Multimodal Ensemble Research Articles

Ensemble Learning for Stellar Classification and Radius Estimation from Multimodal Data

Stellar classification and radius estimation are crucial for understanding the structure of the Universe and stellar evolution. With the advent of the era of astronomical big data, multimodal data are available and theoretically effective for stellar classification and radius estimation. A problem is how to improve the performance of this task by jointly using the multimodal data. However, existing research primarily focuses on using single-modal data. To this end, this paper proposes a model, Multi-Modal SCNet, and its ensemble model Multimodal Ensemble for Stellar Classification and Regression (MESCR) for improving stellar classification and radius estimation performance by fusing two modality data. In this problem, a typical phenomenon is that the sample numbers of some types of stars are evidently more than others. This imbalance has negative effects on model performance. Therefore, this work utilizes a weighted sampling strategy to deal with the imbalance issues in MESCR. Some evaluation experiments are conducted on a test set for MESCR and the classification accuracy is 96.1%, and the radius estimation performance Mean of Absolute Error and σ are 0.084 dex and 0.149 R ⊙, respectively. Moreover, we assessed the uncertainty of model predictions, confirming good consistency within a reasonable deviation range. Finally, we applied our model to 50,871,534 SDSS stars without spectra and published a new catalog.

Assessing the influence of climate change on multiple climate indices in Nepal using CMIP6 global climate models

Global climate models (GCMs) serve as essential tools for projecting future climate trends, but their coarse resolution limits localized impact assessments in sectors like hydrology, agriculture, and biodiversity. Observation data with a spatial resolution of a few kilometers are crucial for downscaling and bias-correcting GCMs at finer resolutions. However, Nepal's extreme topography and organizational challenges have led to uneven distribution of meteorological stations and inconsistent data quality. Moreover, CMIP6-based climate extremes projections for the entire country are currently unavailable. To tackle these challenges, we developed a comprehensive national database for Nepal, offering high-resolution historical and projected precipitation and temperature data analyzed through 25 climate extreme indices from the Expert Team on Climate Change Detection and Indices (ETCCDI). Initially, observation grid data were prepared at a daily timescale with a spatial resolution of 0.05° × 0.05° for baseline period (1981–2010) using the Asian Precipitation High-Resolved Observational Data Integration Toward Evaluation (APHRODITE), the fifth generation of the European Centre for Medium-Range Weather Forecasts Reanalysis (ERA5), and available good quality observed climate data. This data was then utilized to downscale and bias-correct 18 CMIP6 GCMs for 2015–2100 under four SSPs (1–2.6, 2–4.5, 3–7.0, 5–8.5). Quantile mapping was employed for the bias correction of the CMIP6 GCMs. The performance of the multimodal ensemble (MME) indicated better Nash-Sutcliffe Efficiency (NSE), root mean square error ratio (RSR), and Percent Bias (PBIAS) of climate extreme indices for the historical period. A comparative analysis was conducted across Nepal's major geographic regions to account for spatial variability in regional climate systems. The finer-resolution dataset can be crucial to deepen our understanding of climate impacts, and climate change, and eventually informing the policy-making in Nepal. Moreover, the methodology can be effectively replicated in data-scarce developing nations to promote climate research and adaptation efforts.

Future Projection of Water Resources of Ruzizi River Basin: What Are the Challenges for Management Strategy?

This study investigates the impact of climate change on hydrological dynamics in the Ruzizi River Basin (RRB) by leveraging a combination of observational historical data and downscaled climate model outputs. The primary objective is to evaluate changes in precipitation, temperature, and water balance components under different climate scenarios. We employed a multi-modal ensemble (MME) approach to enhance the accuracy of climate projections, integrating historical climate data spanning from 1950 to 2014 with downscaled projections for the SSP2-4.5 and SSP5-8.5 scenarios, covering future periods from 2040 to 2100. Our methodology involved calibrating and validating the SWAT model against observed hydrological data to ensure reliable simulations of future climate scenarios. The model’s performance was assessed using metrics such as R2, NSE, KGE, and PBIAS, which closely aligned with recommended standards. Results reveal a significant decline in mean annual precipitation, with reductions of up to 37.86% by mid-century under the SSP5-8.5 scenario. This decline is projected to lead to substantial reductions in surface runoff, evapotranspiration, and water yield, alongside a marked decrease in mean monthly stream flow, critically impacting agricultural, domestic, and ecological water needs. The study underscores the necessity of adaptive water resource management strategies to address these anticipated changes. Key recommendations include implementing a dynamic reservoir operation system, enhancing forecasting tools, and incorporating green infrastructure to maintain water quality, support ecosystem resilience, and ensure sustainable water use in the RRB. This research emphasizes the need for localized strategies to address climate-driven hydrological changes and protect future water resources.

