Activation Mapping Research Articles

Advancing AI Interpretability in Medical Imaging: A Comparative Analysis of Pixel-Level Interpretability and Grad-CAM Models

This study introduces the Pixel-Level Interpretability (PLI) model, a novel framework designed to address critical limitations in medical imaging diagnostics by enhancing model transparency and diagnostic accuracy. The primary objective is to evaluate PLI’s performance against Gradient-Weighted Class Activation Mapping (Grad-CAM) and achieve fine-grained interpretability and improved localization precision. The methodology leverages the VGG19 convolutional neural network architecture and utilizes three publicly available COVID-19 chest radiograph datasets, consisting of over 1000 labeled images, which were preprocessed through resizing, normalization, and augmentation to ensure robustness and generalizability. The experiments focused on key performance metrics, including interpretability, structural similarity (SSIM), diagnostic precision, mean squared error (MSE), and computational efficiency. The results demonstrate that PLI significantly outperforms Grad-CAM in all measured dimensions. PLI produced detailed pixel-level heatmaps with higher SSIM scores, reduced MSE, and faster inference times, showcasing its ability to provide granular insights into localized diagnostic features while maintaining computational efficiency. In contrast, Grad-CAM’s explanations often lack the granularity required for clinical reliability. By integrating fuzzy logic to enhance visual and numerical explanations, PLI can deliver interpretable outputs that align with clinical expectations, enabling practitioners to make informed decisions with higher confidence. This work establishes PLI as a robust tool for bridging gaps in AI model transparency and clinical usability. By addressing the challenges of interpretability and accuracy simultaneously, PLI contributes to advancing the integration of AI in healthcare and sets a foundation for broader applications in other high-stake domains.

A Hybrid Transfer Learning Framework for Brain Tumor Diagnosis

Brain tumors are among the most severe health challenges, necessitating early and precise diagnosis for effective treatment planning. This study introduces an optimized hybrid transfer learning (TL) framework for brain tumor classification using magnetic resonance imaging images. The proposed system integrates advanced preprocessing techniques, an ensemble of pretrained deep learning models, and explainable artificial intelligence (XAI) methods to achieve high accuracy and reliability. The methodology enhances image quality through noise reduction and contrast enhancement, facilitating robust feature extraction. The ensemble model combines VGG16 and ResNet152V2 architectures, achieving a classification accuracy of 99.47% on a challenging four‐class dataset. Additionally, gradient‐weighted class activation mapping and SHapley Additive exPlanations (SHAP)‐based XAI techniques provide visual and quantitative insights into model predictions, improving interpretability and clinical trust. This comprehensive framework demonstrates the potential of hybrid TL and XAI in advancing diagnostic accuracy and supporting clinical decision‐making for brain tumor detection. The results underscore its applicability in clinical settings, particularly in resource‐constrained environments.

Investigation of multi-input convolutional neural networks for the prediction of particleboard mechanical properties.

This study explored the potential of building an image-based quality control system for particleboard manufacturing. Single-layer particleboards were manufactured under 27 operating conditions and their modulus of elasticity (MOE) and the modulus of rupture (MOR) were determined. Subsequently, images of the upper surface, lower surface, and cross-section of each specimen were collected. Two types of convolutional neural networks (CNNs) were designed: a single-input CNN processing one image and a multi-input CNN capable of analyzing multiple images simultaneously. Their prediction accuracies were then compared. Among the single-input CNNs, the cross-sectional image yielded the best prediction accuracy for both the MOE and MOR. For multi-input CNNs, the combination of the upper surface and cross-sectional images produced the highest scores when the model merged the information from each image at early stage, outperforming single-input CNNs. Adding density information to multi-input CNNs significantly improved prediction accuracy for both MOE and MOR, achieving optimal results. Regression activation maps were constructed to visualize the image features that were strongly correlated with the predicted results. For MOE prediction, the precise location of phenol formaldehyde (PF) resin and particle alignment were crucial. For MOR prediction, the interface between particles and PF resin was the key.

