Lateral Polydactyly of the Foot: Surgical Outcomes Based on a New Classification by Junko Otsuka, MD, Emiko Horii, MD, Shukuki Koh, MD, Hiroki Takeshige, MD

“…Revision procedures were conducted on 8 feet. Three patients with type A-P classification developed painful hammer toe deformities as a late sequela that necessitated extensor tenolysis and metatarsophalangeal joint contracture release during their school-age years. …”
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Hybrid transformer-CNN and LSTM model for lung disease segmentation and classification by Syed Mohammed Shafi, Sathiya Kumar Chinnappan

“…According to the World Health Organization (WHO) report, lung disorders are the third leading cause of mortality worldwide. Approximately three million individuals are affected with various types of lung disorders annually. …”
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Machine Learning-Based Smartphone Grip Posture Image Recognition and Classification by Dohoon Kwon, Xin Cui, Yejin Lee, Younggeun Choi, Aditya Subramani Murugan, Eunsik Kim, Heecheon You

“…The grip postures were categorized into seven types, and three models (MobileNetV2, Inception V3, and ResNet-50), along with an ensemble model, were used for classification. …”
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Orthostatic Hypotension: Definition, Pathophysiology, Classification, Prognostic Aspects, Diagnostics and Treatment by O. D. Ostroumova, M. S. Cherniaeva, M. M. Petrova, O. V. Golovina

“…The article presents an updated definition of OH, modern classification, pathophysiology, feature of the  course  of OH in the  elderly, recommendations for diagnosis and  treatment. …”
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A Combined CNN-LSTM Network for Ship Classification on SAR Images by Abdelmalek Toumi, Jean-Christophe Cexus, Ali Khenchaf, Mahdi Abid

“…Evaluations were performed on three datasets: FUSAR-Ship, OpenSARShip, and MSTAR. …”
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Plant leaf classification using the multiscale entropy of curvature and feature aggregation by Raphael G. Pinheiro, José G.F. Lopes, Marcelo M.S. Souza, Fátima N.S. Medeiros

“…This paper presents a methodology for classifying plant leaves on the basis of handcrafted features derived from the multiscale entropy of curvature and texture, as well as deep features obtained from convolutional neural networks (CNNs). We propose three object descriptors on the basis of the multiscale entropy of curvature. …”
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Significance of pyroptosis-related genes in the diagnosis and classification of diabetic kidney disease by Yixiong Huang, Xinke Yuan

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Comparative analysis of impact of classification algorithms on security and performance bug reports by Said Maryyam, Bin Faiz Rizwan, Aljaidi Mohammad, Alshammari Muteb

“…Comparative analysis reveals that two algorithms (SVM and LR) perform better in terms of precision (0.99) for performance bugs and three algorithms (SVM, ANN, and LR) perform better in terms of F1 score for security bugs as compared to other classification algorithms which are essentially due to the linear dataset and extensive number of features in the dataset.…”
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HyperSMamba: A Lightweight Mamba for Efficient Hyperspectral Image Classification by Mengyuan Sun, Liejun Wang, Shaochen Jiang, Shuli Cheng, Lihan Tang

“…Deep learning has recently achieved remarkable progress in hyperspectral image (HSI) classification. Among these advancements, the Transformer-based models have gained considerable attention due to their ability to establish long-range dependencies. …”
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Distonias: conceitos, classificação e fisiopatologia Dystonias: concepts, classification and pathophysiology by João Carlos Papaterra Limongi

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Statistical Classification and an Optimized Red-Sequence Technique for the Determination of Galaxy Clusters by Dagoberto R. Mares-Rincón, Josué J. Trejo-Alonso, José A. Guerrero-Díaz-de-León, Jorge E. Macías-Díaz

