Automatic Classification System Research Articles

Fake News Detection and Classification with Recurrent Neural Network Based Deep Learning Approaches

Events happening in the world are transmitted to the end user through the news channel. The information transmitted from the news is generally considered to be accurate. However, there may be errors or lies in the information that circulates on the news channels. At the same time, this news has an impact on serious environments, such as the economy. In social networks where data sharing is increasing, news data is piling up uncontrollably. In these data piles, there is real information as well as different information that is not real commercial, political, or sales-orientated. False information and data expand faster as a result of sharing false information by users. This news directly affects users, causing erroneous transactions, misinformation, or financial loss. For the stated reasons, automatic fake news classification systems are proposed in this article by combining natural language processing with Recurrent Neural Network (RNN) based deep learning methods. The proposed systems were tested on a dataset containing 23,481 fake news and 21,417 real news using general performance metrics. As a result of the test processes, the proposed BiLSTM method provided 99,72% accuracy, while the proposed GRU method accessed 97,50% accuracy.

Fake News Detection and Classification with Recurrent Neural Network Based Deep Learning Approaches

Events happening in the world are transmitted to the end user through the news channel. The information transmitted from the news is generally considered to be accurate. However, there may be errors or lies in the information that circulates on the news channels. At the same time, this news has an impact on serious environments, such as the economy. In social networks where data sharing is increasing, news data is piling up uncontrollably. In these data piles, there is real information as well as different information that is not real commercial, political, or sales-orientated. False information and data expand faster as a result of sharing false information by users. This news directly affects users, causing erroneous transactions, misinformation, or financial loss. For the stated reasons, automatic fake news classification systems are proposed in this article by combining natural language processing with Recurrent Neural Network (RNN) based deep learning methods. The proposed systems were tested on a dataset containing 23,481 fake news and 21,417 real news using general performance metrics. As a result of the test processes, the proposed BiLSTM method provided 99,72% accuracy, while the proposed GRU method accessed 97,50% accuracy.

Identifying losers: Automatic identification of growth-stunted salmon in aquaculture using computer vision

During the production of salmonids in aquaculture, it is common to observe growth-stunted individuals. The cause for the so-called “loser fish syndrome” is unclear, which needs further investigation. Here, we present and compare computer vision systems for the automatic detection and classification of loser fish in Atlantic salmon images taken in sea cages. We evaluated two end-to-end approaches (combined detection and classification) based on YoloV5 and YoloV7, and a two-stage approach based on transfer learning for detection and an ensemble of classifiers (e.g., linear perception, Adaline, C-support vector, K-nearest neighbours, and multi-layer perceptron) for classification. To our knowledge, the use of an ensemble of classifiers, considering consolidated classifiers proposed in the literature, has not been applied to this problem before. Classification entailed the assigning of every fish to a healthy and a loser class. The results of the automatic classification were compared to the reliability of human classification. The best-performing computer vision approach was based on YoloV7, which reached a precision score of 86.30%, a recall score of 71.75%, and an F1 score of 78.35%. YoloV5 presented a precision of 79.7%, while the two-stage approach reached a precision of 66.05%. Human classification had a substantial agreement strength (Fleiss’ Kappa score of 0.68), highlighting that evaluation by a human is subjective. Our proposed automatic detection and classification system will enable farmers and researchers to follow the abundance of losers throughout the production period. We provide our dataset of annotated salmon images for further research.

Pure tone audiogram classification using deep learning techniques.

Pure tone audiometry has played a critical role in audiology as the initial diagnostic tool, offering vital insights for subsequent analyses. This study aims to develop a robust deep learning framework capable of accurately classifying audiograms across various commonly encountered tasks. This single-centre retrospective study was conducted in accordance with the STROBE guidelines. A total of 12 518 audiograms were collected from 6259 patients aged between 4 and 96 years, who underwent pure tone audiometry testing between February 2018 and April 2022 at Tongji Hospital, Tongji Medical College, Wuhan, China. Three experienced audiologists independently annotated the audiograms, labelling the hearing loss in degrees, types and configurations of each audiogram. A deep learning framework was developed and utilised to classify audiograms across three tasks: determining the degrees of hearing loss, identifying the types of hearing loss, and categorising the configurations of audiograms. The classification performance was evaluated using four commonly used metrics: accuracy, precision, recall and F1-score. The deep learning method consistently outperformed alternative methods, including K-Nearest Neighbors, ExtraTrees, Random Forest, XGBoost, LightGBM, CatBoost and FastAI Net, across all three tasks. It achieved the highest accuracy rates, ranging from 96.75% to 99.85%. Precision values fell within the range of 88.93% to 98.41%, while recall values spanned from 89.25% to 98.38%. The F1-score also exhibited strong performance, ranging from 88.99% to 98.39%. This study demonstrated that a deep learning approach could accurately classify audiograms into their respective categories and could contribute to assisting doctors, particularly those lacking audiology expertise or experience, in better interpreting pure tone audiograms, enhancing diagnostic accuracy in primary care settings, and reducing the misdiagnosis rate of hearing conditions. In scenarios involving large-scale audiological data, the automated classification system could be used as a research tool to efficiently provide a comprehensive overview and statistical analysis. In the era of mobile audiometry, our deep learning framework can also help patients quickly and reliably understand their self-tested audiograms, potentially encouraging timely consultations with audiologists for further evaluation and intervention.

