Ensemble Of Convolutional Neural Networks Research Articles

Learning from virtual experiments to assist users of Small Angle Neutron Scattering in model selection

In this work, we combine the advantages of virtual Small Angle Neutron Scattering (SANS) experiments carried out by Monte Carlo simulations with the recent advances in computer vision to generate a tool that can assist SANS users in small angle scattering model selection. We generate a dataset of almost 260.000 SANS virtual experiments of the SANS beamline KWS-1 at FRM-II, Germany, intended for Machine Learning purposes. Then, we train a recommendation system based on an ensemble of Convolutional Neural Networks to predict the SANS model from the two-dimensional scattering pattern measured at the position-sensitive detector of the beamline. The results show that the CNNs can learn the model prediction task, and that this recommendation system has a high accuracy in the classification task on 46 different SANS models. We also test the network with real data and explore the outcome. Finally, we discuss the reach of counting with the set of virtual experimental data presented here, and of such a recommendation system in the SANS user data analysis procedure.

Multimodal breast cancer hybrid explainable computer-aided diagnosis using medical mammograms and ultrasound Images

Breast cancer is a prevalent global disease where early detection is crucial for effective treatment and reducing mortality rates. To address this challenge, a novel Computer-Aided Diagnosis (CAD) framework leveraging Artificial Intelligence (AI) techniques has been developed. This framework integrates capabilities for the simultaneous detection and classification of breast lesions. The AI-based CAD framework is meticulously structured into two pipelines (Stage 1 and Stage 2). The first pipeline (Stage 1) focuses on detectable cases where lesions are identified during the detection task. The second pipeline (Stage 2) is dedicated to cases where lesions are not initially detected. Various experimental scenarios, including binary (benign vs. malignant) and multi-class classifications based on BI-RADS scores, were conducted for training and evaluation. Additionally, a verification and validation (V&V) scenario was implemented to assess the reliability of the framework using unseen multimodal datasets for both binary and multi-class tasks. For the detection tasks, the recent AI detectors like YOLO (You Only Look Once) variants were fine-tuned and optimized to localize breast lesions. For classification tasks, hybrid AI models incorporating ensemble convolutional neural networks (CNNs) and the attention mechanism of Vision Transformers were proposed to enhance prediction performance. The proposed AI-based CAD framework was trained and evaluated using various multimodal ultrasound datasets (BUSI and US2) and mammogram datasets (MIAS, INbreast, real private mammograms, KAU-BCMD, and CBIS-DDSM), either individually or in merged forms. Visual t-SNE techniques were applied to visually harmonize data distributions across ultrasound and mammogram datasets for effective various datasets merging. To generate visually explainable heatmaps in both pipelines (stages 1 and 2), Grad-CAM was utilized. These heatmaps assisted in finalizing detected boxes, especially in stage 2 when the AI detector failed to automatically detect breast lesions. The highest evaluation metrics achieved for merged dataset (BUSI, INbreast, and MIAS) were 97.73% accuracy and 97.27% mAP50 in the first pipeline. In the second pipeline, the proposed CAD achieved 91.66% accuracy with 95.65% mAP50 on MIAS and 95.65% accuracy with 96.10% mAP50 on the merged dataset (INbreast and MIAS). Meanwhile, exceptional performance was demonstrated using BI-RADS scores, achieving 87.29% accuracy, 91.68% AUC, 86.72% mAP50, and 64.75% mAP50-95 on a combined dataset of INbreast and CBIS-DDSM. These results underscore the practical significance of the proposed CAD framework in automatically annotating suspected lesions for radiologists.

Development of an ensemble CNN model with explainable AI for the classification of gastrointestinal cancer.