Model-agnostic confidence measurement for aggregating multimodal ensemble models in automatic diagnostic systems

Automatic diagnostic systems (ADSs) have been garnering increased attention because they can alleviate the workload of clinicians by assisting in diagnosis and offering low-cost access to healthcare for people in medically underserved areas. ADS can suggest potential diseases by analyzing a patient's self-report. Previous research on ADS has leveraged diagnostic case data from various patients and medical knowledge to diagnose diseases, with multimodal ensemble methods proving particularly effective. However, the existing multimodal ensemble method combines the probabilities of different models in the aggregating process, which can not properly combine the probabilities that are produced by different criteria. To address these issues, we propose an effective aggregation framework for multimodal ensembles that can properly aggregate model-agnostic confidence scores and predictions from each model. Our framework transforms probability scores from different criteria into unified aggregation rule-based scores and reflects the gap between the probabilities that may be blurred in the aggregation process through the confidence score. In particular, The proposed confidence measurement method employs a post-analysis approach with the developed model or algorithm, making it adaptable in a model-agnostic manner and suitable for multimodal ensemble learning that utilizes heterogeneous prediction results. Our experimental results demonstrate that our framework outperforms existing approaches by more effectively leveraging the strengths of each ensemble member.

Chinese Online Celebrities’ Discursive Strategies of Promoting Domestic Products: Online Posters as Multimodal Ensembles

Although Chinese online celebrities have received much scholarly attention, scant studies pay attention to the crucial role they play in promoting the emerging “buy national” in China. This study investigates the multimodal discourse conveyed in a collection of advertising posters designed by online celebrities to promote Chinese domestic products (known as guohuo in China). Through a social semiotic lens, it provides a multimodal analysis of how online posters employ visual imaginary and textual messages for commercial purposes within the rising consumer nationalistic context. Drawing upon a dataset of 20 online posters, the analysis reveals a set of semiotic resources including color, image, typography, layout and non-linguistic gesture and gaze. These modes are employed collectively to orchestrate the national script and produce the authoritative representatives that convert the online posters into a kind of national multimodal ensemble. The study highlights the extensive spread of online posters and provides new insights for analyzing the visibility of online celebrities in the growing wave of “buy national” in China.

A Multimodal Ensemble Deep Learning Model for Functional Outcome Prognosis of Stroke Patients.

The accurate prediction of functional outcomes in patients with acute ischemic stroke (AIS) is crucial for informed clinical decision-making and optimal resource utilization. As such, this study aimed to construct an ensemble deep learning model that integrates multimodal imaging and clinical data to predict the 90-day functional outcomes after AIS. We used data from the Korean Stroke Neuroimaging Initiative database, a prospective multicenter stroke registry to construct an ensemble model integrated individual 3D convolutional neural networks for diffusion-weighted imaging and fluid-attenuated inversion recovery (FLAIR), along with a deep neural network for clinical data, to predict 90-day functional independence after AIS using a modified Rankin Scale (mRS) of 3-6. To evaluate the performance of the ensemble model, we compared the area under the curve (AUC) of the proposed method with that of individual models trained on each modality to identify patients with AIS with an mRS score of 3-6. Of the 2,606 patients with AIS, 993 (38.1%) achieved an mRS score of 3-6 at 90 days post-stroke. Our model achieved AUC values of 0.830 (standard cross-validation [CV]) and 0.779 (time-based CV), which significantly outperformed the other models relying on single modalities: b-value of 1,000 s/mm2 (P<0.001), apparent diffusion coefficient map (P<0.001), FLAIR (P<0.001), and clinical data (P=0.004). The integration of multimodal imaging and clinical data resulted in superior prediction of the 90-day functional outcomes in AIS patients compared to the use of a single data modality.