TransEBUS: The interpretation of endobronchial ultrasound image using hybrid transformer for differentiating malignant and benign mediastinal lesions

The purpose of this study is to establish a deep learning automatic assistance diagnosis system for benign and malignant classification of mediastinal lesions in endobronchial ultrasound (EBUS) images. EBUS images are in the form of video and contain multiple imaging modes. Different imaging modes and different frames can reflect the different characteristics of lesions. Compared with previous studies, the proposed model can efficiently extract and integrate the spatiotemporal relationships between different modes and does not require manual selection of representative frames. In recent years, Vision Transformer has received much attention in the field of computer vision. Combined with convolutional neural networks, hybrid transformers can also perform well on small datasets. This study designed a novel deep learning architecture based on hybrid transformer called TransEBUS. By adding learnable parameters in the temporal dimension, TransEBUS was able to extract spatiotemporal features from insufficient data. In addition, we designed a two-stream module to integrate information from three different imaging modes of EBUS. Furthermore, we applied contrastive learning when training TransEBUS, enabling it to learn discriminative representation of benign and malignant mediastinal lesions. The results show that TransEBUS achieved a diagnostic accuracy of 82% and an area under the curve of 0.8812 in the test dataset, outperforming other methods. It also shows that several models can improve performance by incorporating two-stream module. Our proposed system has shown its potential to help physicians distinguishing benign and malignant mediastinal lesions, thereby ensuring the accuracy of EBUS examination.

Classification of Image using Convolutional Neural Network (CNN)

Abstract: Computer vision is concerned with the automatic extraction, analysis, and understanding of useful information from a single image or a sequence of images. We have used Convolutional Neural Networks (CNN) in automatic image classification systems. In most cases, we utilize the features from the top layer of the CNN for classification; however, those features may not contain enough useful information to predict an image correctly. In some cases, features from the lower layer carry more discriminative power than those from the top. Therefore, applying features from a specific layer only to classification seems to be a process that does not utilize learned CNN’s potential discriminant power to its full extent. Because of this property we are in need of fusion of features from multiple layers. We want to create a model with multiple layers that will be able to recognize and classify the images. We want to complete our model by using the concepts of Convolutional Neural Network and CIFAR-10 dataset. Moreover, we will show how MatConvNet can be used to implement our model with CPU training as well as less training time. The objective of our work is to learn and practically apply the concepts of Convolutional Neural Network.

Spatial-attention ConvMixer architecture for classification and detection of gastrointestinal diseases using the Kvasir dataset

Gastrointestinal (GI) disorders, encompassing conditions like cancer and Crohn’s disease, pose a significant threat to public health. Endoscopic examinations have become crucial for diagnosing and treating these disorders efficiently. However, the subjective nature of manual evaluations by gastroenterologists can lead to potential errors in disease classification. In addition, the difficulty of diagnosing diseased tissues in GI and the high similarity between classes made the subject a difficult area. Automated classification systems that use artificial intelligence to solve these problems have gained traction. Automatic detection of diseases in medical images greatly benefits in the diagnosis of diseases and reduces the time of disease detection. In this study, we suggested a new architecture to enable research on computer-assisted diagnosis and automated disease detection in GI diseases. This architecture, called Spatial-Attention ConvMixer (SAC), further developed the patch extraction technique used as the basis of the ConvMixer architecture with a spatial attention mechanism (SAM). The SAM enables the network to concentrate selectively on the most informative areas, assigning importance to each spatial location within the feature maps. We employ the Kvasir dataset to assess the accuracy of classifying GI illnesses using the SAC architecture. We compare our architecture’s results with Vanilla ViT, Swin Transformer, ConvMixer, MLPMixer, ResNet50, and SqueezeNet models. Our SAC method gets 93.37% accuracy, while the other architectures get respectively 79.52%, 74.52%, 92.48%, 63.04%, 87.44%, and 85.59%. The proposed spatial attention block improves the accuracy of the ConvMixer architecture on the Kvasir, outperforming the state-of-the-art methods with an accuracy rate of 93.37%.

