Postoperative symptom changes following uterine artery embolization for uterine fibroid based on FIGO classification by Yoshimi Nozaki, Shiori Takeuchi, Masafumi Arai, Yoshiki Kuwatsuru, Hiroshi Toei, Shingo Okada, Hitomi Kato, Naoko Saito, Takamichi Nobushima, Keisuke Murakami, Mari Kitade, Ryohei Kuwatsuru

“…Abstract Background Classifying uterine fibroid using the International Federation of Gynecology and Obstetrics (FIGO) classification system assists treatment decision-making and planning. …”
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Deep learning-based classification of gallbladder lesions in patients with non-diagnostic (GB-RADS 0) ultrasound by Pankaj Gupta, Ruby Siddiqui, Thakur D. Yadav, Lileswar Kaman, Gaurav Prakash, Parikshaa Gupta, Uma N. Saikia, Usha Dutta

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A Computer-Directed Geographic Coastal Use Classification System for Ecologic Planning: The Case of the Florida Keys by Gustavo A. Antonini, Leonard Zobler, Robert Swett

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Improved Support Vector Machine and Image Processing Enabled Methodology for Detection and Classification of Grape Leaf Disease by Arshiya S. Ansari, Malik Jawarneh, Mahyudin Ritonga, Pragti Jamwal, Mohammad Sajid Mohammadi, Ravi Kishore Veluri, Virendra Kumar, Mohd Asif Shah

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Feature Extraction using Histogram of Oriented Gradients and Moments with Random Forest Classification for Batik Pattern Detection by wafiq azizah, soffiana agustin

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Low-Rank Adaptation of Pre-Trained Large Vision Models for Improved Lung Nodule Malignancy Classification by Benjamin P. Veasey, Amir A. Amini

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Artificial Intelligence-Based Classification of Chest X-Ray Images into COVID-19 and Other Infectious Diseases by Arun Sharma, Sheeba Rani, Dinesh Gupta

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Impact of FY-4A Satellite-Based Surface Solar Irradiance on the Classification of Meteorology for Ozone Pollution by Yang Cao, Xiaoli Zhao, Debin Su, Hong Ren, Xiang Cheng, Yuchun Li, Chenxi Wang

“…After including SSI, among the 21 cities in the study area, the number of cities with a classification accuracy exceeding 80% increased from 7 to 14, with 20 cities having a positive accuracy growth rate and 8 cities having an accuracy growth rate exceeding 4%. …”
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RETRACTED: Modern Subtype Classification and Outlier Detection Using the Attention Embedder to Transform Ovarian Cancer Diagnosis by S. M. Nuruzzaman Nobel, S M Masfequier Rahman Swapno, Md. Ashraful Hossain, Mejdl Safran, Sultan Alfarhood, Md. Mohsin Kabir, M. F. Mridha

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Evaluation of Machine Learning Models for Stress Symptom Classification of Cucumber Seedlings Grown in a Controlled Environment by Kyu-Ho Lee, Samsuzzaman, Md Nasim Reza, Sumaiya Islam, Shahriar Ahmed, Yeon Jin Cho, Dong Hee Noh, Sun-Ok Chung

“…Findings suggested that color and texture features were critical indicators of stress, and that the KNN model, with optimized hyperparameters, provided a reliable classification for stress symptom monitoring for seedlings under controlled environments. …”
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A study on the classification of coastal wetland vegetation based on the Suaeda salsa index and its phenological characteristics by Weicheng Huang, Xianyun Fei, Weiwei Yang, Zhen Wang, Yajun Gao, Hong Yan

