Use Of Convolutional Neural Networks Research Articles

Scalable CNN-based classification of selective sweeps using derived allele frequencies.

Selective sweeps can successfully be distinguished from neutral genetic data using summary statistics and likelihood-based methods that analyze single nucleotide polymorphisms (SNPs). However, these methods are sensitive to confounding factors, such as severe population bottlenecks and old migration. By virtue of machine learning, and specifically convolutional neural networks (CNNs), new accurate classification models that are robust to confounding factors have been recently proposed. However, such methods are more computationally expensive than summary-statistic-based ones, yielding them impractical for processing large-scale genomic data. Moreover, SNP data are frequently preprocessed to improve classification accuracy, further exacerbating the long analysis times. To this end, we propose a 1D CNN-based model, dubbed FAST-NN, that does not require any preprocessing while using only derived allele frequencies instead of summary statistics or raw SNP data, thereby yielding a sample-size-invariant, scalable solution. We evaluated several data fusion approaches to account for the variance of the density of genetic diversity across genomic regions (a selective sweep signature), and performed an extensive neural architecture search based on a state-of-the-art reference network architecture (SweepNet). The resulting model, FAST-NN, outperforms the reference architecture by up to 12% inference accuracy over all challenging evolutionary scenarios with confounding factors that were evaluated. Moreover, FAST-NN is between 30× and 259× faster on a single CPU core, and between 2.0× and 6.2× faster on a GPU, when processing sample sizes between 128 and 1000 samples. Our work paves the way for the practical use of CNNs in large-scale selective sweep detection. https://github.com/SjoerdvandenBelt/FAST-NN.

An integrated approach to Bayesian weight regulations and multitasking learning methods for generating emotion-based content in the metaverse

This paper introduces an integrated model designed to analyze topics and sentiments in textual data simultaneously, recognizing their interdependence. By tackling challenges such as data scarcity, missing information, and biased distributions, the proposed model effectively captures the dynamic interactions between topics and sentiments within complex datasets. The method combines the use of Convolutional Neural Networks (CNN) for detecting topic-related patterns with a revised Recurrent Neural Network (RNN) to trace the emotional flow within contexts, leveraging the strengths of sequential data processing. These components are integrated within a Bayesian probability framework, modeling the conditional probabilities of sentences expressing specific sentiments and documents being associated with particular topics. The combined feature and state vectors from the CNN and revised RNN within this Bayesian setup enable precise classification and prediction of topics and sentiments. Furthermore, this paper explores innovative research avenues, including sentiment analysis in 3D virtual reality environments and the development of new algorithms that reflect content creation techniques in the metaverse, offering dynamic system construction. This integrated approach not only enhances data-driven decision-making but also unlocks new possibilities for advanced multi-text analysis.

A secured deep learning based smart home automation system

With the expansion of modern technologies and the Internet of Things (IoT), the concept of smart homes has gained tremendous popularity with a view to making people’s lives easier by ensuring a secured environment. Several home automation systems have been developed to report suspicious activities by capturing the movements of residents. However, these systems are associated with challenges such as weak security, lack of interoperability and integration with IoT devices, timely reporting of suspicious movements, etc. Therefore, the given paper proposes a novel smart home automation framework for controlling home appliances by integrating with sensors, IoT devices, and microcontrollers, which would in turn monitor the movements and send notifications about suspicious movements on the resident’s smartphone. The proposed framework makes use of convolutional neural networks (CNNs) for motion detection and classification based on pre-processing of images. The images related to the movements of residents are captured by a spy camera installed in the system. It helps in identification of outsiders based on differentiation of motion patterns. The performance of the framework is compared with existing deep learning models used in recent studies based on evaluation metrics such as accuracy (%), precision (%), recall (%), and f-1 measure (%). The results show that the proposed framework attains the highest accuracy (98.67%), thereby surpassing the existing deep learning models used in smart home automation systems.

AI-enhanced X-ray diffraction analysis: towards real-time mineral phase identification and quantification.

The use of convolutional neural networks can revolutionize XRD analysis by significantly reducing processing times. Demonstration against synthetic and real mineral mixture data provide a first assessment of the accuracy of such methods.