Deep learning and explainable AI for classification of potato leaf diseases

The accurate classification of potato leaf diseases plays a pivotal role in ensuring the health and productivity of crops. This study presents a unified approach for addressing this challenge by leveraging the power of Explainable AI (XAI) and transfer learning within a deep Learning framework. In this research, we propose a transfer learning-based deep learning model that is tailored for potato leaf disease classification. Transfer learning enables the model to benefit from pre-trained neural network architectures and weights, enhancing its ability to learn meaningful representations from limited labeled data. Additionally, Explainable AI techniques are integrated into the model to provide interpretable insights into its decision-making process, contributing to its transparency and usability. We used a publicly available potato leaf disease dataset to train the model. The results obtained are 97% for validation accuracy and 98% for testing accuracy. This study applies gradient-weighted class activation mapping (Grad-CAM) to enhance model interpretability. This interpretability is vital for improving predictive performance, fostering trust, and ensuring seamless integration into agricultural practices.

Geomorphologic mapping and analysis of fault activities along the northern margin of the Yanfan Basin, China

Geomorphologic mapping and analysis of fault activities along the northern margin of the Yanfan Basin, China

Efficient proxy for time-lapse seismic forward modeling using a U-net encoder-decoder approach

The time-lapse seismic (4D seismic) forward modeling provides crucial data for calibrating reservoir models through different data assimilation algorithms. Unfortunately, the traditional 4D seismic forward-modeling methodology is time-expensive and entails significant computational resource consumption. To address these drawbacks, in this work, our goal is to develop a proxy model for the 4D seismic forward modeling using a class of machine learning algorithm named U-Net encoder–decoder. We applied the developed proxy model to a benchmark carbonate reservoir using an ensemble of reservoir simulation models from UNISIM IV dataset (a synthetic benchmark based on real data of a Brazilian pre-salt field). Moreover, we aim to introduce seminal strategies for interpreting the proposed proxy model operation, its outputs, and possible correlations between input and output variables. To achieve this, we trained and tested two versions of U-net-based models and applied methods for explainable artificial intelligence, such as Gradient-weighted Class Activation Mapping (Grad-CAM) and Forward Feature Selection. The experiments showed good results when applied to the test dataset. The correlation coefficient (R2) values were in the range of 0.7 to 0.9, showing the efficiency of the proxy model to replace the 4D seismic forward modeling. Through qualitative analysis, it was possible to identify which input properties and regions of the reservoir are more relevant for the model’s inference. These results are a step toward robust, explainable machine learning-based proxy forward modeling.

Development and validation of a deep learning–based pathomics signature for prognosis and chemotherapy benefits in colorectal cancer: A retrospective multicenter cohort study.

298 Background: The current TNM staging system fails to provide adequate information for prognosis and adjuvant chemotherapy benefits in colorectal cancer (CRC). Pathomics, an emerging field, shows promise in improving prognosis estimation and decision-making. In this study, we developed and validated a pathomics signature (PS CRC ) that directly analyzes hematoxylin and eosin–stained slides using deep learning to predict outcomes. Methods: A total of 883 whole slide images from two cohorts, Harbin Medical University Cancer Hospital and The Cancer Genome Atlas (TCGA), were retrospectively analyzed. An interpretable, multi-instance deep learning model was proposed to establish the PS CRC . Shapley additive explanations were employed to interpret the model's decisions, and gradient-weighted class activation mapping was applied to visualise the pathological phenotypes of the PS CRC . The transcriptomics data from TCGA cohort was used to explore the potential pathogenesis underlying the PS CRC . Results: The PS CRC was identified as an independent prognostic factor associated with both overall survival and disease-free survival. Incorporating the PS CRC into the TNM stage model resulted in a significant improvement in prognosis estimation, as evidenced by a notable increase in net reclassification improvement and integrated discrimination improvement. Moreover, among stage II and III CRC patients with low levels of PS CRC , satisfactory benefits from chemotherapy were observed. Notably, the main underlying features of PS CRC include tumor cell infiltration, adipocyte accumulation, fibrous tissue deposition, and stromal infiltration. Transcriptome analysis further support the relevance of PS CRC to tumor progression and immune suppression. Conclusions: Our finds highlight the significant potential of histopathology images-based deep learning in predicting prognosis and assess therapeutic response of CRC. The PS CRC could serve as an effective tool in clinical decision for CRC management, providing insights into the underlying pathogenic mechanisms. However, prospective studies are still necessary for further validation.

