB for BPSK and 14 dB for higher-order modulation schemes. As the value of M increases, the performance gap between the MLD and the proposed SAE model narrows. The ASA model, however, effectively mitigates the error floor observed in the SAE model for all values of M and across all modulation schemes. This research highlights the benefits of integrating an attention mechanism with SAE, resulting in enhanced robustness and reliability in communication systems characterized by improved accuracy and reduced error rates.

Image enhancement methods for inspection of planar and non-planar FRP structures using a noise-based microwave NDT inspection system

Microwave nondestructive testing (MNDT) includes inspection techniques that assess a particular material’s health status using low-power and contactless inspection systems. In near-field microwave inspections, imaging results are heavily influenced by the standoff distance parameter, i.e., the physical separation between the microwave sensor and the sample under test (SUT). Variations in the standoff distance during an inspection tend to cause defect masking of disbonds and delaminations in fiber-reinforced polymer (FRP) materials, causing defects to go undetected frequently. An MNDT near-field inspection system using noise waveforms is used to identify engineered internal defects within carbon fiber-reinforced polymer (CFRP) samples. Tactics utilizing Principal Component Analysis (PCA), Stacked Sparse Autoencoders (SSAEs), and an autoencoder network trained in a manner for anomaly detection are used to minimize the effects of standoff distance, reduce defect masking, and increase the ability to identify hidden defects. The samples tested are constructed to possess planar and non-planar geometries, such that the viability of the data-driven image enhancement and standoff distance correction methods are demonstrated with respect to a wide variety of in-situ inspection applications.

Novel sparse auto-encoder framework with pseudo-labeled reinforcement for cross domain fault diagnosis with imbalanced samples

Abstract By applying diagnostic expertise from one area to another that is closely related, transfer fault diagnosis is an efficient strategy for guaranteeing the secure and dependable operation of mechanical equipment. However, the majority of rotating machinery monitoring data are gathered in industrial applications under normal operating settings, which leads to an imbalance between positive and negative samples. Consequently, performing high-precision fault diagnosis with imbalanced data and different working conditions becomes challenging due to the escalating difficulty and cost of acquiring labeled fault samples. For cross domain fault diagnosis, an enhanced deep transfer sparse auto-encoder (SAE) framework is provided. This approach leverages a SAE to delve deeper into fault features within imbalanced data and reconstruct the samples accordingly. Furthermore, the research proposes a feature domain penalty term to facilitate cross-domain training by aligning the distribution of the source and target domain data which can reduce data distribution deviation. A pseudo-labeled reinforcement training method is presented to further improve cross-domain classification accuracy with imbalanced samples. Extensive experiments were conducted on two datasets to assess the proposed method. The results were compared with other algorithms, demonstrating the effectiveness and superiority of the proposed approach.

An automated intrusion detection system in IoT system using Attention based Deep Bidirectional Sparse Auto Encoder model

Nowadays, the Internet of Things (IoT) is a smart network connected to the Internet for transmitting gathered data with verified protocols. Attackers frequently use communication protocol defects as the basis for their attacks. Better protection measures are required since attacks affect the reputations of service providers. Both machine learning (ML) and deep learning (DL) methods have been developed in a number of research works to detect network intrusions. However, the system's security is limited by the rising number of new threats. Critical problems in IoT platforms, cyber-physical systems, wireless networks, and fog computing are caused by such attacks. The development of various cyber-security attacks reinforces the need for a strong intrusion detection system (IDS) in the IoT platform. The proposed study introduced a robust deep-feature learning mechanism for automatically detecting network intruders in the IoT platform. Initially, input data are gathered from the given dataset. Pre-processing helps reduce any noise in the data and improves the data quality using cleaning, outlier removal, and min-max normalization. The proposed Attention-based Deep Bidirectional Sparse Auto Encoder (AD-BiSA) model is the most important feature retrieved using the attention-based deep Bi-LSTM model. The different IoT network threats are categorized using a sparse Autoencoder approach. The chaotic Seagull Optimization (CSGO) algorithm decreases the loss and enhances the weight in the proposed DL technique. The UNSW NB15_IDS and NSL-KDD datasets achieve accuracy rates of 99.71% and 98.97%, respectively, for the proposed technique. The proposed method achieves better performance than existing approaches.