An efficient brain tumor detection and classification using pre-trained convolutional neural network models

In cases of brain tumors, some brain cells experience abnormal and rapid growth, leading to the development of tumors. Brain tumors represent a significant source of illness affecting the brain. Magnetic Resonance Imaging (MRI) stands as a well-established and coherent diagnostic method for brain cancer detection. However, the resulting MRI scans produce a vast number of images, which require thorough examination by radiologists. Manual assessment of these images consumes considerable time and may result in inaccuracies in cancer detection. Recently, deep learning has emerged as a reliable tool for decision-making tasks across various domains, including finance, medicine, cybersecurity, agriculture, and forensics. In the context of brain cancer diagnosis, Deep Learning and Machine Learning algorithms applied to MRI data enable rapid prognosis. However, achieving higher accuracy is crucial for providing appropriate treatment to patients and facilitating prompt decision-making by radiologists. To address this, we propose the use of Convolutional Neural Networks (CNN) for brain tumor detection. Our approach utilizes a dataset consisting of two classes: three representing different tumor types and one representing non-tumor samples. We present a model that leverages pre-trained CNNs to categorize brain cancer cases. Additionally, data augmentation techniques are employed to augment the dataset size. The effectiveness of our proposed CNN model is evaluated through various metrics, including validation loss, confusion matrix, and overall loss. The proposed approach employing ResNet50 and EfficientNet demonstrated higher levels of accuracy, precision, and recall in detecting brain tumors.

Prediction of midpalatal suture maturation stage based on transfer learning and enhanced vision transformer

BackgroundMaxillary expansion is an important treatment method for maxillary transverse hypoplasia. Different methods of maxillary expansion should be carried out depending on the midpalatal suture maturation levels, and the diagnosis was validated by palatal plane cone beam computed tomography (CBCT) images by orthodontists, while such a method suffered from low efficiency and strong subjectivity. This study develops and evaluates an enhanced vision transformer (ViT) to automatically classify CBCT images of midpalatal sutures with different maturation stages.MethodsIn recent years, the use of convolutional neural network (CNN) to classify images of midpalatal suture with different maturation stages has brought positive significance to the decision of the clinical maxillary expansion method. However, CNN cannot adequately learn the long-distance dependencies between images and features, which are also required for global recognition of midpalatal suture CBCT images. The Self-Attention of ViT has the function of capturing the relationship between long-distance pixels of the image. However, it lacks the inductive bias of CNN and needs more data training. To solve this problem, a CNN-enhanced ViT model based on transfer learning is proposed to classify midpalatal suture CBCT images. In this study, 2518 CBCT images of the palate plane are collected, and the images are divided into 1259 images as the training set, 506 images as the verification set, and 753 images as the test set. After the training set image preprocessing, the CNN-enhanced ViT model is trained and adjusted, and the generalization ability of the model is tested on the test set.ResultsThe classification accuracy of our proposed ViT model is 95.75%, and its Macro-averaging Area under the receiver operating characteristic Curve (AUC) and Micro-averaging AUC are 97.89% and 98.36% respectively on our data test set. The classification accuracy of the best performing CNN model EfficientnetV2_S was 93.76% on our data test set. The classification accuracy of the clinician is 89.10% on our data test set.ConclusionsThe experimental results show that this method can effectively complete CBCT images classification of midpalatal suture maturation stages, and the performance is better than a clinician. Therefore, the model can provide a valuable reference for orthodontists and assist them in making correct a diagnosis.

An optimized model based on adaptive convolutional neural network and grey wolf algorithm for breast cancer diagnosis.

Medical image classification (IC) is a method for categorizing images according to the appropriate pathological stage. It is a crucial stage in computer-aided diagnosis (CAD) systems, which were created to help radiologists with reading and analyzing medical images as well as with the early detection of tumors and other disorders. The use of convolutional neural network (CNN) models in the medical industry has recently increased, and they achieve great results at IC, particularly in terms of high performance and robustness. The proposed method uses pre-trained models such as Dense Convolutional Network (DenseNet)-121 and Visual Geometry Group (VGG)-16 as feature extractor networks, bidirectional long short-term memory (BiLSTM) layers for temporal feature extraction, and the Support Vector Machine (SVM) and Random Forest (RF) algorithms to perform classification. For improved performance, the selected pre-trained CNN hyperparameters have been optimized using a modified grey wolf optimization method. The experimental analysis for the presented model on the Mammographic Image Analysis Society (MIAS) dataset shows that the VGG16 model is powerful for BC classification with overall accuracy, sensitivity, specificity, precision, and area under the ROC curve (AUC) of 99.86%, 99.9%, 99.7%, 97.1%, and 1.0, respectively, on the MIAS dataset and 99.4%, 99.03%, 99.2%, 97.4%, and 1.0, respectively, on the INbreast dataset.