Revolutionizing Cesium Monitoring in Seawater through Electrochemical Voltammetry and Machine Learning

Monitoring radioactive cesium ions (Cs+) in seawater is vital for environmental safety but remains challenging due to limitations in the accessibility, stability, and selectivity of traditional methods. This study presents an innovative approach that combines electrochemical voltammetry using nickel hexacyanoferrate (NiHCF) thin-film electrode with machine learning (ML) to enable accurate and portable detection of Cs+. Optimizing the fabrication of NiHCF thin-film electrodes enabled the development of a robust sensor that generates cyclic voltammograms (CVs) sensitive to Cs⁺ concentrations as low as 1 ppb in synthetic seawater and 10 ppb in real seawater, with subtle changes in CV patterns caused by trace Cs⁺ effectively identified and analyzed using ML. Using 2D convolutional neural networks (CNNs), we classified Cs+ concentrations across eight logarithmic classes (0 – 106 ppb) with 100% accuracy and an F1-score of 1 in synthetic seawater datasets, outperforming the 1D CNN and deep neural networks. Validation using real seawater datasets confirmed the applicability of our model, achieving high performance. Moreover, gradient-weighted class activation mapping (Grad-CAM) identified critical CV regions that were overlooked during manual inspection, validating model reliability. This integrated method offers a sensitive and practical solutions for monitoring Cs+ in seawater, helping to prevent its accumulation in ecosystems.

Thermal field predictions and boiling curve reconstruction from bubble dynamics using conditional generative adversarial networks

Although the accurate prediction of boiling phenomena is crucial for preventing equipment damage and ensuring energy system safety, it remains challenging owing to the intertwined thermal–hydraulic interactions. We predicted the temperature field of boiling surfaces from the visualization results of bubble dynamics using conditional Generative Adversarial Networks (cGANs). The network was trained to generate infrared images from side-view images in a flow boiling experiment, and its rationale is supported by intermediate activation maps. Additionally, the model was applied to a data-deficient scenario based on pool boiling to derive heat flux components and reconstruct the boiling curve. This approach enhances the insight into both the visible and invisible aspects of boiling phenomena, which is particularly advantageous when direct and elaborate data acquisition faces facility limitations.

Interpretable Deep Learning Framework for COVID-19 Detection: Grad-CAM Integration with Pre-trained CNN Models on Chest X-Ray Images

This study is present a novel approach for interpretability enhancing of the deep learning models (EfficientNet, ResNet, VGG) that applied to COVID-19 diagnosis by using the Gradient-Weighted Class Activation Mapping (Grad-CAM) all that to make transparent decision-making improved. To do this we leveraging the capabilities of Grad-CAM, and we aim to provide not only accurate diagnostic predictions but also give a visual explanations, that support the professionals in the healthcare to understanding the underlying features that aided to the model’s decisions. This interpretability is important for building trust in the AI systems, especially in medical areas diagnosis that critical such. This interpretability is essential for building trust in the AI systems, especially in critical areas such as medical diagnosis, that is allowing healthcare professionals to understand the rationale behind the AI-generated recommendations and decisions. In the context of COVID-19, using techniques like Gradient-Weighted Class Activation Mapping (Grad-CAM) can provide insights into which features of medical imaging data contribute most significantly to the model’s predictions, this enhancing reliability and transparency of the AI system. This capability not only aids clinicians in understanding the rationale behind AI-driven diagnoses but it is also fosters greater trust in the automated systems, especially in high-stakes scenarios like healthcare. It is crucial this transparency is ensuring that healthcare professionals can make informed decisions based on the AI’s outputs. As the COVID-19 pandemic demonstrated, timely and accurate diagnosis is an essential for the effective patient management.

An explainable transformer model integrating PET and tabular data for histologic grading and prognosis of follicular lymphoma: a multi-institutional digital biopsy study.

Pathological grade is a critical determinant of clinical outcomes and decision-making of follicular lymphoma (FL). This study aimed to develop a deep learning model as a digital biopsy for the non-invasive identification of FL grade. This study retrospectively included 513 FL patients from five independent hospital centers, randomly divided into training, internal validation, and external validation cohorts. A multimodal fusion Transformer model was developed integrating 3D PET tumor images with tabular data to predict FL grade. Additionally, the model is equipped with explainable modules, including Gradient-weighted Class Activation Mapping (Grad-CAM) for PET images, SHapley Additive exPlanations analysis for tabular data, and the calculation of predictive contribution ratios for both modalities, to enhance clinical interpretability and reliability. The predictive performance was evaluated using the area under the receiver operating characteristic curve (AUC) and accuracy, and its prognostic value was also assessed. The Transformer model demonstrated high accuracy in grading FL, with AUCs of 0.964-0.985 and accuracies of 90.2-96.7% in the training cohort, and similar performance in the validation cohorts (AUCs: 0.936-0.971, accuracies: 86.4-97.0%). Ablation studies confirmed that the fusion model outperformed single-modality models (AUCs: 0.974 - 0.956, accuracies: 89.8%-85.8%). Interpretability analysis revealed that PET images contributed 81-89% of the predictive value. Grad-CAM highlighted the tumor and peri-tumor regions. The model also effectively stratified patients by survival risk (P < 0.05), highlighting its prognostic value. Our study developed an explainable multimodal fusion Transformer model for accurate grading and prognosis of FL, with the potential to aid clinical decision-making.