The implementation of AI assisted cancer detection systems in clinical environments has faced numerous hurdles, mainly because of the restricted explainability of their elemental mechanisms, even though such detection systems have proven to be highly effective. Medical practitioners are skeptical about adopting AI assisted diagnoses as due to the latter's inability to be transparent about decision making processes. In this respect, explainable artificial intelligence (XAI) has emerged to provide explanations for model predictions, thereby overcoming the computational black box problem associated with AI systems. In this particular research, the focal point has been the exploration of the Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME) approaches which enable model prediction explanations. This study used an ensemble model consisting of three convolutional neural networks(CNN): InceptionV3, InceptionResNetV2 and VGG16, which was based on averaging techniques and by combining their respective predictions. These models were trained on the Kvasir dataset, which consists of pathological findings related to gastrointestinal cancer. An accuracy of 96.89% and F1-scores of 96.877% were attained by our ensemble model. Following the training of the ensemble model, we employed SHAP and LIME to analyze images from the three classes, aiming to provide explanations regarding the deterministic features influencing the model's predictions. The results obtained from this analysis demonstrated a positive and encouraging advancement in the exploration of XAI approaches, specifically in the context of gastrointestinal cancer detection within the healthcare domain.

CNN ensemble approach for early detection of sugarcane diseases – a comparison

This paper mainly concentrates and discusses on sugarcane crop, the variety of cane seeds available for sowing; various cane diseases and its early detection using different approaches. Machine Learning (ML) and Deep Learning (DL) techniques are used to analyze agricultural data like temperature, soil quality, yield prediction, selling price forecasts, etc. and avoid crop damage from a variety of sources, including diseases. In the proposed work, with particular reference to eight specific sugarcane crop diseases and including healthy crop database, the neural network algorithms are tested and verified in terms quality metrics like accuracy, F1 score, recall and precision.

High-resolution mapping of tree species and associated uncertainty by combining aerial remote sensing data and convolutional neural networks ensemble

Mapping tree species diversity is essential for monitoring and managing forest ecosystems. Automating ecoforestry mapping using remote sensing images remains an important challenge due to the tremendous variability in forest covers and the conditions under which images used for classification are acquired. Deep learning algorithms have been increasingly used over the last few years to analyse remote sensing data for mapping tree species. However, most of these studies focus only on a small number of species or a limited area and avoid providing a spatially explicit representation of the uncertainty related to the predictions, rendering them unsuitable for operational use.In this study, we used an ensemble of convolutional neural networks to map forest species and land cover types across a 10,000 km2 area in Quebec, Canada, spanning mixed and boreal forests. We built a georeferenced label database to train and test nine models, which resulted from the combination of three training datasets and three-commonly used convolutional neural network architectures (VGG16, ResNet50v2, Densenet121). These models were trained on multiband aerial photographs and a canopy height model derived from airborne lidar point clouds and used to map the diversity and distribution of tree species and land cover types. The level of agreement among models was used to generate uncertainty maps. The performance of the super-ensemble using 1311 independent forest inventory plots and assessed the extent to which uncertainty maps could serve as a spatially explicit indicator of model performance.The super-ensemble achieved 90% global accuracy. Our results indicated that the performance of the super-ensemble surpassed that of all individual architectures while also showing a positive effect of the canopy height model on performance. The comparison of the super-ensemble map with the proportion of basal area measured in forest inventory plots confirmed the reliability of the model over the study area. Our results also indicated that mapping the inter-model agreement provides a reliable spatially explicit estimate of model performance. The robustness and reliability of the proposed approach support its use in an operational context while also providing a conceptual framework to evaluate the reliability of uncertainty maps.

Analysis of convolutional neural networks for fronto-temporal dementia biomarker discovery.