Deteksi Kesegaran Telur Ayam pada Citra Cangkang Menggunakan Metode Convolutional Neural Network (CNN)

This research developed an egg freshness detection system using the Convolutional Neural Network (CNN) method to analyze egg shell images. A dataset consisting of 1,898 images of fresh and stale egg shells was used in this study. The pre-processing stage included resizing and labeling data, followed by training the CNN model using the SqueezeNet and AlexNet architectures. The results showed an accuracy of 90% in classifying egg freshness. The use of CNN proved to be more effective than conventional methods such as thresholding and K-Nearest Neighbor (KNN). This research makes a significant contribution to improving the efficiency and accuracy of egg sorting in Indonesia, reducing reliance on manual methods, and speeding up the sorting process.

Classification of Breast Cancer with Transfer Learning on Convolutional Neural Network Models

Breast cancer is a serious medical condition and a leading cause of death among women. Early and accurate diagnosis is crucial for improving patient outcomes. This study explores the use of Convolutional Neural Networks (CNNs) with Transfer Learning using DenseNet121 and ResNet50 models to enhance breast cancer classification via mammography. Transfer Learning enables CNN models to leverage knowledge learned from larger datasets such as ImageNet to improve performance on specific breast cancer datasets. The dataset comprised medical images with three breast variations: benign, malignant, and normal, totaling 531 data points. Data was split with a 70% training and 30% validation ratio. Two CNN models, AlexNet and ResNet50, were evaluated to compare their performance in classifying these breast cancer types. The experimental results show that AlexNet achieved a training accuracy of 98.01%, while ResNet50 achieved 64.07%. AlexNet demonstrated superior performance in identifying complex patterns in mammography images, resulting in more accurate classification of different breast cancer types. These findings highlight the potential of deep learning applications to support more precise and effective medical diagnostics for breast cancer. This research contributes significantly to the development of AI technologies in healthcare aimed at improving early detection of breast cancer. The implications of this study could expand our understanding of Transfer Learning applications in medical contexts, driving further advancements in this field to enhance patient care and prognosis

METHODS OF MATHEMATICAL ANALYSIS AND PROCESSING OF EEG SIGNALS: OVERVIEW OF THE CURRENT STATE AND LATEST TRENDS

Aim. With the help of retrospective analysis, establish the current level of achievements in the field of applying methods of mathematical analysis and processing of biomedical signals, in particular EEG, and characterize the main trends. Materials and methods. The research was conducted using theoretical methods, by studying and analyzing freely available literature, publications, materials of working groups of domestic and foreign scientists over the past few years. Results. The analysis of scientific works devoted to the development and development of EEG showed that over the past five years there has been a deepening of the tendency to separate the methods of improving the hardware and software parts of electroencephalographic complexes. The last of them, in turn, can be divided into several main directions. The first direction is the improvement of real-time EEG analysis methods, including automatic processing included in the software of EEG complexes. The second direction combines mathematical methods of signal analysis to detect existing pathologies and predict the onset of pathological conditions, such as statistical, spectral-correlation analysis, building mathematical models. The next direction brings together the application of mathematical methods for EEG signal processing and further use in deep learning, the use of convolutional neural networks to detect epileptic seizures, sleep disorders, differentiation of neuropsychiatric disorders, etc. The direction of using EEG in prosthetics deserves special attention. Methods of mathematical processing of the EEG signal are widely implemented in the development of bionic prostheses. The main achievements in these directions are considered in detail in the work. Conclusions. While the improvement of the material and technical part of the EEG is focused on the issues of improving the methods of recording biological signals, finding optimal methods of suppressing noise without losing the useful signal and does not currently have breakthrough results, the methods of analysis and processing of the already received signal have developed rapidly. The use of EEG to detect pathological conditions, the study of the neurophysiological basis of cognitive processes, etc., has always attracted the interest of scientists, but the use of neural networks for EEG processing and analysis and their implementation in robotics developments has given this direction a new impetus.