Design of an Automatic Classification System for Educational Reform Documents Based on Naive Bayes Algorithm

With the continuous deepening of educational reform, a large number of educational policies, programs, and research reports have emerged, bringing a heavy burden of information processing and management to educators. Traditional manual classification and archiving methods are inefficient and susceptible to subjective factors. Therefore, an automated method is needed to quickly and accurately classify and archive documents into their respective categories. Based on this, this paper proposes a design of an automatic document classification system for educational reform based on the Naive Bayes algorithm to address the challenges of document management in the education field. Firstly, the relevant literature and document data in the field of educational reform are collected and organized to establish an annotated dataset for model detection. Secondly, the raw data are preprocessed by cleaning and transforming the original text data to make them more suitable for input into machine learning algorithms. Thirdly, various algorithms are trained and selected to determine the best algorithm for classifying educational reform documents. Finally, based on the determined algorithm, a corresponding classification software is designed to automatically classify and archive educational reform documents for analysis. Through experimental evaluation and result analysis, this research demonstrates the effectiveness and accuracy of the education reform document automatic classification system based on the Naive Bayes algorithm. This method can efficiently classify a large number of documents into their respective categories quickly and accurately, thereby improving the efficiency of educators and their information management capabilities. In the future, further exploration of feature extraction methods and machine learning algorithms can be conducted to optimize the classification performance and apply this method to practical management and decision-making in the education field.

An automatic bioinformatic system for Identification and classification of Hymenoptera (The Apidae family)

In this paper we illustrate our bioinformatics system destined for an automatic insect classification in different taxonomic categories corresponding to different levels of precision according to their morphological characteristics such as: size; color; antennae; wings; mouthpart; etc. Our main goal is to automate the research; classification and to create an exhaustive database of insects in order to create a common directory of insects to save significant research time and facilitate classification. Our system is considered as a bioinformatics system, it uses artificial intelligence techniques to create an inference engine able to automatically recognize and classify a new insect. It also uses database creation techniques to create the knowledge and fact bases necessary for the proper functioning of the inference engine. Due to its interactive graphic interface, and by guiding its user, step by step, the system starts with the introduction of the morphological characteristics of the insect and the result is to automatically classify the insect in the database in the appropriate species. Our classification is focused on Hymenoptera precisely on the Apidae family, we have given a detailed classification of the Apidae family with its three subfamilies: Apinae, Xylocopinae and Nomadini. Our system is implemented completely in JAVA programming language using phpMyadmin as a database management system.

A system for automatic classification of endodontic treatment quality in CBCT.

An evaluation of the effectiveness of a new computational system proposed for automatic classification, developed based on a Siamese network combined with Convolutional Neural Networks (CNNs), is presented. It aims to identify endodontic technical errors using Cone Beam Computed Tomography (CBCT). The study also aims to compare the performance of the automatic classification system with that of dentists. One thousand endodontically treated maxillary molars sagittal and coronal reconstructions were evaluated for the quality of the endodontic treatment and the presence of periapical hypodensities by three board-certified dentists and by an oral and maxillofacial radiologist. The proposed classification system was based on a Siamese network combined with EfficientNet B1 or EfficientNet B7 networks. Accuracy, sensivity, precision, specificity, and F1-score values were calculated for automated artificial systems and dentists. Chi-square tests were performed. The performances were obtained for EfficienteNet B1, EfficientNet B7 and dentists. Regarding accuracy, sensivity and specificity, the best results were obtained with EfficientNet B1. Concerning precision and F1-score, the best results were obtained with EfficientNet B7. The presence of periapical hypodensity lesions was associated with endodontic technical errors. In contrast, the absence of endodontic technical errors was associated with the absence of hypodensity. Quality evaluation of the endodontic treatment performed by dentists and by Siamese Network combined with EfficientNet B7 or EfficientNet B1 networks was comparable with a slight superiority for the Siamese Network. CNNs have the potential to be used as a support and standardization tool in assessing endodontic treatment quality in clinical practice.