Dictionary trained attention constrained low rank and sparse autoencoder for hyperspectral anomaly detection

Dictionary representations and deep learning Autoencoder (AE) models have proven effective in hyperspectral anomaly detection. Dictionary representations offer self-explanation but struggle with complex scenarios. Conversely, autoencoders can capture details in complex scenes but lack self-explanation. Complex scenarios often involve extensive spatial information, making its utilization crucial in hyperspectral anomaly detection. To effectively combine the advantages of both methods and address the insufficient use of spatial information, we propose an attention constrained low-rank and sparse autoencoder for hyperspectral anomaly detection. This model includes two encoders: an attention constrained low-rank autoencoder (AClrAE) trained with a background dictionary and incorporating a Global Self-Attention Module (GAM) to focus on global spatial information, resulting in improved background reconstruction; and an attention constrained sparse autoencoder (ACsAE) trained with an anomaly dictionary and incorporating a Local Self-Attention Module (LAM) to focus on local spatial information, resulting in enhanced anomaly reconstruction. Finally, to merge the detection results from both encoders, a nonlinear fusion scheme is employed. Experiments on multiple real and synthetic datasets demonstrate the effectiveness and feasibility of the proposed method.

Circular supply chain for smart production in Industry 4.0

Sustainable practices in industrial engineering are not just ideal but necessary. The push for Circular Supply Chain (CSC) in Industry 4.0 has become increasingly vital. Motivated by this, this study proposed the UNISONE framework, integrated with advanced smart production technologies grounded in CSC principles. The proposed framework resulted in enhanced operational efficiency and a sustainable production system that significantly minimizes waste. The research was validated through an empirical study within a bearing factory, successfully demonstrating the framework’s efficacy and creating value within the CSC paradigm. Key findings include the impact of different signal-to-noise ratios on model performance, with peak test accuracy at a noise ratio of 0.6, and the best results of 85.32% accuracy achieved by combining sparse and noise reduction autoencoder techniques in generative artificial intelligence. This study underscores the framework’s potential to address industrial engineering challenges and promote scalable, efficient, and eco-conscious manufacturing, benefiting both the environment and the economy.

Predicting the potential associations between circRNA and drug sensitivity using a multisource feature-based approach.

The unique non-coding RNA molecule known as circular RNA (circRNA) is distinguished from conventional linear RNA by having a longer half-life, greater degree of conservation and inherent solidity. Extensive research has demonstrated the profound impact of circRNA expression on cellular drug sensitivity and therapeutic efficacy. There is an immediate need for the creation of efficient computational techniques to anticipate the potential correlations between circRNA and drug sensitivity, as classical biological research approaches are time-consuming and costly. In this work, we introduce a novel deep learning model called SNMGCDA, which aims to forecast the relationships between circRNA and drug sensitivity. SNMGCDA incorporates a diverse range of similarity networks, enabling the derivation of feature vectors for circRNAs and drugs using three distinct calculation methods. First, we utilize a sparse autoencoder for the extraction of drug characteristics. Subsequently, the application of non-negative matrix factorization (NMF) enables the identification of relationships between circRNAs and drugs based on their shared features. Additionally, the multi-head graph attention network is employed to capture the characteristics of circRNAs. After acquiring the characteristics from these three separate components, we combine them to form a unified and inclusive feature vector for each cluster of circRNA and drug. Finally, the relevant feature vectors and labels are inputted into a multilayer perceptron (MLP) to make predictions. The outcomes of the experiment, obtained through 5-fold cross-validation (5-fold CV) and 10-fold cross-validation (10-fold CV), demonstrate SNMGCDA outperforms five other state-of-art methods in terms of performance. Additionally, the majority of case studies have predominantly confirmed newly discovered correlations by SNMGCDA, thereby emphasizing its reliability in predicting potential relationships between circRNAs and drugs.

Identification of Anomalies in Lung and Colon Cancer Using Computer Vision-Based Swin Transformer with Ensemble Model on Histopathological Images.