Machine Learning-Assisted Raman Spectroscopy and SERS for Bacterial Pathogen Detection: Clinical, Food Safety, and Environmental Applications

Detecting pathogenic bacteria and their phenotypes including microbial resistance is crucial for preventing infection, ensuring food safety, and promoting environmental protection. Raman spectroscopy offers rapid, seamless, and label-free identification, rendering it superior to gold-standard detection techniques such as culture-based assays and polymerase chain reactions. However, its practical adoption is hindered by issues related to weak signals, complex spectra, limited datasets, and a lack of adaptability for detection and characterization of bacterial pathogens. This review focuses on addressing these issues with recent Raman spectroscopy breakthroughs enabled by machine learning (ML), particularly deep learning methods. Given the regulatory requirements, consumer demand for safe food products, and growing awareness of risks with environmental pathogens, this study emphasizes addressing pathogen detection in clinical, food safety, and environmental settings. Here, we highlight the use of convolutional neural networks for analyzing complex clinical data and surface enhanced Raman spectroscopy for sensitizing early and rapid detection of pathogens and analyzing food safety and potential environmental risks. Deep learning methods can tackle issues with the lack of adequate Raman datasets and adaptability across diverse bacterial samples. We highlight pending issues and future research directions needed for accelerating real-world impacts of ML-enabled Raman diagnostics for rapid and accurate diagnosis and surveillance of pathogens across critical fields.

A comprehensive health assessment approach using ensemble deep learning model for remote patient monitoring with IoT

The goal of this research is to create an ensemble deep learning model for Internet of Things (IoT) applications that specifically target remote patient monitoring (RPM) by integrating long short-term memory (LSTM) networks and convolutional neural networks (CNN). The work tackles important RPM concerns such early health issue diagnosis and accurate real-time physiological data collection and analysis using wearable IoT devices. By assessing important health factors like heart rate, blood pressure, pulse, temperature, activity level, weight management, respiration rate, medication adherence, sleep patterns, and oxygen levels, the suggested Remote Patient Monitor Model (RPMM) attains a noteworthy accuracy of 97.23%. The model's capacity to identify spatial and temporal relationships in health data is improved by novel techniques such as the use of CNN for spatial analysis and feature extraction and LSTM for temporal sequence modeling. Early intervention is made easier by this synergistic approach, which enhances trend identification and anomaly detection in vital signs. A variety of datasets are used to validate the model's robustness, highlighting its efficacy in remote patient care. This study shows how using ensemble models' advantages might improve health monitoring's precision and promptness, which would eventually benefit patients and ease the burden on healthcare systems.

IoT traffic classification and anomaly detection method based on deep autoencoders

This study investigates anomaly detection of IoT device traffic using Convolutional Neural Networks (CNN) and Variational Autoencoders (VAE) to enhance the detection capability of security threats in IoT environments. A series of hardware configurations, software environments, and hyperparameters were utilized to optimize the training and testing processes of the models. The CNN model demonstrates robust classification performance, achieving an accuracy rate of 95.85% on the test dataset, effectively distinguishing between different types of IoT device traffic. Meanwhile, the VAE model exhibits proficient anomaly detection capabilities by effectively capturing abnormal patterns in the data using reconstruction loss and KL divergence. The combined use of CNN and VAE models offers a comprehensive solution to cybersecurity challenges in IoT environments. Future research directions include exploring diverse IoT traffic data, practical deployment for validation, and further optimization of model structures and parameters to improve performance and applicability.