Image-based security techniques for water critical infrastructure surveillance

Security-related image analysis of critical infrastructures involves using advanced technologies to analyze visual data to identify potential threats, vulnerabilities, or abnormal activities. This type of analysis can be crucial for safeguarding critical infrastructure such as water management systems, power plants, transportation systems, communication networks, and more. In this paper, we propose two-factor-based authentication and pose-based monitoring tasks for water critical infrastructures. In the first phase, we detect and recognize the faces of authorized persons and check their liveness for anti-spoofing by eye-clicking checking and multimodal ensemble methods. In the second phase, we create our dataset, WCI-Pose, and we use four pose-based approaches – OpenPose, AlphaPose, Kapao, and YOLOv8 – for human action recognition. Experimental results show that OpenPose algorithm provides more favorable results compared to the other algorithms with an accuracy of 92.4%. SVM with multimodal data has the best performance score with an accuracy of 92% for face anti-spoofing.

Abstract 2411: A deep learning-based multimodal ensemble algorithm for lung cancer early detection with cross-ethnic generalizability

Abstract Background: Lung cancer is the leading cause of cancer-related mortality worldwide, and early detection is crucial for improving patient outcomes. Cell-free DNA (cfDNA) whole genome sequencing (WGS) is a non-invasive technique that has the potential to detect lung cancer early, but it is challenging to develop accurate and generalizable detection algorithms due to the low abundance of tumor-derived cfDNA in early-stage cancer. Methods: We developed a novel deep learning-based multimodal ensemble algorithm that combined two independent models: FEMS (fragment end motif frequency and size) and COV (coverage of genome positions). To assess the performance and ethnic generalizability of our algorithm, we evaluated it on three distinct cohorts: a Korean development cohort, a Korean external cohort, and a Caucasian external cohort. The Korean development cohort for model development comprised 236 lung cancer samples (stage I: 30%, stage II: 12%, stage III: 24%, stage IV: 20%, unknown: 14%) and 2205 normal samples. The Korean external cohort for performance evaluation of the model comprised 288 lung cancer samples (stage I: 31%, stage II: 13%, stage III: 26%, stage IV: 19%, unknown: 12%) and 1463 normal samples. And the Caucasian external cohort for confirm the ethnic generalizability consisted of 126 lung cancer samples (stage I: 17%, stage II: 12%, stage III: 37%, stage IV: 32%, unknown: 3%) and 93 normal samples. All data were generated using Novaseq6000 with a minimum of 40 million reads. Results: Our multimodal ensemble algorithm achieved high sensitivity and specificity in all three cohorts. The Korean development cohort had an overall sensitivity of 94.1% (95% CI: 90.7% to 97%) and specificity of 85%. The Korean external cohort had an overall sensitivity of 90.4% (95% CI: 82.7% to 98.1%) and specificity of 82%. The Caucasian external cohort had an overall sensitivity of 92.1% (95% CI: 87.3% to 96%) and specificity of 82.8%. The Caucasian cohort also had a sensitivity of 80% for early-stage lung cancer. Conclusion: Our deep learning-based multimodal ensemble algorithm is a promising tool for early lung cancer detection using low-coverage cell-free whole genome sequencing (LC-cfWGS). It performed well on three distinct cohorts, including a Caucasian cohort, suggesting that it is robust and generalizable to different populations. This makes it a potential candidate for clinical implementation. Citation Format: Tae-Rim Lee, Jin Mo Ahn, Junnam Lee, Dasom Kim, Byeong-Ho Jeong, Dongryul Oh, Mengchi Wang, Michael Salmans, Andrew Carson, Bryan Leatham, Kristin Fathe, Byung In Lee, Chang-Seok Ki, Young Sik Park, Eun-Hae Cho. A deep learning-based multimodal ensemble algorithm for lung cancer early detection with cross-ethnic generalizability [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2411.