Lung and colon cancer (LCC) is a dominant life-threatening disease that needs timely attention and precise diagnosis for efficient treatment. The conventional diagnostic techniques for LCC regularly encounter constraints in terms of efficiency and accuracy, thus causing challenges in primary recognition and treatment. Early diagnosis of the disease can immensely reduce the probability of death. In medical practice, the histopathological study of the tissue samples generally uses a classical model. Still, the automated devices that exploit artificial intelligence (AI) techniques produce efficient results in disease diagnosis. In histopathology, both machine learning (ML) and deep learning (DL) approaches can be deployed owing to their latent ability in analyzing and predicting physically accurate molecular phenotypes and microsatellite uncertainty. In this background, this study presents a novel technique called Lung and Colon Cancer using a Swin Transformer with an Ensemble Model on the Histopathological Images (LCCST-EMHI). The proposed LCCST-EMHI method focuses on designing a DL model for the diagnosis and classification of the LCC using histopathological images (HI). In order to achieve this, the LCCST-EMHI model utilizes the bilateral filtering (BF) technique to get rid of the noise. Further, the Swin Transformer (ST) model is also employed for the purpose of feature extraction. For the LCC detection and classification process, an ensemble deep learning classifier is used with three techniques: bidirectional long short-term memory with multi-head attention (BiLSTM-MHA), Double Deep Q-Network (DDQN), and sparse stacked autoencoder (SSAE). Eventually, the hyperparameter selection of the three DL models can be implemented utilizing the walrus optimization algorithm (WaOA) method. In order to illustrate the promising performance of the LCCST-EMHI approach, an extensive range of simulation analyses was conducted on a benchmark dataset. The experimentation results demonstrated the promising performance of the LCCST-EMHI approach over other recent methods.

An enhanced Deep-Learning empowered Threat-Hunting Framework for software-defined Internet of Things

The Software-Defined Networking (SDN) powered Internet of Things (IoT) offers a global perspective of the network and facilitates control and access of IoT devices using a centralized high-level network approach called Software Defined-IoT (SD-IoT). However, this integration and high flow of data generated by IoT devices raises serious security issues in the centralized control intelligence of SD-IoT. Motivated by the aforementioned challenges, we present a new Deep-Learning empowered Threat Hunting Framework named DLTHF to protect SD-IoT data and detect (binary and multi-vector) attack vectors. First, an automated unsupervised feature extraction module is designed that combines data perturbation-driven encoding and normalization-driven scaling with the proposed Long Short-Term Memory Contractive Sparse AutoEncoder (LSTMCSAE) method to filter and transform dataset values into the protected format. Second, using the encoded data, a novel Threat Detection System (TDS) using Multi-head Self-attention-based Bidirectional Recurrent Neural Networks (MhSaBiGRNN) is designed to detect cyber threats and their types. In particular, a unique TDS strategy is developed in which each time instances is analyzed and allocated a self-learned weight based on the degree of relevance. Further, we also design a deployment architecture for DLTHF in the SD-IoT network. The framework is rigorously evaluated on two new SD-IoT data sources to show its effectiveness.

Artificial-Intelligence-Based Condition Monitoring of Industrial Collaborative Robots: Detecting Anomalies and Adapting to Trajectory Changes

The increasing use of collaborative robots in smart manufacturing, owing to their flexibility and safety benefits, underscores a critical need for robust predictive maintenance strategies to prevent unexpected faults/failures of the machine. This paper focuses on fault detection and employs multivariate operational data from a universal robot to detect anomalies or early-stage faults using test data from designed anomalous conditions and artificial-intelligence-based anomaly detection techniques called autoencoders. The performance of three autoencoders, namely, a multi-layer-perceptron-based autoencoder, convolutional-neural-network-based autoencoder, and sparse autoencoder, was compared in detecting anomalies. The results indicate that the autoencoders effectively detected anomalies in the examined complex and noisy datasets with more than 93% overall accuracy and an F1 score exceeding 96% for the considered anomalous cases. Moreover, the integration of trajectory change detection and anomaly detection algorithms (i.e., the dynamic time warping algorithm and sparse autoencoder, respectively) was proposed for the local implementation of online condition monitoring. This integrated approach to anomaly detection and trajectory change provides a practical, adaptive, and economical solution for enhancing the reliability and safety of collaborative robots in smart manufacturing environments.