Depth from Defocus Technique For High Number Density Particle Images

The Depth from Defocus (DFD) imaging technique is used to measure the size and number concentration of particles in dispersed two-phase flows, but until now it has primarily been applied to low concentration particle images. This study explores how the technique can be extended to handle overlapping images caused by neighboring particles, significantly broadening the application scope of the DFD technique and enabling measurements at higher particle number/volume concentrations. The processing algorithms are experimentally validated using a dedicated apparatus that can systematically vary particle size, shape, and degree of image overlap. Additionally, this study explores the use of Convolutional Neural Networks (CNN) for this task, comparing these results with those obtained using conventional analyses in terms of accuracy, tolerable concentration limits, and computational speed. This approach requires a large teaching dataset of images, which is only practical and feasible if the dataset can be synthetically generated. An image generation procedure for out-of-focus neighboring spherical particles, resulting in a known blurred image overlap, is therefore first developed. This procedure is validated using laboratory images with known particle size distribution, position, and image overlap before creating the teaching dataset. The trained processing scheme is then applied to both synthetic datasets and experimental data, allowing the evaluation of the technique’s limits in terms of image overlap and tolerable volume concentration, as a function of particle size distribution.

A Single Camera Calibration-Free Approach For Dispersion Size Measurement Using Depth From Defocus

The Depth from Defocus (DFD) imaging technique is used to measure the size and number concentration of particles in dispersed two-phase flows, but until now it has primarily been applied to low concentration particle images. This study explores how the technique can be extended to handle overlapping images caused by neighboring particles, significantly broadening the application scope of the DFD technique and enabling measurements at higher particle number/volume concentrations. The processing algorithms are experimentally validated using a dedicated apparatus that can systematically vary particle size, shape, and degree of image overlap. Additionally, this study explores the use of Convolutional Neural Networks (CNN) for this task, comparing these results with those obtained using conventional analyses in terms of accuracy, tolerable concentration limits, and computational speed. This approach requires a large teaching dataset of images, which is only practical and feasible if the dataset can be synthetically generated. An image generation procedure for out-of-focus neighboring spherical particles, resulting in a known blurred image overlap, is therefore first developed. This procedure is validated using laboratory images with known particle size distribution, position, and image overlap before creating the teaching dataset. The trained processing scheme is then applied to both synthetic datasets and experimental data, allowing the evaluation of the technique’s limits in terms of image overlap and tolerable volume concentration, as a function of particle size distribution.

Deep-learning map segmentation for protein X-ray crystallographic structure determination.

When solving a structure of a protein from single-wavelength anomalous diffraction X-ray data, the initial phases obtained by phasing from an anomalously scattering substructure usually need to be improved by an iterated electron-density modification. In this manuscript, the use of convolutional neural networks (CNNs) for segmentation of the initial experimental phasing electron-density maps is proposed. The results reported demonstrate that a CNN with U-net architecture, trained on several thousands of electron-density maps generated mainly using X-ray data from the Protein Data Bank in a supervised learning, can improve current density-modification methods.

Optimasi Convolutional Neural Networks untuk Deteksi Kanker Payudara menggunakan Arsitektur DenseNet

Breast cancer is a disease commonly suffered by women worldwide, ranking as the second-largest disease burden. In response to the urgent need for improved detection accuracy, Convolutional Neural Networks (CNNs) promise significant advancements. The objective of this research is to optimize the use of CNNs with the DenseNet architecture for breast cancer detection. The study employs quantitative methods, leveraging Deep Learning through CNNs. Mammography data is sourced from Kaggle, specifically the “Breast Histopathology Images” dataset. This dataset comprises 90,000 digital mammography images, which are preprocessed and divided proportionally for training, validation, and model testing. Research variables encompass CNN model parameters, training techniques, and the integration of imaging modalities to enhance breast cancer detection performance. The research focuses on processed mammography data, with accuracy and image quality as key evaluation metrics for breast cancer sample identification. Our findings demonstrate that the DenseNet architecture within CNNs achieves an impressive 92% accuracy in breast cancer detection. This remarkable performance signifies success in enhancing image quality and class prediction, aligning with the DenseNet architecture’s flow diagram. Ultimately, these results contribute significantly to effective breast cancer diagnosis by optimizing CNNs with the DenseNet architecture to improve image quality during breast cancer sampling.

Classification of white blood cells (leucocytes) from blood smear imagery using machine and deep learning models: A global scoping review.