Abstract 4800: Reducing health disparities for prostate adenocarcinoma by integrating multi-omics data via a multi-modal transfer learning approach

Abstract Prostate adenocarcinoma (PRAD) is the most common subtype of prostate cancer, which is the second leading cause of cancer death in American men. Although significant progress has been made to improve PRAD prognosis, existing studies indicated that Black American men have disproportionately high incidence and mortality rate in prostate cancer compared with non-Hispanic White American men. With artificial intelligence (AI) and machine learning (ML) being increasingly applied to PRAD research and clinical decision-making, PRAD data disparities would introduce bias to AI/ML models and further enhance negative impacts on healthcare towards underrepresented groups. Transfer learning has shown potential to reduce racial disparities in PRAD. However, its performance may be deteriorated due to: (1) its model requires large-scale training samples which are difficult to obtain in clinical settings, and (2) it only uses single-omics data without integrating multi-omics information. To address these concerns, we propose to develop a multi-modal transfer learning model to integrate multi-omics data for reducing health disparities. Specifically, we first investigated two multi-modal ensemble methods, Pearson Correlation Coefficient (PCC) based patient-pairwise similarity, and variational autoencoder (VAE) to integrate different types of omics data. Then, we leveraged a transfer learning model based on domain adaptation to pre-train the model on the majority group (White Americans) and fine-tune the model using the minority group (Black Americans). To further address the imbalanced data among ethnic groups, we explored implementing a data augmentation method, Synthetic Minority Oversampling Technique (SMOTE), to increase the size of minority group data. We evaluated our model on multi-omics data (mRNA, miRNA and methylation) of PRAD from The Cancer Genome Atlas (TCGA) database. Results suggested that our proposed approach achieved better performance of reducing health disparities for Black Americans compared with the mixture model and the independent model as well as the conventional transfer learning model in predicting progression-free interval (PFI) prognosis for PRAD patients. Furthermore, we also demonstrated that SMOTE alleviated the data imbalanced problem and improved the performance of the prognosis classification for ethnic minority groups. In summary, our results demonstrated that our proposed multi-modal transfer learning approach could effectively reduce the performance gap between majority and minority groups in PRAD prognosis, thereby mitigating health disparities. We expect that our proposed multi-model transfer learning framework can be customized and extensible to reduce health disparities in other types of cancer. Citation Format: Lusheng Li, Jieqiong Wang, Shibiao Wan. Reducing health disparities for prostate adenocarcinoma by integrating multi-omics data via a multi-modal transfer learning approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4800.

Divergent path: isolating land use and climate change impact on river runoff

Water resource management requires a thorough examination of how land use and climate change affect streamflow; however, the potential impacts of land-use changes are frequently ignored. Therefore, the principal goal of this study is to isolate the effects of anticipated climate and land-use changes on streamflow at the Indus River, Besham, Pakistan, using the Soil and Water Assessment Tool (SWAT). The multimodal ensemble (MME) of 11 general circulation models (GCMs) under two shared socioeconomic pathways (SSPs) 245 and 585 was computed using the Taylor skill score (TSS) and rating metric (RM). Future land use was predicted using the cellular automata artificial neural network (CA-ANN). The impacts of climate change and land-use change were assessed on streamflow under various SSPs and land-use scenarios. To calibrate and validate the SWAT model, the historical record (1991–2013) was divided into the following two parts: calibration (1991–2006) and validation (2007–2013). The SWAT model performed well in simulating streamflow with NSE, R2, and RSR values during the calibration and validation phases, which are 0.77, 0.79, and 0.48 and 0.76, 0.78, and 0.49, respectively. The results show that climate change (97.47%) has a greater effect on river runoff than land-use change (2.53%). Moreover, the impact of SSP585 (5.84%–19.42%) is higher than that of SSP245 (1.58%–4%). The computed impacts of climate and land-use changes are recommended to be incorporated into water policies to bring sustainability to the water environment.

MultiModal Ensemble Approach Leveraging Spatial, Skeletal, and Edge Features for Enhanced Bangla Sign Language Recognition

MultiModal Ensemble Approach Leveraging Spatial, Skeletal, and Edge Features for Enhanced Bangla Sign Language Recognition

Multi-Modal Ensemble Deep Learning in Head and Neck Cancer HPV Sub-Typing.

