Results 81 to 90 of about 17,818 (183)

CT‐Based Radiomic Features Predict Cervical Lymph Node Metastasis in Dogs With Oral Malignancy: A Machine Learning Study Using Leave‐One‐Patient‐Out Cross‐Validation

Veterinary and Comparative Oncology, EarlyView.
ABSTRACT Accurate preoperative identification of cervical lymph node (LN) metastasis is essential for staging and treatment planning in dogs with oral malignancy, yet conventional imaging offers limited diagnostic sensitivity. This retrospective study evaluated whether CT‐derived radiomic features, combined with machine learning classifiers, could ...
Christopher J. Pinard, Andrew Lagree, Ryan Appleby, Michelle Oblak, Kuan‐Chuen Wu, William T. Tran   +5 more
wiley   +1 more source

The Evolution of Breast Cancer Detection: A Review of Imaging, Machine Learning, and Multimodal Strategies

Computational and Systems Oncology, Volume 6, Issue 1, December 2026.
ABSTRACT Breast cancer is still a serious problem in the world arena, where its early and prompt detection is the most important factor in improving patient prognosis and survival. The use of traditional diagnostic techniques, such as imaging (e.g., mammography and ultrasound) and subsequent histopathological examination, is the mainstay, which ...
Likhon Chandra Sarkar, Farida Siddiqi Prity, Farhad Hussain, Anup Talukder, Mirza Raquib, Sharmina Rahman, MD Jiabul Hoque   +6 more
wiley   +1 more source

GLCMTextures: GLCM Textures of Raster Layers

Calculates grey level co-occurrence matrix (GLCM) based texture measures (Hall-Beyer (2017) ; Haralick et al. (1973) ) of raster layers using a sliding rectangular window.
Ilich, Alexander
core   +1 more source

IDENTIFICATION OF POTATO LEAF DISEASES USING ARTIFICIAL NEURAL NETWORKS WITH EXTREME LEARNING MACHINE ALGORITHM

Pilar Nusa Mandiri
Potato plants have an important role in providing a source of carbohydrates for society. However, potato production is often threatened by various plant diseases, such as leaf disease, which can cause a decrease in yields.
Moh. Erkamim, Ri Sabti Septarini, Mursalim Tonggiroh, Siti Nurhayati   +3 more
doaj   +1 more source

UAV‐based deep transfer learning to improve grain yield prediction in winter wheat across temporal and spatial variability

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Accurate prediction of grain yield (GY) remains a major challenge in plant breeding due to complex interactions between genotype, environment, and management (G × E × M) factors. Remote sensing data from unmanned aerial vehicles (UAVs) equipped with multispectral sensors have emerged as a pivotal resource for high‐throughput phenotyping.
Swas Kaushal, Mohammad Maruf Billah, Mandeep Singh, Dante Koupal, Pradeep Raj Sitaula, Jyotirmoy Halder, Jagdeep Sidhu, Maitiniyazi Maimaitijiang, Sunish K. Sehgal   +8 more
wiley   +1 more source

Dual‐Branch Network With Enhanced Feature Fusion for Improved Rice Classification Accuracy

Journal of Sustainable Agriculture and Environment, Volume 5, Issue 4, December 2026.
ABSTRACT Accurate mapping of rice, one of the world's most important food crops, is critical for monitoring its growth, estimating yields, and supporting global food security. Most deep learning methods for crop classification rely on multi‐temporal optical remote sensing data. However, it is difficult to obtain sufficient ground‐truth samples for time‐
Rongfei Ma, Wenjiao Shi, Minglei Wang, Xiwei Chen, Chenchen Zhang, Baihong Pan, Xiangming Xiao   +6 more
wiley   +1 more source

Enhancing diagnostic precision for BI‐RADS 4a breast nodules: A multimodal AI model integrating ultrasound radiomics, hemodynamic signatures, and clinical profiles

Journal of Applied Clinical Medical Physics, Volume 27, Issue 10, October 2026.
Abstract Background Breast Imaging Reporting and Data System (BI‐RADS) 4a nodules represent a diagnostic dilemma, with a malignancy rate ranging from 2% to 10%. The majority of these nodules prove benign after biopsy, leading to unnecessary invasive procedures, patient anxiety, and healthcare costs.
Jun Yang, Longman Long, Guang Li, Jian Zeng, Caixiao Peng, Jingting Yan   +5 more
wiley   +1 more source

GLCM texture analysis on different color space for pterygium grading [PDF]

, 2015
GLCM texture features have been widely used to characterize biomedical images. Most of the previous studies using GLCM features to characterize biomedical images only consider single or limited color space due to the use of only one color model. To mimic
Che Azemin, Mohd Zulfaezal, Hilmi, Mohd Radzi, Mohd. Kamal, Khairidzan, Mohd Tamrin, Mohd Izzuddin   +3 more
core  

Integrating Multimodal MRI Habitat and Transformer‐Based Pathomics to Predict High‐Risk Molecular Subtypes and Explore Biological Mechanisms in Adult Diffuse Gliomas

CNS Neuroscience &Therapeutics, Volume 32, Issue 10, October 2026.
Based on the 2021 WHO CNS5 guidelines, this study introduces an integrated high‐risk molecular classification for gliomas and constructs a predictive model by combining multimodal MRI habitat imaging, Transformer‐based WSI pathomics, and clinical information.
Wenju Niu, Xin Duan, Xuan Li, Zehui Li, Qian Liang, Xiangli Yang, Yan Tan, Xiaochun Wang, Guoqiang Yang, Hui Zhang   +9 more
wiley   +1 more source

GLCM and PSNR Analysis of Woven Fabric Images Made from Natural Dyes Due to Sunlight Exposure

JOIV: International Journal on Informatics Visualization
Traditional woven fabrics generally use natural dyes that come from the local area. Natural dyes are often considered low quality if exposed to sunlight.
Patrisius Batarius, Albertus Joko Santoso, Alfry Aristo Jansen Sinlae   +2 more
doaj   +1 more source