Electrocardiogram Heartbeat Classification using Convolutional Neural Network-k Nearest Neighbor

Electrocardiogram (ECG) analysis is widely used by cardiologists and medical practitioners for monitoring cardiac health. A high-performance automatic ECG classification system is challenging because there is difficulty in detecting and categorizing different waveforms in the signal, especially in manual analysis of ECG signals, which means, a better classification system is needed in terms of performance and accuracy. Hence, in this paper, the authors propose an accurate ECG classification and monitoring system called convolutional neural network-k nearest neighbor (CNN-kNN). The proposed method utilizes 1D-CNN and kNN. Unlike the existing techniques, the examined technique does not need training during classifying the ECG signals. The CNN-kNN is evaluated against the PhysioNet’s MIT-BIH and PTB diagnostics datasets. The CNN is fed using the ECG beat raw signal directly. In addition, the learned features are extracted from the 1D-CNN model and its dimensions are reduced using two fully connected layers and then fed to the k-NN classifier. The CNN-kNN model achieved average accuracies of 98% and 97.4% on arrhythmia and myocardial infarction classifications, respectively. These results are evidence of the great ability of the proposed model compared to the mentioned models in this article.

An artificial intelligence platform for automated PFAS subgroup classification: A discovery tool for PFAS screening

Since structural analyses and toxicity assessments have not been able to keep up with the discovery of unknown per- and polyfluoroalkyl substances (PFAS), there is an urgent need for effective categorization and grouping of PFAS. In this study, we presented PFAS-Atlas, an artificial intelligence-based platform containing a rule-based automatic classification system and a machine learning-based grouping model. Compared with previously developed classification software, the platform's classification system follows the latest Organization for Economic Co-operation and Development (OECD) definition of PFAS and reduces the number of uncategorized PFAS. In addition, the platform incorporates deep unsupervised learning models to visualize the chemical space of PFAS by clustering similar structures and linking related classes. Through real-world use cases, we demonstrate that PFAS-Atlas can rapidly screen for relationships between chemical structure and persistence, bioaccumulation, or toxicity data for PFAS. The platform can also guide the planning of the PFAS testing strategy by showing which PFAS classes urgently require further attention. Ultimately, the release of PFAS-Atlas will benefit both the PFAS research and regulation communities.

A Novel Approach to the Technique of Lung Region Segmentation Based on a Deep Learning Model to Diagnose COVID-19 X-ray Images.

The novel coronavirus pandemic has caused a global health crisis, placing immense strain on healthcare systems worldwide. Chest X-ray technology has emerged as a critical tool for the diagnosis and treatment of COVID-19. However, the manual interpretation of chest X-ray films has proven to be inefficient and time-consuming, necessitating the development of an automated classification system. In response to the challenges posed by the COVID-19 pandemic, we aimed to develop a deep learning model that accurately classifies chest X-ray images, specifically focusing on lung regions, to enhance the efficiency and accuracy of COVID-19 and pneumonia diagnosis. We have proposed a novel deep network called "FocusNet" for precise segmentation of lung regions in chest radiographs. This segmentation allows for the accurate extraction of lung contours from chest X-ray images, which are then input into the classification network, ResNet18. By training the model on these segmented lung datasets, we sought to improve the accuracy of classification. The performance of our proposed system was evaluated on three types of lung regions in normal individuals, COVID-19 patients, and those with pneumonia. The average accuracy of the segmentation model (FocusNet) in segmenting lung regions was found to be above 90%. After reclassification of the segmented lung images, the specificities and sensitivities for normal, COVID-19, and pneumonia were excellent, with values of 98.00%, 99.00%, 99.50%, and 98.50%, 100.00%, and 99.00%, respectively. ResNet18 achieved impressive area under the curve (AUC) values of 0.99, 1.00, and 0.99 for classifying normal, COVID-19, and pneumonia, respectively, on the segmented lung datasets. Moreover, the AUC values of the three groups increased by 0.02, 0.02, and 0.06, respectively, when compared to the direct classification of unsegmented original images. Overall, the accuracy of lung region classification after processing the datasets was 99.3%. Our deep learning-based automated chest X-ray classification system, incorporating lung region segmentation using FocusNet and subsequent classification with ResNet18, has significantly improved the accuracy of diagnosing respiratory lung diseases, including COVID-19. The proposed approach has great potential to revolutionize the diagnosis of COVID-19 and other respiratory lung diseases, offering a valuable tool to support healthcare professionals during health crises.