Joint Optimization-Based QoS and PAPR Reduction Technique for Energy-Efficient Massive MIMO System

Massive multiple input multiple output (MIMO) is an innovative wireless communication technology that significantly enhances the capacity and efficiency of data transmission in modern networks. Massive multiple input multiple output, despite benefits, faces difficulties in managing numerous antennas affecting signal quality, peak-to-average power ratio (PAPR), and energy efficiency due to its scale and complexity. However, overcoming these complexities is vital for unlocking massive multiple input multiple output’s potential in high-quality, energy-efficient wireless communication. A proposed joint optimization-based technique aims to address these issues in massive MIMO systems. In phase 1, joint optimization with quality of service maximizes capacity via power distribution, employing the hybrid spider wasp Fick’s law algorithm. In phase 2, zero-forcing with symbol-level linear precoding, especially the stacked convolutional sparse bidirectional long short-term memory autoencoder (SCS–BiLSTMAE) network, efficiently reduces peak-to-average power ratio, collaborating for reduced bit rate error and peak-to-average power ratio through adaptive mapping. Refining the model involves precise hyperparameter tuning using the enhanced influencer buddy optimization algorithm. The proposed framework not only demonstrates exceptional performance, but the metrics also underscore the model's effectiveness in addressing diverse challenges, establishing it as a robust solution for quality enhancement, peak-to-average power ratio reduction, and energy efficiency. Additionally, the proposed model attains 28.14% greater system capacity than existing methods.

Self-Supervised Marine Noise Learning with Sparse Autoencoder Network for Generative Target Magnetic Anomaly Detection

As an effective physical field feature to perceive ferromagnetic targets, magnetic anomaly is widely used in covert marine surveillance tasks. However, its practical usability is affected by the complex marine magnetic noise interference, making robust magnetic anomaly detection (MAD) quite a challenging task. Recently, learning-based detectors have been widely studied for the discrimination of magnetic anomaly signal and achieve superior performance than traditional rule-based detectors. Nevertheless, learning-based detectors require abundant data for model parameter training, which are difficult to access in practical marine applications. In practice, target magnetic anomaly data are usually expensive to acquire, while rich marine magnetic noise data are readily available. Thus, there is an urgent need to develop effective models to learn discriminative features from the abundant marine magnetic noise data for newly appearing target anomaly detection. Motivated by this, in this paper we formulate MAD as a single-edge detection problem and develop a self-supervised marine noise learning approach for target anomaly classification. Specifically, a sparse autoencoder network is designed to model the marine noise and restore basis geomagnetic field from the collected noisy magnetic data. Subsequently, reconstruction error of the network is used as a statistical decision criterion to discriminate target magnetic anomaly from cluttered noise. Finally, we verify the effectiveness of the proposed approach on real sea trial data and compare it with seven state-of-the-art MAD methods on four numerical indexes. Experimental results indicate that it achieves a detection accuracy of 93.61% and has a running time of 21.06 s on the test dataset, showing superior MAD performance over its counterparts.

Drive-by damage detection methodology for high-speed railway bridges using sparse autoencoders

Drive-by damage detection methodology for high-speed railway bridges using sparse autoencoders

A detection method of oil content for maize kernels based on CARS feature selection and deep sparse autoencoder feature extraction

To achieve accurate, rapid, and non-destructive oil content determination in maize, a neural fitting model that combines feature selection and feature extraction is proposed. Competitive adaptive re-weighted sampling (CARS) is used to choose features, while the deep sparse autoencoder (DSAE) network is utilized to extract features. The 160 spectral data from the near-infrared spectra public datasets of maize served as experimental samples. Several preprocessing approaches were tested, and the first-order derivatives fared the best. To capture the various degrees of abstract features in spectral data, SSAE networks with two or three hidden layers are developed. The number of nodes in the network's hidden layer (30, 50, 70, 100, 200, 300, respectively) and the network's sparsity parameter (0.001, 0.004, 0.007, 0.01, 0.04, 0.07, 0.1, 0.2, …, 1) are tuned to generate partial least squares regression (PLSR) models for testing the feature learning effect. The highest performance comes from a three-layer network with 200 hidden layer nodes and a sparsity parameter of 0.04. This network extracts three levels of features from the preprocessed spectral data: F1, F2, and F3. Full_cars, F1_cars, F2_cars, and F3_cars are obtained using the CARS algorithm for variable selection on the preprocessed spectral data, F1, F2, and F3, respectively. They are then integrated into a final feature set to build a neural-fitting network (FNN). After training and testing, the test set has a correlation coefficient of 0.964205 and an mean square error (MSE) of 0.00636809. Comparing this model to support vector machine regression (SVR), PLSR, and gaussian process regression (GPR), the findings demonstrate that the model described in this study outperforms the others in terms of fitting accuracy and generalization performance. To summarize, this paper introduces a new regression analysis method based on near-infrared spectra.