Enhancing furcation involvement classification on panoramic radiographs with vision transformers

BackgroundThe severity of furcation involvement (FI) directly affected tooth prognosis and influenced treatment approaches. However, assessing, diagnosing, and treating molars with FI was complicated by anatomical and morphological variations. Cone-beam computed tomography (CBCT) enhanced diagnostic accuracy for detecting FI and measuring furcation defects. Despite its advantages, the high cost and radiation dose associated with CBCT equipment limited its widespread use. The aim of this study was to evaluate the performance of the Vision Transformer (ViT) in comparison with several commonly used traditional deep learning (DL) models for classifying molars with or without FI on panoramic radiographs.MethodsA total of 1,568 tooth images obtained from 506 panoramic radiographs were used to construct the database and evaluate the models. This study developed and assessed a ViT model for classifying FI from panoramic radiographs, and compared its performance with traditional models, including Multi-Layer Perceptron (MLP), Visual Geometry Group (VGG)Net, and GoogLeNet.ResultsAmong the evaluated models, the ViT model outperformed all others, achieving the highest precision (0.98), recall (0.92), and F1 score (0.95), along with the lowest cross-entropy loss (0.27) and the highest accuracy (92%). ViT also recorded the highest area under the curve (AUC) (98%), outperforming the other models with statistically significant differences (p < 0.05), confirming its enhanced classification capability. The gradient-weighted class activation mapping (Grad-CAM) analysis on the ViT model revealed the key areas of the images that the model focused on during predictions.ConclusionDL algorithms can automatically classify FI using readily accessible panoramic images. These findings demonstrate that ViT outperforms the tested traditional models, highlighting the potential of transformer-based approaches to significantly advance image classification. This approach is also expected to reduce both the radiation dose and the financial burden on patients while simultaneously improving diagnostic precision.

Digital framework for georeferenced multiplatform surveillance of banana wilt using human in the loop AI and YOLO foundation models

Bananas (Musa spp.) are a critical global food crop, providing a primary source of nutrition for millions of people. Traditional methods for disease monitoring and detection are often time-consuming, labor-intensive, and prone to inaccuracies. This study introduces an AI-powered multiplatform georeferenced surveillance system designed to enhance the detection and management of banana wilt diseases. We developed and evaluated several deep learning foundation models, including YOLO-NAS, YOLOv8, YOLOv9, and Faster-RCNN to perform accurate disease detection on both platforms. Our results demonstrate the superior performance of YOLOv9 in detecting healthy, Fusarium Wilt and Xanthomonas Wilt diseased plants in aerial images, achieving high mAP@50, precision and recall metrics ranging from 55 to 86%. In terms of ground level images, we organized the dataset based on disease occurrence in Africa, Latin America, India, Asia and Australia. For this platform, YOLOv8 outperforms the rest and achieves mAP@50, precision and recall between 65 and 99% depending on the plant part and region. Additionally, we incorporated Explainable AI techniques, such as Gradient-weighted Class Activation Mapping, to enhance model transparency and trustworthiness. Human in the Loop Artificial Intelligence was also utilized to enhance the ground level model’s predictions.

Super-Resolved Mapping of Electrochemical Reactivity in Single 3D Catalysts.

Crystals with three-dimensional (3D) stereoscopic structures, characterized by diverse shapes, crystallographic planes, and morphologies, represent a significant advancement in catalysis. Differentiating and quantifying the catalytic activity of specific surface facets and sites at the single-particle level is essential for understanding and predicting catalytic performance. This study employs super-resolution radial fluctuations electrogenerated chemiluminescence microscopy (SRRF-ECLM) to achieve high-resolution mapping of electrocatalytic activity on individual 3D Cu2O crystals, including cubic, octahedral, and truncated octahedral structures. With a spatial resolution below 100 nm, SRRF-ECLM precisely delineates the contours of Cu2O crystals, enabling detailed analysis of activity distribution across distinct facets and interfaces. By quantitatively measuring ECL emission intensities from different planes and joint interfaces, we constructed 3D catalytic activity distributions, offering an intuitive and comprehensive perspective of single-catalyst activity. This approach advances single-particle electrochemical analysis and provides valuable insights for designing more efficient catalysts in energy conversion and chemical synthesis applications.