UC-stack: a deep learning computer automatic detection system for diabetic retinopathy classification.

Object. The existing diagnostic paradigm for diabetic retinopathy (DR) greatly relies on subjective assessments by medical practitioners utilizing optical imaging, introducing susceptibility to individual interpretation. This work presents a novel system for the early detection and grading of DR, providing an automated alternative to the manual examination.Approach. First, we use advanced image preprocessing techniques, specifically contrast-limited adaptive histogram equalization and Gaussian filtering, with the goal of enhancing image quality and module learning capabilities. Second, a deep learning-based automatic detection system is developed. The system consists of a feature segmentation module, a deep learning feature extraction module, and an ensemble classification module. The feature segmentation module accomplishes vascular segmentation, the deep learning feature extraction module realizes the global feature and local feature extraction of retinopathy images, and the ensemble module performs the diagnosis and classification of DR for the extracted features. Lastly, nine performance evaluation metrics are applied to assess the quality of the model's performance.Main results. Extensive experiments are conducted on four retinal image databases (APTOS 2019, Messidor, DDR, and EyePACS). The proposed method demonstrates promising performance in the binary and multi-classification tasks for DR, evaluated through nine indicators, including AUC and quadratic weighted Kappa score. The system shows the best performance in the comparison of three segmentation methods, two convolutional neural network architecture models, four Swin Transformer structures, and the latest literature methods.Significance. In contrast to existing methods, our system demonstrates superior performance across multiple indicators, enabling accurate screening of DR and providing valuable support to clinicians in the diagnostic process. Our automated approach minimizes the reliance on subjective assessments, contributing to more consistent and reliable DR evaluations.

Predictive Analysis in Nephrology: Evaluating Machine Learning Models for Chronic Kidney Disease Prediction

Abstract: In the realm of healthcare, early detection and accurate diagnosis are critical for effective treatment, especially for diseases that are often undiagnosed until later stages. This research presents an Automated Classification System focused on Chronic Kidney Disease (CKD), a prevalent condition frequently undetected in its early stages. Leveraging advanced machine learning techniques, the study aims to overcome the traditional reliance on doctors' intuition and experience by providing a data-driven approach for CKD diagnosis. Utilizing a comprehensive dataset, the research explores various data pre-processing methods, including innovative approaches for handling missing values and data integrity, which surpass the conventional mean and mode-based imputation methods prevalent in existing literature. Through exploratory data analysis and the application of diverse machine learning algorithms, the study seeks to identify the most effective model for predicting CKD risk. The findings of this research have significant implications for enhancing clinical decisions, offering a path towards more accessible, efficient, and accurate diagnosis of Chronic Kidney Disease, ultimately contributing to improved patient outcomes and healthcare practices

Deep learning application to automated classification of recommendations made by hospital pharmacists during medication prescription review.

Recommendations to improve therapeutics are proposals made by pharmacists during the prescription review process to address suboptimal use of medicines. Recommendations are generated daily as text documents but are rarely reused beyond their primary use to alert prescribers and caregivers. If recommendation data were easier to summarize, they could be used retrospectively to improve safeguards for better prescribing. The objective of this work was to train a deep learning algorithm for automated recommendation classification to valorize the large amount of recommendation data. The study was conducted in a French university hospital, at which recommendation data were collected throughout 2017. Data from the first 6 months of 2017 were labeled by 2 pharmacists who assigned recommendations to 1 of the 29 possible classes of the French Society of Clinical Pharmacy classification. A deep neural network classifier was trained to predict the class of recommendations. In total, 27,699 labeled recommendations from the first half of 2017 were used to train and evaluate a classifier. The prediction accuracy calculated on a validation dataset was 78.0%. We also predicted classes for unlabeled recommendations collected during the second half of 2017. Of the 4,460 predictions reviewed, 67 required correction. When these additional labeled data were concatenated with the original dataset and the neural network was retrained, accuracy reached 81.0%. To facilitate analysis of recommendations, we have implemented an automated classification system using deep learning that achieves respectable performance. This tool can help to retrospectively highlight the clinical significance of daily medication reviews performed by hospital clinical pharmacists.