A Novel Perspective on the Quality Index for Fused Satellite Images

Blind Image Quality Assessment for Remote Sensing Applications has garnered a significant amount of interest from the community, presenting a problem that is both persistent and difficult to solve. The current quality indicators are, for the most part, in agreement with each individual's subjective perception. Traditional handcrafted quality measures focus on detecting low-level features such as contour, edge, color, texture, and shape. These are the attributes that will be measured. Nevertheless, it is possible that they will disregard the essential semantics that lie behind the warped image. Within the scope of popular deep learning, the process of acquiring multilayer features is a straightforward exercise. The unfortunate reality is that a significant number of these models either ignore superficial traits or focus only on high-level variables, which ultimately leads to poor prediction performance. In order to successfully portray varying degrees of distortion, the fusion features make advantage of the laws that govern the human visual system in order to extract both local and global components of information. In conclusion, the ultimate quality score is determined by considering the features of both the local and global levels. In this investigation, we developed two different NR-IQA methods. In one, a markov random field is used, while in the other, a sparse approximation variational autoencoder is established as the foundation. The effectiveness of our work is shown by the evaluation of multiple IQA datasets, where our model consistently generates findings that are at the leading edge of the field. This evaluation does not make use of any quality standards; rather, it compares it against a variety of well-known cutting-edge techniques and average opinion ratings based on the opinions of different people. Quality ratings are accurately predicted by the regression models that have been provided.

A novel approach to coral species classification using deep learning and unsupervised feature extraction

ABSTRACT This study presents a novel methodology to enhance coral species classification in underwater images by integrating Tri Convolutional Deep Autoencoders (TRI-CDAE) and Sparse Deep Autoencoders (SPDAE). TRI-CDAE employs three parallel CDAEs trained on distinct color spaces (RGB, HSV, LUV) to capture diverse features. These extracted features are fused and refined using SPDAE, promoting sparsity and enhancing discriminative power. The refined features are then classified using a softmax classifier. Evaluation on four coral image datasets shows exceptional performance, with recall (96.5%), F1 score (97.4%), precision (97.2%), accuracy (98.5%), Cohen’s J (0.959), Jaccard Index (0.971), Cohen’s Kappa (0.961), and Matthews Correlation Coefficient (0.982).

Detection of human movement by combining supervised machine learning and an embroidered textile capacitance sensor

This study contributes to respiratory pattern detection by introducing a fabric sensor utilizing capacitance measurement and a semi-supervised machine learning algorithm known as an AI-based autoencoder. The sensor, consisting of two embroidered electrodes composed of silver-coated conductive nylon filaments, leverages the body as a dielectric material. In the research, a garment-type respiratory sensor was employed to continuously monitor respiratory data during both static (standing) and dynamic (walking, brisk walking, running) actions. The sparse autoencoder algorithm was particularly employed for individual static and dynamic actions, effectively distinguishing respiratory patterns corresponding to various movements. In addition, the sparse autoencoder helps prevent overfitting, fundamentally minimizing errors between the compression and reconstruction of signals. The maximum number of epochs was set to 2000, and the target error was set at 0.005. All data were compared against the static walking as the training baseline. Ultimately, the root mean squared error (RMSE) between static postures averaged 0.1, while the RMSEs between dynamic actions of walking, brisk walking, and running were 0.61, 0.91, and 2.78, respectively. These results suggest that movement detection through error detection is practically feasible and possesses discernible capabilities.