Machine learning (ML) and deep learning (DL) models are being increasingly employed for medical imagery analyses, with both approaches used to enhance the accuracy of classification/prediction in the diagnoses of various cancers, tumors and bloodborne diseases. To date however, no review of these techniques and their application(s) within the domain of white blood cell (WBC) classification in blood smear images has been undertaken, representing a notable knowledge gap with respect to model selection and comparison. Accordingly, the current study sought to comprehensively identify, explore and contrast ML and DL methods for classifying WBCs. Following development and implementation of a formalized review protocol, a cohort of 136 primary studies published between January 2006 and May 2023 were identified from the global literature, with the most widely used techniques and best-performing WBC classification methods subsequently ascertained. Studies derived from 26 countries, with highest numbers from high-income countries including the United States (n = 32) and The Netherlands (n = 26). While WBC classification was originally rooted in conventional ML, there has been a notable shift toward the use of DL, and particularly convolutional neural networks (CNN), with 54.4% of identified studies (n = 74) including the use of CNNs, and particularly in concurrence with larger datasets and bespoke features e.g., parallel data pre-processing, feature selection, and extraction. While some conventional ML models achieved up to 99% accuracy, accuracy was shown to decrease in concurrence with decreasing dataset size. Deep learning models exhibited improved performance for more extensive datasets and exhibited higher levels of accuracy in concurrence with increasingly large datasets. Availability of appropriate datasets remains a primary challenge, potentially resolvable using data augmentation techniques. Moreover, medical training of computer science researchers is recommended to improve current understanding of leucocyte structure and subsequent selection of appropriate classification models. Likewise, it is critical that future health professionals be made aware of the power, efficacy, precision and applicability of computer science, soft computing and artificial intelligence contributions to medicine, and particularly in areas like medical imaging.

CNN-Based Segmentation and Detection of Brain Tumors MRI Images: A Review

In order to provide effective therapy and enhance patient outcomes, it is essential to diagnose brain tumors as early as possible and with high accuracy. Due to the fact that it provides very specific anatomical information, magnetic resonance imaging (MRI) is an extremely important tool for diagnosing brain tumors. The manual segmentation and identification of brain tumors from MRI images, on the other hand, would take a significant amount of time and are prone to human mistake. The use of convolutional neural networks, often known as CNNs, has become more popular as a resource for automating certain activities. The purpose of this review article is to investigate the current developments in CNN-based methods for the segmentation and identification of brain tumors in magnetic resonance imaging (MRI) images. We describe the most significant difficulties that are connected with the analysis of brain tumors using MRI, investigate the different CNN designs that are used for this purpose, and evaluate the performance metrics of these architectures. The purpose of this study is to offer a complete overview of the present state-of-the-art in CNN-based brain tumor analysis of MRI data, emphasizing both the promise and limits of this method.

Prostate Cancer: MRI Image Detection Based on Deep Learning: A Review

This comprehensive study delves into the transformative role of artificial intelligence (AI) and deep learning (DL) in the realm of prostate cancer care, an issue of paramount importance in men’s health worldwide. Prostate cancer, marked by the unchecked growth of cells in the prostate gland, poses risks of tumor formation and eventual metastasis. The crux of combating this disease lies in its early detection and precise diagnosis, for which traditional screening methodologies like Prostate-Specific Antigen (PSA) tests and multiparametric Magnetic Resonance Imaging (mp-MRI) are fundamental. The introduction of AI and DL into these diagnostic avenues has been nothing short of revolutionary, enhancing the precision of medical imaging and significantly reducing the rates of unnecessary biopsies. The advancements in DL, particularly through the use of convolutional neural networks (CNNs) and the application of multiparametric MRI, have been instrumental in improving the accuracy of diagnoses, foreseeing the progression of the disease, and tailoring individualized treatment regimens. This paper meticulously examines various DL models and their successful application in the detection, classification, and segmentation of prostate cancer, establishing their superiority over conventional diagnostic techniques. Despite the promising horizon these technologies offer, their implementation is not without challenges. The requisite for specialized expertise to handle these advanced tools and the ethical dilemmas they present, such as data privacy and potential biases, are significant hurdles. Nevertheless, the potential of AI and DL to inaugurate a new chapter in prostate cancer management is undeniable. The emphasis on interdisciplinary collaboration among scientists, clinicians, and technologists is crucial for pushing the boundaries of current research and clinical practice, ensuring the ethical deployment of AI and DL technologies. This collaborative effort is vital for realizing the full potential of these innovations in providing more accurate, efficient, and patient-centric care in the fight against prostate cancer, heralding a future where the burden of this disease is significantly mitigated.