Automated Classification of Agricultural Species through Parallel Artificial Multiple Intelligence System–Ensemble Deep Learning

The classification of certain agricultural species poses a formidable challenge due to their inherent resemblance and the absence of dependable visual discriminators. The accurate identification of these plants holds substantial importance in industries such as cosmetics, pharmaceuticals, and herbal medicine, where the optimization of essential compound yields and product quality is paramount. In response to this challenge, we have devised an automated classification system based on deep learning principles, designed to achieve precision and efficiency in species classification. Our approach leverages a diverse dataset encompassing various cultivars and employs the Parallel Artificial Multiple Intelligence System–Ensemble Deep Learning model (P-AMIS-E). This model integrates ensemble image segmentation techniques, including U-Net and Mask-R-CNN, alongside image augmentation and convolutional neural network (CNN) architectures such as SqueezeNet, ShuffleNetv2 1.0x, MobileNetV3, and InceptionV1. The culmination of these elements results in the P-AMIS-E model, enhanced by an Artificial Multiple Intelligence System (AMIS) for decision fusion, ultimately achieving an impressive accuracy rate of 98.41%. This accuracy notably surpasses the performance of existing methods, such as ResNet-101 and Xception, which attain 93.74% accuracy on the testing dataset. Moreover, when applied to an unseen dataset, the P-AMIS-E model demonstrates a substantial advantage, yielding accuracy rates ranging from 4.45% to 31.16% higher than those of the compared methods. It is worth highlighting that our heterogeneous ensemble approach consistently outperforms both single large models and homogeneous ensemble methods, achieving an average improvement of 13.45%. This paper provides a case study focused on the Centella Asiatica Urban (CAU) cultivar to exemplify the practical application of our approach. By integrating image segmentation, augmentation, and decision fusion, we have significantly enhanced accuracy and efficiency. This research holds theoretical implications for the advancement of deep learning techniques in image classification tasks while also offering practical benefits for industries reliant on precise species identification.

Development of Architectural Object Automatic Classification Technology for Point Cloud-Based Remodeling of Aging Buildings

In this study, we address the challenge of efficiently handling the maintenance and remodeling of buildings constructed post-1960s, lacking architectural drawings. The conventional approach involves manual measurements and data recording, followed by digital drawing creation. However, we leverage Fourth Industrial Revolution technologies to develop a deep learning-based automatic object classification system using point cloud data. We employ the FCAF3D network with multiscale cells, optimizing its configuration for classifying building components such as walls, floors, roofs, and other objects. While classifying walls, floors, and roofs using bounding boxes led to some boundary-related errors, the model performed well for objects with distinct shapes. Our approach emphasizes efficiency in the remodeling process rather than precise numerical calculations, reducing labor and improving architectural planning quality. While our dataset labeling strategy involved bounding boxes with limitations in numerical precision, future research could explore polygon-based labeling, minimizing loss of space and potentially yielding more meaningful results in classification. In summary, our technology aligns with the initial research objectives, and further investigations could enhance the methodology for even more accurate building object classification.

Brain Tumor Detection with Deep Learning Methods’ Classifier Optimization Using Medical Images

It is known that, with the development of artificial intelligence science in recent years, it has started to be used in all areas of life. Due to the increase in diseases that threaten human life, such as epidemics and cancer, more attention has been paid to research in this field. Especially in the field of biomedical image processing, very successful results have been obtained in recent years with the use of deep learning methods. For this study, MR images are utilized to diagnose brain tumors. To assist doctors and radiologists in automatic brain tumor diagnosis and to overcome the need for manual diagnosis, a brain MR image automated classification system is being developed. The data used in the study are open access data obtained from the Kaggle library. This paper presents a novel approach for classifying brain MR images utilizing a dataset of 7022 MR images. To give an unbiased evaluation of the dataset, it is divided into a 40% test and 60% training set. Respectively, VGG, ResNet, DenseNet and SqueezeNet architectures are trained and used for feature extraction from brain MRI images. In order to classify the extracted features, machine learning methods (Support Vector Machines, K-Nearest Neighbors, Naive Bayes, Decision Tree, Linear Regression Analysis) are applied first, then an ensemble learning method is applied and the best validation method is selected. In addition, parameter optimization is applied to the trained CNN algorithms. In order to develop the proposed methods, the Python software program was used in the training and testing phases of the models, and the classification success rates were mutually evaluated. Among the results found, it can see that the ResNet architecture reached 100% accuracy. The data obtained as a result of the study were compared with the results of similar studies. In conclusion, the techniques and methods applied highlight their effectiveness in accurately classifying brain MRI images and their potential to improve diagnostic capabilities.

Advancing automatic plant classification system in Saudi Arabia: introducing a novel dataset and ensemble deep learning approach

Advancing automatic plant classification system in Saudi Arabia: introducing a novel dataset and ensemble deep learning approach