Frontotemporal lobe dementia (FTD) results from the degeneration of the frontal and temporal lobes. It can manifest in several different ways, leading to the definition of variants characterised by their distinctive symptomatologies. As these variants are detected based on their symptoms, it can be unclear if they represent different types of FTD or different symptomatological axes. The goal of this paper is to investigate this question with a constrained cohort of FTD patients in order to see if the heterogeneity within this cohort can be inferred from medical images rather than symptom severity measurements. An ensemble of convolutional neural networks (CNNs) is used to classify diffusion tensor images collected from two databases consisting of 72 patients with behavioural variant FTD and 120 healthy controls. FTD biomarkers were found using voxel-based analysis on the sensitivities of these CNNs. Sparse principal components analysis (sPCA) is then applied on the sensitivities arising from the patient cohort in order to identify the axes along which the patients express these biomarkers. Finally, this is correlated with their symptom severity measurements in order to interpret the clinical presentation of each axis. The CNNs result in sensitivities and specificities between 83 and 92%. As expected, our analysis determines that all the robust biomarkers arise from the frontal and temporal lobes. sPCA identified four axes in terms of biomarker expression which are correlated with symptom severity measurements. Our analysis confirms that behavioural variant FTD is not a singular type or spectrum of FTD, but rather that it has multiple symptomatological axes that relate to distinct regions of the frontal and temporal lobes. This analysis suggests that medical images can be used to understand the heterogeneity of FTD patients and the underlying anatomical changes that lead to their different clinical presentations.

Advancing Arabic Handwriting Recognition with Convolutional and Recurrent Neural Network Ensembles

Advancing Arabic Handwriting Recognition with Convolutional and Recurrent Neural Network Ensembles

DeepRA: A novel deep learning-read-across framework and its application in non-sugar sweeteners mutagenicity prediction

Non-sugar sweeteners (NSSs) or artificial sweeteners have long been used as food chemicals since World War II. NSSs, however, also raise a concern about their mutagenicity. Evaluating the mutagenic ability of NSSs is crucial for food safety; this step is needed for every new chemical registration in the food and pharmaceutical industries. A computational assessment provides less time, money, and involved animals than the in vivo experiments; thus, this study developed a novel computational method from an ensemble convolutional deep neural network and read-across algorithms, called DeepRA, to classify the mutagenicity of chemicals. The mutagenicity data were obtained from the curated Ames test data set. The DeepRA model was developed using both molecular descriptors and molecular fingerprints. The obtained DeepRA model provides accurate and reliable mutagenicity classification through an independent test set. This model was then used to examine the NSSs-related chemicals, enabling the evaluation of mutagenicity from the NSSs-like substances. Finally, this model was publicly available at https://github.com/taraponglab/deepra for further use in chemical regulation and risk assessment.

MEGANET: Improved framework with nature inspired approach for colorectal cancer polyp classification

BACKGROUND: Polyps are tumorous growths in the colon or rectal area which can turn into cancer at later stages, thus detection of polyps is very important for timely prevention of colorectal cancer. The aim of the study is to develop a framework to accurately detect polyp images in colonoscopy images. OBJECTIVE: Development of an intelligent framework for classification of colorectal cancer from colon and rectal images. The standard machine learning, convolutional neural networks and ensemble models with nature inspired approach were implemented for this study. Model optimization was performed by varying hyper parameters. The main objective was to find an optimal model with high accuracy, optimized weights and less parameters. METHODS: The deep learning Convolutional Neural Network (CNN) models such as VGG19, ResNet50, EfficientNet, Ensemble Model (EM), and Modified Ensemble CNN with Genetic Algorithm (MEGANET) were implemented for the classification of colon images. RESULTS: Ensemble model was also created with two best performing deep learning models to further achieve higher accuracy of 96%. The ensemble model outperformed the other models in terms of accuracy, precision, recall, and F1 score. But this model has more complexity. The MEGANET, nature inspired evolutionary ensemble CNN model was implemented with transfer learning and genetic algorithms for weights optimization and parameter reduction. It achieved accuracy of 95%, on training data. CONCLUSION: The MEGANET performed similar to EM with less number of parameters on training, validation and test dataset. In future different methods will be implemented to further reduce the parameters and attain reasonable accuracy using MEGANET.

Redefining brain tumor segmentation: a cutting-edge convolutional neural networks-transfer learning approach

Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to precisely delineate tumor boundaries from magnetic resonance imaging (MRI) scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The model is rigorously trained and evaluated, exhibiting remarkable performance metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical image analysis and enhance healthcare outcomes. This research paves the way for future exploration and optimization of advanced CNN models in medical imaging, emphasizing addressing false positives and resource efficiency.