Adaptive spectrum amplitude modulation method for rolling bearing fault frequency determination

Abstract Signal preprocessing and feature extraction are decisive factors in determining the frequency of bearing faults. The presence of noise interference in the status signal of rolling bearings often hampers accurate fault detection. Although there are various methods for preprocessing vibration signals in rolling bearings, they need further improvement in terms of enhancing fault feature expression and localizing fault frequency bands. This limitation significantly hinders the accuracy of fault frequency determination. In order to enhance the representation of fault information on the frequency spectrum, this study proposes a combined approach that incorporates sparse stacked autoencoder (SSAE), wavelet packet decomposition (WPD), and adaptive spectrum amplitude modulation (ASAM). The resulting method is referred to as SSAE-WPD-ASAM. Firstly, the bearing vibration signal is decomposed by wavelet packet according to the scale and frequency band of the signal. On this basis, the signal reconstruction is realized based on the wavelet packet coefficient and energy distribution in different frequency bands. Secondly, for the whole life cycle signal, the reconstructed signal is self-encoded by sparse stacked autoencoder to achieve dimensionality reduction of the reconstructed signal. Then, the spare reconstructed signal is subjected to ASAM. Finally, through envelope demodulation, peak detection of fault frequency and empirical fault frequency comparison, the specific fault types of rolling bearings are determined. The proposed method is verified by theoretical simulation and three groups of practical experiments. The results show that the proposed method has a significant improvement in diagnostic efficiency and accuracy compared with traditional diagnostic methods.

Efficient sparse spiking auto-encoder for reconstruction, denoising and classification

Auto-encoders are capable of performing input reconstruction, denoising, and classification through an encoder-decoder structure. Spiking Auto-Encoders (SAEs) can utilize asynchronous sparse spikes to improve power efficiency and processing latency on neuromorphic hardware. In our work, we propose an efficient SAE trained using only Spike-Timing-Dependant Plasticity (STDP) learning. Our auto-encoder uses the Time-To-First-Spike (TTFS) encoding scheme and needs to update all synaptic weights only once per input, promoting both training and inference efficiency due to the extreme sparsity. We showcase robust reconstruction performance on the Modified National Institute of Standards and Technology (MNIST) and Fashion-MNIST datasets with significantly fewer spikes compared to state-of-the-art SAEs by 1–3 orders of magnitude. Moreover, we achieve robust noise reduction results on the MNIST dataset. When the same noisy inputs are used for classification, accuracy degradation is reduced by 30%–80% compared to prior works. It also exhibits classification accuracies comparable to previous STDP-based classifiers, while remaining competitive with other backpropagation-based spiking classifiers that require global learning through gradients and significantly more spikes for encoding and classification of MNIST/Fashion-MNIST inputs. The presented results demonstrate a promising pathway towards building efficient sparse spiking auto-encoders with local learning, making them highly suited for hardware integration.

Optimizing MobileNetV2 for improved accuracy in early gastric cancer detection based on dynamic pelican optimizer

This paper presents an innovative framework for the automated diagnosis of gastric cancer using artificial intelligence. The proposed approach utilizes a customized deep learning model called MobileNetV2, which is optimized using a Dynamic variant of the Pelican Optimization Algorithm (DPOA). By combining these advanced techniques, it is feasible to achieve highly accurate results when applied to a dataset of endoscopic gastric images. To evaluate the performance of the model based on the benchmark, its data is divided into training (80 %) and testing (20 %) sets. The MobileNetV2/DPOA model demonstrated an impressive accuracy of 97.73 %, precision of 97.88 %, specificity of 97.72 %, sensitivity of 96.35 %, Matthews Correlation Coefficient (MCC) of 96.58 %, and F1-score of 98.41 %. These results surpassed those obtained by other well-known models, such as Convolutional Neural Networks (CNN), Mask Region-Based Convolutional Neural Networks (Mask R–CNN), U-Net, Deep Stacked Sparse Autoencoder Neural Networks (SANNs), and DeepLab v3+, in terms of most quantitative metrics. Despite the promising outcomes, it is important to note that further research is needed. Specifically, larger and more diverse datasets as well as exhaustive clinical validation are necessary to validate the effectiveness of the proposed method. By implementing this innovative approach in the detection of gastric cancer, it is possible to enhance the speed and accuracy of diagnosis, leading to improved patient care and better allocation of healthcare resources.