Oropharyngeal Squamous Cell Carcinoma (OPSCC) is one of the common forms of heterogeneity in head and neck cancer. Infection with human papillomavirus (HPV) has been identified as a major risk factor for OPSCC. Therefore, differentiating the HPV-positive and negative cases in OPSCC patients is an essential diagnostic factor influencing future treatment decisions. In this study, we investigated the accuracy of a deep learning-based method for image interpretation and automatically detected the HPV status of OPSCC in routinely acquired Computed Tomography (CT) and Positron Emission Tomography (PET) images. We introduce a 3D CNN-based multi-modal feature fusion architecture for HPV status prediction in primary tumor lesions. The architecture is composed of an ensemble of CNN networks and merges image features in a softmax classification layer. The pipeline separately learns the intensity, contrast variation, shape, texture heterogeneity, and metabolic assessment from CT and PET tumor volume regions and fuses those multi-modal features for final HPV status classification. The precision, recall, and AUC scores of the proposed method are computed, and the results are compared with other existing models. The experimental results demonstrate that the multi-modal ensemble model with soft voting outperformed single-modality PET/CT, with an AUC of 0.76 and F1 score of 0.746 on publicly available TCGA and MAASTRO datasets. In the MAASTRO dataset, our model achieved an AUC score of 0.74 over primary tumor volumes of interest (VOIs). In the future, more extensive cohort validation may suffice for better diagnostic accuracy and provide preliminary assessment before the biopsy.

Advertising translation in social media

Abstract In the multimodal era, communication in social media incorporates new codes and elements that challenge traditional definitions of text and the translation field itself. Integrating semiotics, understanding that modes follow a particular hierarchical order, and analyzing how they influence the (re)construction of meaning in certain communities is key to studying transnational hybrid messages, both global and local, in virtual environments. The translation act has proven to be a particularly useful circumstance to understand that modes should not be regarded as signs that carry meaning across cultures. Instead, this paper aims to identify which elements act as “prompts,” which (re)activate meaning once the message is repositioned in an alternative cultural context. Through a contrastive analysis of ten transpositions, in Kress’s terms, of a Burger King campaign, this work aims to shed light on which elements can be defined as modes and prompts, as well as reflecting upon its hierarchical status in this particular multimodal ensemble, while considering that, in the virtual space, other factors such as repetition and simultaneity may condition the impact and reception of global messages.

Scrutinizing video use in primary English classrooms

Abstract Despite the popularity of video in ELT, video use in primary English classrooms remains under-scrutinized. Grounded in a social semiotic conceptualization of video as multimodal ensemble, this study explored 115 Chinese primary English teachers’ use of videos and the influencing factors in the post-pandemic era. Data were collected through a narrative frame, semi-structured interviews, and classroom observations. The findings revealed a predominant use of video for motivational and linguistic purposes, with occasional use of video as multimodal ensemble for critical and creative viewing purposes. Factors that shaped video use were also identified, including limited understanding of viewing multimodal texts as part of language competence, absence of institutional policies and support, and constrained access to quality videos. These findings call for attention to the importance of multimodal awareness for primary English teachers to develop pedagogic competences to use videos for an expanded range of literacy purposes in contemporary ELT landscape.

Memes as multimodal ensemble

Abstract Memes have now become a common medium of communication. There are multiple ways memes are considered in academia. Semiotics offers information on how the media and modes that memes consist of can be interpreted and how the characteristics of semiotic resources apply to memes. Drawing from a pool of memes collected during the Kerala assembly election in 2021, this research argues that certain memes need to be categorised as multimodal ensemble. Different modalities play different roles meaning construction, and they also collaborate with each other for a uniform purpose. By comparing existing memes defined in academia and multiple methodologies to analyse memes, the paper puts forth a framework to analyse memes.

A Deep Learning-Based Multimodal Architecture to predict Signs of Dementia

This paper proposes a multimodal deep learning architecture combining text and audio information to predict dementia, a disease which affects around 55 million people all over the world and makes them in some cases dependent people. The system was evaluated on the DementiaBank Pitt Corpus dataset, which includes audio recordings as well as their transcriptions for healthy people and people with dementia. Different models have been used and tested, including Convolutional Neural Networks (CNN) for audio classification, Transformers for text classification, and a combination of both in a multimodal ensemble. These models have been evaluated on a test set, obtaining the best results by using the text modality, achieving 90.36% accuracy on the task of detecting dementia. Additionally, an analysis of the corpus has been conducted for the sake of explainability, aiming to obtain more information about how the models generate their predictions and identify patterns in the data.

An Integrated Ensemble Network Model for Skin Abnormality Detection with Combined Textural Features.