“…The proposed algorithm employs Gaussian mixture models to analyze the distribution of three key variables: <i>r</i> magnitude, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>g</mi><mrow><mo>–</mo></mrow><mi>r</mi></mrow></semantics></math></inline-formula> color index, and redshift <i>z</i>. …”
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Adaptive neuro-fuzzy inference systems for improved mastitis classification and diagnosis by Javad Shirani Shamsabadi, Saeid Ansari Mahyari, Mostafa Ghaderi-Zefrehei

“…The aim of this study was to compare the performance of three adaptive neuro-fuzzy inference systems (ANFIS) classification methodologies in classifying mastitis in Holstein dairy cattle: gradient descent (GD)-based ANFIS (GD-ANIFIS), particle swarm optimization (PSO)-based ANFIS (PSO-ANFIS) and genetic algorithm (GA)-based ANFIS (GA-ANFIS). …”
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Uncertainty CNNs: A path to enhanced medical image classification performance by Vasileios E. Papageorgiou, Georgios Petmezas, Pantelis Dogoulis, Maxime Cordy, Nicos Maglaveras

“…The proposed DL model was evaluated using three datasets: (a) brain magnetic resonance imaging (MRI), (b) lung computed tomography (CT) scans, and (c) cardiac MRI. …”
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Noise-Robust Local Ternary Pattern Center for Noisy Texture Classification by Farhan A. Alenizi, Mokhtar Mohammadi, Mohammad Hossein Shakoor

“…The proposed method has three steps. First a criterion is proposed that estimates the quality of the texture. …”
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GlassBoost: A Lightweight and Explainable Classification Framework for Tabular Datasets by Ehsan Namjoo, Alison N. O’Connor, Jim Buckley, Conor Ryan

“…This paper introduces a novel XAI system designed for classification tasks on tabular data, which offers a balance between performance and interpretability. …”
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Artificial Intelligence Classification for Detecting and Grading Lumbar Intervertebral Disc Degeneration by Wongthawat Liawrungrueang, Watcharaporn Cholamjiak, Peem Sarasombath, Khanathip Jitpakdee, Vit Kotheeranurak

“…For Grade V, the prediction error was 2.3%, 2%, and 2.5% in the training, testing, and validation sets, respectively. …”
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Tomato classification with YOLOv8: Enhancing automated sorting and quality assessment by Viviana Moya, Michael Guerra, Karina Pazmiño, Faruk Abedrabbo, Fernando A. Chicaiza, David Pozo-Espín

“…The proposed system integrates computer vision and deep learning with a physical sorting mechanism to categorize tomatoes into three classes: green, red, and damaged, while also determining their size. …”
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Enhancing colorectal polyp classification using gaze-based attention networks by Zhenghao Guo, Yanyan Hu, Peixuan Ge, In Neng Chan, Tao Yan, Pak Kin Wong, Shaoyong Xu, Zheng Li, Shan Gao

“…Colorectal polyps are potential precursor lesions of colorectal cancer. Accurate classification of colorectal polyps during endoscopy is crucial for early diagnosis and effective treatment. …”
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Kernel to computation: identifying optimal feature set for red rice classification by Suma D, Narendra V G, Darshan Holla M, Shreyas, Raviraja Holla M

“…While existing research focuses extensively on white rice classification with readily available datasets, automated classification of red rice varieties remains largely unexplored with no publicly available datasets, creating a significant research gap in agricultural image processing applications. …”
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Crushed Stone Grain Shapes Classification Using Convolutional Neural Networks by Alexey N. Beskopylny, Evgenii M. Shcherban’, Sergey A. Stel’makh, Irina Razveeva, Alexander L. Mailyan, Diana Elshaeva, Andrei Chernil’nik, Nadezhda I. Nikora, Gleb Onore

“…Rapid determination of the crushed stone grain class will allow determining the content of lamellar and acicular grains, which in turn is a characteristic that affects the strength, adhesion, and filler placement. The classification algorithms were based on the ResNet50, MobileNetV3 Small, and DenseNet121 architectures. …”
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