Bio-inspired deep learning-personalized ensemble Alzheimer's diagnosis model for mental well-being

Most classification models for Alzheimer's Diagnosis (AD) do not have specific strategies for individual input samples, leading to the problem of easily overlooking personalized differences between samples. This research introduces a customized dynamically ensemble convolution neural network (PDECNN), which is able to build a specific integration strategy based on the distinctiveness of the sample. In this paper, we propose a personalized dynamic ensemble alzheimer's Diagnosis classification model. This model will dynamically modify the deteriorated brain areas of interest depending on various samples since it can adjust to variations in the degeneration of sample brain areas. In clinical problems, the PDECNN model has additional diagnostic importance since it can identify sample-specific degraded brain areas based on input samples. This model considers the variability of brain region degeneration levels between input samples, evaluates the degree of degeneration of specific brain regions using an attention mechanism, and selects and integrates brain region features based on the degree of degeneration. Furthermore, by redesigning the classification accuracy performance, we respectively improve it by 4 %, 11 %, and 8 %. Moreover, the degraded brain regions identified by the model show high consistency with the clinical manifestations of AD.

Classification of mango disease using ensemble convolutional neural network

Mango is a highly significant fruit crop that thrives in a variety of agro-ecologies around the world. Mangoes are rich in vitamins and minerals. However, its yield is currently severely constrained due to disease and pest infestations. Thus, in order to improve mango fruit quality and productivity, illnesses and insect pests must be detected early on. In this study, we conceived and constructed a mango leaf disease detection mechanism utilizing an ensemble convolutional neural network approach. Healthy and diseased mango leaf images were manually obtained from main producing locations in Amhara Region for Merawi fruit and vegetable research identification. To improve the datasets, several pre-processing procedures (such as image resizing, noise reduction, and image augmentation) were used. To improve classification performance and meet the study's purpose, various segmentation approaches such as k means and Mask R-CNN were applied. Furthermore, following pre-processing and segmentation, features of mango leaf images were retrieved using CNN to obtain important features. The classification model was then constructed using fully-connected layer classifiers on the retrieved features of mango leaf images. The ensemble proposed GoogLeNet and VGG16 based CNN model in the study encompasses various operations, including dataset collection, image preprocessing, noise removal, segmentation, data augmentation, feature extraction, and classification. Upon testing, the model demonstrated impressive performance with 99.87 % training classification accuracy, 99.72 % validation accuracy, and 99.21 % testing accuracy. This indicates the effectiveness of the ensemble approach in achieving high accuracy in image classification tasks.

A Multichannel CT and Radiomics-Guided CNN-ViT (RadCT-CNNViT) Ensemble Network for Diagnosis of Pulmonary Sarcoidosis.

Pulmonary sarcoidosis is a multisystem granulomatous interstitial lung disease (ILD) with a variable presentation and prognosis. The early accurate detection of pulmonary sarcoidosis may prevent progression to pulmonary fibrosis, a serious and potentially life-threatening form of the disease. However, the lack of a gold-standard diagnostic test and specific radiographic findings poses challenges in diagnosing pulmonary sarcoidosis. Chest computed tomography (CT) imaging is commonly used but requires expert, chest-trained radiologists to differentiate pulmonary sarcoidosis from lung malignancies, infections, and other ILDs. In this work, we develop a multichannel, CT and radiomics-guided ensemble network (RadCT-CNNViT) with visual explainability for pulmonary sarcoidosis vs. lung cancer (LCa) classification using chest CT images. We leverage CT and hand-crafted radiomics features as input channels, and a 3D convolutional neural network (CNN) and vision transformer (ViT) ensemble network for feature extraction and fusion before a classification head. The 3D CNN sub-network captures the localized spatial information of lesions, while the ViT sub-network captures long-range, global dependencies between features. Through multichannel input and feature fusion, our model achieves the highest performance with accuracy, sensitivity, specificity, precision, F1-score, and combined AUC of 0.93 ± 0.04, 0.94 ± 0.04, 0.93 ± 0.08, 0.95 ± 0.05, 0.94 ± 0.04, and 0.97, respectively, in a five-fold cross-validation study with pulmonary sarcoidosis (n = 126) and LCa (n = 93) cases. A detailed ablation study showing the impact of CNN + ViT compared to CNN or ViT alone, and CT + radiomics input, compared to CT or radiomics alone, is also presented in this work. Overall, the AI model developed in this work offers promising potential for triaging the pulmonary sarcoidosis patients for timely diagnosis and treatment from chest CT.