A deep learning pipeline for three-dimensional brain-wide mapping of local neuronal ensembles in teravoxel light-sheet microscopy.

Teravoxel-scale, cellular-resolution images of cleared rodent brains acquired with light-sheet fluorescence microscopy have transformed the way we study the brain. Realizing the potential of this technology requires computational pipelines that generalize across experimental protocols and map neuronal activity at the laminar and subpopulation-specific levels, beyond atlas-defined regions. Here, we present artficial intelligence-based cartography of ensembles (ACE), an end-to-end pipeline that employs three-dimensional deep learning segmentation models and advanced cluster-wise statistical algorithms, to enable unbiased mapping of local neuronal activity and connectivity. Validation against state-of-the-art segmentation and detection methods on unseen datasets demonstrated ACE's high generalizability and performance. Applying ACE in two distinct neurobiological contexts, we discovered subregional effects missed by existing atlas-based analyses and showcase ACE's ability to reveal localized or laminar neuronal activity brain-wide. Our open-source pipeline enables whole-brain mapping of neuronal ensembles at a high level of precision across a wide range of neuroscientific applications.

An endoscopic ultrasound-based interpretable deep learning model and nomogram for distinguishing pancreatic neuroendocrine tumors from pancreatic cancer

To retrospectively develop and validate an interpretable deep learning model and nomogram utilizing endoscopic ultrasound (EUS) images to predict pancreatic neuroendocrine tumors (PNETs). Following confirmation via pathological examination, a retrospective analysis was performed on a cohort of 266 patients, comprising 115 individuals diagnosed with PNETs and 151 with pancreatic cancer. These patients were randomly assigned to the training or test group in a 7:3 ratio. The least absolute shrinkage and selection operator algorithm was employed to reduce the dimensionality of deep learning (DL) features extracted from pre-standardized EUS images. The retained nonzero coefficient features were subsequently applied to develop predictive eight DL models based on distinct machine learning algorithms. The optimal DL model was identified and used to establish a clinical signature, which subsequently informed the construction and evaluation of a nomogram. Gradient-weighted Class Activation Mapping (Grad-CAM) and Shapley Additive Explanations (SHAP) were implemented to interpret and visualize the model outputs. A total of 2048 DL features were initially extracted, from which only 27 features with coefficients greater than zero were retained. The support vector machine (SVM) DL model demonstrated exceptional performance, achieving area under the curve (AUC) values of 0.948 and 0.795 in the training and test groups, respectively. Additionally, a nomogram was developed, incorporating both DL and clinical signatures, and was visually represented for practical application. Finally, the calibration curves, decision curve analysis (DCA) plots, and clinical impact curves (CIC) exhibited by the DL model and nomogram indicated high accuracy. The application of Grad-CAM and SHAP enhanced the interpretability of these models. These methodologies contributed substantial net benefits to clinical decision-making processes. A novel interpretable DL model and nomogram were developed and validated using EUS images, cooperating with machine learning algorithms. This approach demonstrates significant potential for enhancing the clinical applicability of EUS in predicting PNETs from pancreatic cancer, thereby offering valuable insights for future research and implementation.

CrackCLIP: Adapting Vision-Language Models for Weakly Supervised Crack Segmentation

Weakly supervised crack segmentation aims to create pixel-level crack masks with minimal human annotation, which often only differentiate between crack and normal no-crack patches. This task is crucial for assessing structural integrity and safety in real-world industrial applications, where manually labeling the location of cracks at the pixel level is both labor-intensive and impractical. Addressing the challenges of labeling uncertainty, this paper presents CrackCLIP, a novel approach that leverages language prompts to augment the semantic context and employs the Contrastive Language–Image Pre-Training (CLIP) model to enhance weakly supervised crack segmentation. Initially, a gradient-based class activation map is used to generate pixel-level coarse pseudo-labels from a trained crack patch classifier. The estimated coarse pseudo-labels are utilized to fine-tune additional linear adapters, which are integrated into the frozen image encoders of CLIP to adapt the CLIP model to the specialized task of crack segmentation. Moreover, specific textual prompts are crafted for crack characteristics, which are input into the frozen text encoder of CLIP to extract features encapsulating the semantic essence of the cracks. The final crack segmentation is determined by comparing the similarity between text prompt features and visual patch token features. Comparative experiments on the Crack500, CFD, and DeepCrack datasets demonstrate that the proposed framework outperforms existing weakly supervised crack segmentation methods, and the pre-trained vision-language model exhibits strong potential for crack feature learning, thereby enhancing the overall performance and generalization capabilities of the proposed framework.