“…In order to demonstrate the efficacy of the constructed vegetation indices, this study employed both the NDVI and the existing Suaeda salsa Vegetation Index (SSVI) to calculate the phenological metrics for classification purposes. The results showed that: (1) The classification results of the phenological metrics using any of the S. salsa indices were significantly better than those of the NDVI, with RSSI being the best, with the accuracy of S. salsa being improved by 10 % for the producers and 30 % for the users, and the overall accuracy being improved by 13 %, the Kappa coefficient increased by 0.19. (2) The results of RSSI(1) and SSVI were more consistent with each other, with the overall accuracy increased by 10 % and the Kappa coefficient increased by 0.14. (3) When the combination of phenological metrics obtained from four vegetation indices was used for classification, the results were slightly better than those of a single vegetation index, with the overall accuracy increasing by 2 % and the Kappa coefficient increasing by 0.03. …”
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YOLOX-SwinT algorithm improves the accuracy of AO/OTA classification of intertrochanteric fractures by orthopedic trauma surgeons by Xue-Si Liu, Rui Nie, Ao-Wen Duan, Li Yang, Xiang Li, Le-Tian Zhang, Guang-Kuo Guo, Qing-Shan Guo, Dong-Chu Zhao, Yang Li, He-Hua Zhang

“…Results: The mean average precision at the intersection over union (IoU) of 0.5 (mAP50) for subgroup detection reached 90.29%. The classification accuracy values of SOTS, JOTS, SOTS + AI, and JOTS + AI groups were 56.24% ± 4.02%, 35.29% ± 18.07%, 79.53% ± 7.14%, and 71.53% ± 5.22%, respectively. …”
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Agricultural and Related Pest Control Applicator License Classifications under the Florida Department of Agriculture and Consumer Services (FDACS) by Frederick M. Fishel

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Class Weighting Approach For Handling Imbalanced Data On Forest Fire Classification Using EfficientNet-B1 by Arvinanto Bahtiar, Muhammad Ihsan Prawira Hutomo, Agung Widiyanto, Siti Khomsah

“…While training duration of 14 minutes and 45 seconds, outperforming the data augmentation method in terms of time efficiency. …”
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The impact of integrated genomic analysis on molecular classifications and prognostic risk stratification in endometrial cancer: a Chinese experience by Qian Zheng, Qian Zheng, Qian Zheng, Di Shao, Jin Shu, Jin Shu, Jin Shu, Qin Zhang, Qin Zhang, Qin Zhang, Min Huang, Dong Wang, Dong Wang, Dong Wang, Dongling Zou, Dongling Zou, Dongling Zou

“…With the application of the new European Society for Medical Oncology (ESMO) 2022 classification, 27 patients (14.1%) were reclassified. …”
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Automatic Detection and Classification of Road, Car, and Pedestrian Using Binocular Cameras in Traffic Scenes with a Common Framework by Yongchao Song, Jieru Yao, Yongfeng Ju, Yahong Jiang, Kai Du

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Transcriptomic Profiling and Tumor Microenvironment Classification Reveal Unique and Dynamic Immune Biology in HIV-Associated Kaposi Sarcoma by Jihua Yang, Ayse Ece Cali Daylan, Aleksei Shevkoplias, Ekaterina Postovalova, Meng Wang, Andrey Tyshevich, Matthew Lee, Hiba Narvel, Ksenia Zornikova, Nara Shin, Nikita Kotlov, Luca Paoluzzi, Changcheng Zhu, Balazs Halmos, Xingxing Zang, Haiying Cheng

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Skin Cancer Segmentation and Classification Using Vision Transformer for Automatic Analysis in Dermatoscopy-Based Noninvasive Digital System by Galib Muhammad Shahriar Himel, Md. Masudul Islam, Kh. Abdullah Al-Aff, Shams Ibne Karim, Md. Kabir Uddin Sikder

“…Segment Anything Model (SAM) is employed to segment the cancerous areas from the images; achieving an IOU of 96.01% and Dice coefficient of 98.14% and then various pretrained models are used for classification using vision transformer architecture. …”
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Deep learning in gonarthrosis classification: a comparative study of model architectures and single vs. multi-model methods by Sahika Betul Yayli, Kutay Kılıç, Salih Beyaz

“…(3) What is the impact of CLAHE (Contrast Limited Adaptive Histogram Equalization) on classification performance?ApproachWe created a dataset of 14,607 annotated knee AP X-rays from three hospitals. …”
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Characterization and classification of the compositional quality of milk from river buffaloes (Bubalus bubalis) and cows (Bos spp.) in Colombia by W. A. Barragán-Hernández, L. Mahecha-Ledesma, J. Angulo-Arizala, M. Olivera-Angel

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