Graph neural networks for automatic extraction and labeling of the coronary artery tree in CT angiography.

Automatic comprehensive reporting of coronary artery disease (CAD) requires anatomical localization of the coronary artery pathologies. To address this, we propose a fully automatic method for extraction and anatomical labeling of the coronary artery tree using deep learning. We include coronary CT angiography (CCTA) scans of 104 patients from two hospitals. Reference annotations of coronary artery tree centerlines and labels of coronary artery segments were assigned to 10 segment classes following the American Heart Association guidelines. Our automatic method first extracts the coronary artery tree from CCTA, automatically placing a large number of seed points and simultaneous tracking of vessel-like structures from these points. Thereafter, the extracted tree is refined to retain coronary arteries only, which are subsequently labeled with a multi-resolution ensemble of graph convolutional neural networks that combine geometrical and image intensity information from adjacent segments. The method is evaluated on its ability to extract the coronary tree and to label its segments, by comparing the automatically derived and the reference labels. A separate assessment of tree extraction yielded an score of 0.85. Evaluation of our combined method leads to an average score of 0.74. The results demonstrate that our method enables fully automatic extraction and anatomical labeling of coronary artery trees from CCTA scans. Therefore, it has the potential to facilitate detailed automatic reporting of CAD.

Early Identification of Rotten Potatoes Using an Electronic Nose Based on Feature Discretization and Ensemble Convolutional Neural Network.

The early identification of rotten potatoes is one of the most important challenges in a storage facility because of the inconspicuous symptoms of rot, the high density of storage, and environmental factors (such as temperature, humidity, and ambient gases). An electronic nose system based on an ensemble convolutional neural network (ECNN, a powerful feature extraction method) was developed to detect potatoes with different degrees of rot. Three types of potatoes were detected: normal samples, slightly rotten samples, and totally rotten samples. A feature discretization method was proposed to optimize the impact of ambient gases on electronic nose signals by eliminating redundant information from the features. The ECNN based on original features presented good results for the prediction of rotten potatoes in both laboratory and storage environments, and the accuracy of the prediction results was 94.70% and 90.76%, respectively. Moreover, the application of the feature discretization method significantly improved the prediction results, and the accuracy of prediction results improved by 1.59% and 3.73%, respectively. Above all, the electronic nose system performed well in the identification of three types of potatoes by using the ECNN, and the proposed feature discretization method was helpful in reducing the interference of ambient gases.

Towards Safer Roads: A Deep Learning Approach to Driver Distraction Detection in Four-wheeler Cars

The fact that there are more than 50 million cars sold annually and more than 1.3 million fatal motor vehicle accidents each year indicates the urgent need for stronger road safety regulations. Driver behaviour needs to be addressed, especially in emerging nations like India, which is responsible for 11% of all road fatalities worldwide. Diversion has been identified as the leading cause of 78% of accidents involving drivers. Distractions can take many different forms, from using a phone to interacting with others, and they greatly hinder road safety. This work aims to address this important problem by creating a highly effective machine learning (ML) model that employs computer vision techniques to classify various driver distractions in real-time. Utilising cutting-edge models, such as an ensemble of CNNs and convolutional neural networks (CNNs) like ResNet50. Our goal is to effectively identify and categorise distractions through the use of deep learning and picture recognition, allowing for proactive intervention to avert mishaps. Beyond classification accuracy, this study evaluates the model’s overall speed and scalability, which are critical for deployment on edge devices. We assess the practical practicality and wide-spread adoption of our approach by analysing performance parameters like inference time and resource utilisation