Potential value of novel multiparametric MRI radiomics for preoperative prediction of microsatellite instability and Ki-67 expression in endometrial cancer

Exploring the potential of advanced artificial intelligence technology in predicting microsatellite instability (MSI) and Ki-67 expression of endometrial cancer (EC) is highly significant. This study aimed to develop a novel hybrid radiomics approach integrating multiparametric magnetic resonance imaging (MRI), deep learning, and multichannel image analysis for predicting MSI and Ki-67 status. A retrospective study included 156 EC patients who were subsequently categorized into MSI and Ki-67 groups. The hybrid radiomics model (HMRadSum) was developed by extracting quantitative imaging features and deep learning features from multiparametric MRI using emerging attention mechanism. Tumor markers were subsequently predicted utilizing an XGBoost classifier. Model performance and interpretability were evaluated using standard classification metrics, Gradient-weighted Class Activation Mapping (Grad-CAM), and SHapley Additive exPlanations (SHAP) techniques. For the MSI prediction task, the HMRadSum model achieved area-under-curve (AUC) value of 0.945 (95% CI 0.862-1.000) and accuracy of 0.889. For the Ki-67 prediction task, the AUC and accuracy of HMRadSum model was 0.888 (95% CI 0.743-1.000) and 0.810. This hybrid radiomics model effectively extracted features associated with EC gene expression, providing potential clinical implications for personalized diagnosis, treatment, and treatment strategy optimization.

A high-resolution view of RNA endonuclease cleavage in Bacillus subtilis.

RNA endonucleases are the rate-limiting initiator of decay for many bacterial mRNAs. However, the positions of cleavage and their sequence determinants remain elusive even for the well-studied Bacillus subtilis. Here we present two complementary approaches-transcriptome-wide mapping of endoribonucleolytic activity and deep mutational scanning of RNA cleavage sites-that reveal distinct rules governing the specificity among B. subtilis endoribonucleases. Detection of RNA terminal nucleotides in both 5'- and 3'-exonuclease-deficient cells revealed>103 putative endonucleolytic cleavage sites with single-nucleotide resolution. We found a surprisingly weak consensus for RNase Y targets, a contrastingly strong primary sequence motif for EndoA targets, and long-range intramolecular secondary structures for RNase III targets. Deep mutational analysis of RNase Y cleavage sites showed that the specificity is governed by many disjointed sequence features. Our results highlight the delocalized nature of mRNA stability determinants and provide a strategy for elucidating endoribonuclease specificity in vivo.

A BETTER CONSUMER UNDERSTANDING – A NEUROMARKETING APPROACH

The consumer mind-set is complex and not easily interpreted by anyone, as it encompasses their thoughts, statements, actions, and emotions. Therefore, marketers not only aim to identify and satisfy consumer needs but also consider their feelings. The exploration of consumer emotions is termed neuromarketing, a concept introduced by Dutch marketing professor Ale Smids at Harvard University in 1990. Neuromarketing is becoming a strategic method that enables the mapping of consumer brain activities and offers insights into their sensory and motor responses to various business marketing strategies. Techniques in neuromarketing aid in examining the human brain to forecast decision-making through brainwave activities, with methodologies such as eye tracking and skin response being scientifically analysed using fMRI (functional magnetic resonance imaging). This medical technology assesses brain function by observing changes linked to blood flow. Marketing experts utilize fMRI technology to identify fluctuations in brain activity and seek to comprehend which areas of the brain are activated when consumers make purchase decisions. On-going research aims to investigate, identify, and elucidate how businesses can integrate neuromarketing as a tool for understanding consumer behaviour. This research paper will be beneficial for marketers, academics, and organizations, as neuromarketing provides a fresh perspective on marketing practices and consumer psychology. Keywords - consumer, behaviour, sensorimotor, emotional, neuromarketing, marketing,