Results 21 to 30 of about 14,783 (197)

An Improved VGG19 Transfer Learning Strip Steel Surface Defect Recognition Deep Neural Network Based on Few Samples and Imbalanced Datasets

Applied Sciences, 2021
The surface defects’ region of strip steel is small, and has various defect types and, complex gray structures. There tend to be a large number of false defects and edge light interference, which lead traditional machine vision algorithms to be unable to
Xiang Wan, Xiangyu Zhang, Lilan Liu
doaj   +1 more source

Do less and achieve more: Training CNNs for action recognition utilizing action images from the Web [PDF]

, 2017
Recently, attempts have been made to collect millions of videos to train Convolutional Neural Network (CNN) models for action recognition in videos. However, curating such large-scale video datasets requires immense human labor, and training CNNs on ...
Bargal, Sarah Adel, Ma, Shugao, Sclaroff, Stan, Sigal, Leonid, Zhang, Jianming   +4 more
core   +1 more source

Automated Brain Tumor Classification using Deep Convolutional and Transfer Learning Networks [PDF]

Advances in Engineering and Intelligence Systems
Brain cancers are some of the fastest-growing and most deadly types of neurological diseases in medicine. Early detection with accuracy is very important to improve the survival of patients. Manually reading MRI scan images is a slow process. It requires
Vinodkumar Patil, Manisha Patil, Pramod Deore, Vijay Patil, Pankaj Baviskar   +4 more
doaj   +1 more source

Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study [PDF]

, 2019
BACKGROUND: For virtually every patient with colorectal cancer (CRC), hematoxylin-eosin (HE)-stained tissue slides are available. These images contain quantitative information, which is not routinely used to objectively extract prognostic biomarkers.
Brenner, H., Chang-Claude, J., Charoentong, P., Ferber, D., Gaiser, T., Halama, N., Herpel, E., Hoffmeister, M., Jansen, L., Jäger, D., Kather, J. N., Krisam, J., Luedde, T., Marx, A., Reyes-Aldasoro, C. C., Valous, N. A., Weis, C. A., Zörnig, I.   +17 more
core   +2 more sources

Deep Neural Networks for Dental Implant System Classification

Biomolecules, 2020
In this study, we used panoramic X-ray images to classify and clarify the accuracy of different dental implant brands via deep convolutional neural networks (CNNs) with transfer-learning strategies.
Shintaro Sukegawa, Kazumasa Yoshii, Takeshi Hara, Katsusuke Yamashita, Keisuke Nakano, Norio Yamamoto, Hitoshi Nagatsuka, Yoshihiko Furuki   +7 more
doaj   +1 more source

A Decision Support System Based on Machine Learning for Land Investment. [PDF]

مجلة التربية والعلم, 2023
This research paper proposes a methodology for classifying aerial photographs and lands using deep learning with transfer learning. The study utilizes the Aerial Image Dataset (AID), which contains a diverse set of aerial images with 30 scene classes ...
Dhufr Hussein Alali, Timur Inan
doaj   +1 more source

Weed Identification in Soybean Seedling Stage Based on Optimized Faster R-CNN Algorithm

Agriculture, 2023
Soybean in the field has a wide range of intermixed weed species and a complex distribution status, and the weed identification rate of traditional methods is low.
Xinle Zhang, Jian Cui, Huanjun Liu, Yongqi Han, Hongfu Ai, Chang Dong, Jiaru Zhang, Yunxiang Chu   +7 more
doaj   +1 more source

MirBot: A collaborative object recognition system for smartphones using convolutional neural networks [PDF]

, 2018
MirBot is a collaborative application for smartphones that allows users to perform object recognition. This app can be used to take a photograph of an object, select the region of interest and obtain the most likely class (dog, chair, etc.) by means of ...
Bernabeu, Marisa, Gallego, Antonio-Javier, Pertusa, Antonio   +2 more
core   +3 more sources

Non-destructive classification of sugarcane milling feasibility using deep learning: A comparative study of VGG19 and ResNet50

IJAIN (International Journal of Advances in Intelligent Informatics)
Assessing sugarcane quality is crucial for ensuring both economic value and processing efficiency in sugar production. Conventional approaches, such as refractometer-based Brix measurements, are destructive, labor-intensive, and unsuitable for large ...
Nur Indrianti, Raden Achmad Chairdino Leuveano, Heru Cahya Rustamaji, Andrey Ferriyan, Panut Mulyono, Bayu Prasetya Wijaya   +5 more
doaj   +1 more source

Detection of ship targets in photoelectric images based on an improved recurrent attention convolutional neural network

International Journal of Distributed Sensor Networks, 2020
Deep learning algorithms have been increasingly used in ship image detection and classification. To improve the ship detection and classification in photoelectric images, an improved recurrent attention convolutional neural network is proposed.
Zhijing Xu, Yuhao Huo, Kun Liu, Sidong Liu   +3 more
doaj   +1 more source