Crack pattern–based machine learning prediction of residual drift capacity in damaged masonry walls

AbstractIn this paper, we present a method based on an ensemble of convolutional neural networks (CNNs) for the prediction of residual drift capacity in unreinforced damaged masonry walls using as only input the crack pattern. We use an accurate block‐based numerical model to generate mechanically consistent crack patterns induced by external actions (earthquake‐like loads and differential settlements). For a damaged masonry wall, we extract the crack width cumulative distribution, we derive a crack width exceedance curve (CWEC), and we evaluate the drift loss (DL) with respect to the undamaged wall. Numerous pairs of CWEC and DL are thus generated and used for training (and validating) an ensemble of CNNs generated via repeated ‐folding cross validation with shuffling. As a result, a method for damage prognosis (Level IV of SHM) is provided. Such method appears general, inexpensive, and able to adequately predict the DL using as only input the CWEC, providing real‐time support for decision making in damaged masonry structures.

A novel ensemble learning framework based on a genetic algorithm for the classification of pneumonia

Pneumonia is a disease that can be detected by the opacity changes in chest X-rays and can lead to fatal consequences. Medical image analysis has several challenges, such as limited labeled datasets, imbalanced class distribution, image noise, and overfitting, so individual Convolutional Neural Networks (CNNs) are insufficient to detect pneumonia accurately. Although ensemble CNN models have been used in previous studies, the literature lacks guidance on identifying the optimal CNN models and weight ratio to combine them. In this study, we propose a novel ensemble CNN framework to accurately detect pneumonia, with optimum weights set by a Genetic Algorithm (GA). Firstly, a noise outside the lung was removed, and the model performance was enhanced by performing lung segmentation on Chest X-ray. The performances of several CNN models were analyzed by hyperparameter optimization. The framework combines the three models that give the best accuracy and the two models that provide the lowest false-negative value with the ensemble method in the ratio of the most appropriate weights. The proposed framework provided the best performance on the public test dataset with an accuracy of 97.23% and an F1-score of 97.45% compared to state-of-the-art methods. The study's main contributions are determining suitable models and their optimal weights of the ensemble method based on the GA. The proposed framework enables a rapid and effective diagnostic process, less costly healthcare services, and more efficient use of resources. The demo-link is https://www.youtube.com/watch?v=KZ50K3HL70U.

CRIECNN: Ensemble convolutional neural network and advanced feature extraction methods for the precise forecasting of circRNA-RBP binding sites

Circular RNAs (circRNAs) have surfaced as important non-coding RNA molecules in biology. Understanding interactions between circRNAs and RNA-binding proteins (RBPs) is crucial in circRNA research. Existing prediction models suffer from limited availability and accuracy, necessitating advanced approaches. In this study, we propose CRIECNN (Circular RNA-RBP Interaction predictor using an Ensemble Convolutional Neural Network), a novel ensemble deep learning model that enhances circRNA-RBP binding site prediction accuracy. CRIECNN employs advanced feature extraction methods and evaluates four distinct sequence datasets and encoding techniques (BERT, Doc2Vec, KNF, EIIP). The model consists of an ensemble convolutional neural network, a BiLSTM, and a self-attention mechanism for feature refinement. Our results demonstrate that CRIECNN outperforms state-of-the-art methods in accuracy and performance, effectively predicting circRNA-RBP interactions from both full-length sequences and fragments. This novel strategy makes an enormous advancement in the prediction of circRNA-RBP interactions, improving our understanding of circRNAs and their regulatory roles.

Grading the severity of diabetic retinopathy using an ensemble of self-supervised pre-trained convolutional neural networks: ESSP-CNNs

Grading the severity of diabetic retinopathy using an ensemble of self-supervised pre-trained convolutional neural networks: ESSP-CNNs