Melanoma is the most lethal of all skin cancers. This necessitates the need for a machine learning-driven skin cancer detection system to help medical professionals with early detection. We propose an integrated multi-modal ensemble framework that combines deep convolution neural representations with extracted lesion characteristics and patient meta-data. This study intends to integrate transfer-learned image features, global and local textural information, and patient data using a custom generator to diagnose skin cancer accurately. The architecture combines multiple models in a weighted ensemble strategy, which was trained and validated on specific and distinct datasets, namely, HAM10000, BCN20000 + MSK, and the ISIC2020 challenge datasets. They were evaluated on the mean values of precision, recall or sensitivity, specificity, and balanced accuracy metrics. Sensitivity and specificity play a major role in diagnostics. The model achieved sensitivities of 94.15%, 86.69%, and 86.48% and specificity of 99.24%, 97.73%, and 98.51% for each dataset, respectively. Additionally, the accuracy on the malignant classes of the three datasets was 94%, 87.33%, and 89%, which is significantly higher than the physician recognition rate. The results demonstrate that our weighted voting integrated ensemble strategy outperforms existing models and could serve as an initial diagnostic tool for skin cancer.

SusRec: An Approach to Sustainable Developer Recommendation for Bug Resolution Using Multimodal Ensemble Learning

The sustainability of an open source project is essential for the long-term and reliable development of software. Most existing studies focus on the recommendation accuracy of bug report assignment while ignoring inexperienced developers in the open source community. This gives inexperienced developers less opportunity to resolve bugs and can cause them to gradually lose interest in the development of open source software (OSS). To address this problem, this article proposes a novel approach called sustainable recommender (SusRec) to make sustainable report assignments without sacrificing accuracy. The SusRec approach is based on multimodal learning and ensemble learning, and it consists of two stages: the preprocessing stage and the developer scoring stage. In the preprocessing stage, the approach selects candidate developers who have participated in the resolution of bugs under the product of a new bug report. It then divides the candidate developers into three types—core developers, active developers, and peripheral developers—according to their experience. In the developer scoring stage, multimodal learning is adopted to score the three types of bug report–developer pairs, and ensemble learning is adopted to weight the scores of the three types of bug report–developer pairs and recommend developers for bug reports. We conduct extensive experiments using the bug repositories of the Eclipse and Mozilla projects to compare the proposed SusRec approach with the baseline methods in bug report assignment. The results demonstrate that the proposed SusRec approach cannot only improve the accuracy of developer recommendations for bug reports, but also the sustainability of OSS projects by providing more opportunities for active developers and peripheral developers to participate in bug resolution.

A hybrid learning framework for multi-modal facial prediction and recognition using improvised non-linear SVM classifier

Multi-modal classification plays a vital role for the real-time applications since most of the conventional models are independent of homogeneous features with single classifier. In addition, traditional directional gradient descriptors are difficult to find the heterogeneous features on different biometric classification. Feature extraction, segmentation, and multi-modal classification are the essential key factors that improve the true positive rate, error rate, and false positive rate of human-based recognition systems. In this work, a hybrid ensemble-based feature selection ranking measure, hybrid segmentation, and ensemble multi-class multi-modal classification framework on different biometric features is designed. Experimental results show that the proposed multi-class multi-modal ensemble classification framework has better optimization in terms of false positive rate, error rate, and precision than the conventional homogeneous local gradient feature extraction-based classification models on different features.

Hybrid Decision Fusion based Multimodal Ensemble Framework for Cervical Cancer Detection

Cervical cancer is fourth in the list of cancers that affect women. It has remained the main cause of death for women in developing nations. The cancer is spread through human papilloma virus (HPV), which is sexually transmitted. Pap smear and colposcopy image analysis remain prominent methods of diagnosis. These screening tests require skilled diagnostic experts, a scarce resource in developing countries thus restricting the effectiveness of the cancer detection process in large scale. Machine learning and deep learning are branches of artificial intelligence that are being used increasingly in cancer diagnosis. This study proposes a novel hybrid intelligent system for cervical cancer detection. A hybrid model of feature extraction and feature fusion is proposed for merging the two-state image and clinical data. Subsequently a machine learning ensemble learner is assembled to classify the features. The model performed with a satisfactory accuracy of 96.16%. Our results show that our method outperforms state of the art approaches and archives better, dependable accuracy.