Results 91 to 100 of about 22,865 (233)

Concrete Slump Classification Using GLCM Feature Extraction

Advanced Science, Engineering and Medicine, 2016
Digital image processing technologies have been widely applies in analyzing concrete structure because the accuracy and real time result. The aim of this study is to classify concrete slump by using image processing technique. For this purpose, concrete mix design of 30 MPa compression strength designed with slump of 0-10 mm, 10-30 mm, 30-60 mm, and 60-
Relly Andayani, Syarifudin Madenda
openaire   +1 more source

Radiomics Analysis for Predicting Medication‐Related Osteonecrosis of the Jaw Using Computed Tomography

Oral Diseases, EarlyView.
ABSTRACT Objectives To determine whether pretreatment radiomics can predict medication‐related osteonecrosis of the jaw (MRONJ). Methods Patients with mandibular MRONJ at Hiroshima University Hospital (2009–2024) who underwent MDCT before initiating bone resorption inhibitors were retrospectively identified. Records from 2009 to 2020 were also reviewed
Masaru Konishi
wiley   +1 more source

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

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

Hybrid methods for feature extraction for breast masses classification

Egyptian Informatics Journal, 2018
This paper is focusing on feature extraction methods for malignant masses in mammograms and its classification. It proposes seven texture features for GLCM method and to be applied on sub-images to enhance its performance.
Mohamed A. Berbar
doaj   +1 more source

Thangka Image Retrieval System Based on GLCM

DEStech Transactions on Engineering and Technology Research, 2018
Thangka Image Retrieval System is a challenging task in Thangka image classification. The Thangka images obtained from different ways are not all true Thangka images, which may lead to the misclassification of Thangka images. Therefore, a retrieval system is needed to distinguish true and false Thangka images.
SHOU-LIANG TANG, JIAN-BANG JIA, CHUAN-QIAN TANG, XIAO-JING LIU, ZHI-QIANG LIU   +4 more
openaire   +2 more sources

3D-GLCM CNN: A 3-dimensional gray-level co-occurrence matrix based CNN model for polyp classification via CT colonography

IEEE Transactions on Medical Imaging, 2019
Accurately classifying colorectal polyps, or differentiating malignant from benign ones, has a significant clinical impact on early detection and identifying optimal treatment of colorectal cancer.
Jiaxing Tan, Yongfeng Gao, Zhengrong Liang, W. Cao, M. Pomeroy, Yumei Huo, Lihong C. Li, M. Barish, A. Abbasi, P. Pickhardt   +9 more
semanticscholar   +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

An interpretable radiomics–deep learning nomogram from whole‐body bone scintigraphy for MDP‐avid bone metastasis prediction in NSCLC

Journal of Applied Clinical Medical Physics, Volume 27, Issue 8, August 2026.
Abstract Background Whole‐body bone scintigraphy (WBS) remains a widely used first‐line screening tool for bone metastasis, but differentiating MDP‐avid metastatic lesions from benign bone abnormalities in non‐small cell lung cancer (NSCLC) remains challenging. Purpose To develop and externally validate a radiomics and deep learning bone signature (RDB)
Weihao Zhai, Xiaolin Li, Qian Zhou, Taohu Zhou, Yi Wang, Xiuxiu Zhou, Xin'ang Jiang, Ziwei Zhang, Qianxi Jin, Shiyuan Liu, Li Fan   +10 more
wiley   +1 more source