200 related articles for article (PubMed ID: 36217185)
21. Research on machine vision and deep learning based recognition of cotton seedling aphid infestation level.
Xu X; Shi J; Chen Y; He Q; Liu L; Sun T; Ding R; Lu Y; Xue C; Qiao H
Front Plant Sci; 2023; 14():1200901. PubMed ID: 37645464
[No Abstract] [Full Text] [Related]
22. Facial Expressions Recognition for Human-Robot Interaction Using Deep Convolutional Neural Networks with Rectified Adam Optimizer.
Melinte DO; Vladareanu L
Sensors (Basel); 2020 Apr; 20(8):. PubMed ID: 32340140
[TBL] [Abstract][Full Text] [Related]
23. Performance of deep convolutional neural network for classification and detection of oral potentially malignant disorders in photographic images.
Warin K; Limprasert W; Suebnukarn S; Jinaporntham S; Jantana P
Int J Oral Maxillofac Surg; 2022 May; 51(5):699-704. PubMed ID: 34548194
[TBL] [Abstract][Full Text] [Related]
24. White blood cells detection and classification based on regional convolutional neural networks.
Kutlu H; Avci E; Özyurt F
Med Hypotheses; 2020 Feb; 135():109472. PubMed ID: 31760248
[TBL] [Abstract][Full Text] [Related]
25. Application of Deep-Learning Methods to Bird Detection Using Unmanned Aerial Vehicle Imagery.
Hong SJ; Han Y; Kim SY; Lee AY; Kim G
Sensors (Basel); 2019 Apr; 19(7):. PubMed ID: 30959913
[TBL] [Abstract][Full Text] [Related]
26. AI-based analysis of oral lesions using novel deep convolutional neural networks for early detection of oral cancer.
Warin K; Limprasert W; Suebnukarn S; Jinaporntham S; Jantana P; Vicharueang S
PLoS One; 2022; 17(8):e0273508. PubMed ID: 36001628
[TBL] [Abstract][Full Text] [Related]
27. Agricultural Greenhouses Detection in High-Resolution Satellite Images Based on Convolutional Neural Networks: Comparison of Faster R-CNN, YOLO v3 and SSD.
Li M; Zhang Z; Lei L; Wang X; Guo X
Sensors (Basel); 2020 Aug; 20(17):. PubMed ID: 32878345
[TBL] [Abstract][Full Text] [Related]
28. Real-time Detection of Aortic Valve in Echocardiography using Convolutional Neural Networks.
Nizar MHA; Chan CK; Khalil A; Yusof AKM; Lai KW
Curr Med Imaging; 2020; 16(5):584-591. PubMed ID: 32484093
[TBL] [Abstract][Full Text] [Related]
29. Deep Learning-Based Methods for Automatic Diagnosis of Skin Lesions.
El-Khatib H; Popescu D; Ichim L
Sensors (Basel); 2020 Mar; 20(6):. PubMed ID: 32245258
[TBL] [Abstract][Full Text] [Related]
30. Detection and classification of the breast abnormalities in digital mammograms via regional Convolutional Neural Network.
Al-Masni MA; Al-Antari MA; Park JM; Gi G; Kim TY; Rivera P; Valarezo E; Han SM; Kim TS
Annu Int Conf IEEE Eng Med Biol Soc; 2017 Jul; 2017():1230-1233. PubMed ID: 29060098
[TBL] [Abstract][Full Text] [Related]
31. Deep Learning Computer-Aided Diagnosis for Breast Lesion in Digital Mammogram.
Al-Antari MA; Al-Masni MA; Kim TS
Adv Exp Med Biol; 2020; 1213():59-72. PubMed ID: 32030663
[TBL] [Abstract][Full Text] [Related]
32. Deep-learning-based, computer-aided classifier developed with a small dataset of clinical images surpasses board-certified dermatologists in skin tumour diagnosis.
Fujisawa Y; Otomo Y; Ogata Y; Nakamura Y; Fujita R; Ishitsuka Y; Watanabe R; Okiyama N; Ohara K; Fujimoto M
Br J Dermatol; 2019 Feb; 180(2):373-381. PubMed ID: 29953582
[TBL] [Abstract][Full Text] [Related]
33. Artificial intelligence using a convolutional neural network for automatic detection of small-bowel angioectasia in capsule endoscopy images.
Tsuboi A; Oka S; Aoyama K; Saito H; Aoki T; Yamada A; Matsuda T; Fujishiro M; Ishihara S; Nakahori M; Koike K; Tanaka S; Tada T
Dig Endosc; 2020 Mar; 32(3):382-390. PubMed ID: 31392767
[TBL] [Abstract][Full Text] [Related]
34. Weed Identification by Single-Stage and Two-Stage Neural Networks: A Study on the Impact of Image Resizers and Weights Optimization Algorithms.
Saleem MH; Velayudhan KK; Potgieter J; Arif KM
Front Plant Sci; 2022; 13():850666. PubMed ID: 35548295
[TBL] [Abstract][Full Text] [Related]
35. A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification.
Al-Antari MA; Al-Masni MA; Choi MT; Han SM; Kim TS
Int J Med Inform; 2018 Sep; 117():44-54. PubMed ID: 30032964
[TBL] [Abstract][Full Text] [Related]
36. Detection and classification of breast lesions with You Only Look Once version 5.
Meng M; Zhang M; Shen D; He G; Guo Y
Future Oncol; 2022 Dec; 18(39):4361-4370. PubMed ID: 36519579
[TBL] [Abstract][Full Text] [Related]
37. Computer-aided epiluminescence microscopy of pigmented skin lesions: the value of clinical data for the classification process.
Binder M; Kittler H; Dreiseitl S; Ganster H; Wolff K; Pehamberger H
Melanoma Res; 2000 Dec; 10(6):556-61. PubMed ID: 11198477
[TBL] [Abstract][Full Text] [Related]
38. Deep neural networks show an equivalent and often superior performance to dermatologists in onychomycosis diagnosis: Automatic construction of onychomycosis datasets by region-based convolutional deep neural network.
Han SS; Park GH; Lim W; Kim MS; Na JI; Park I; Chang SE
PLoS One; 2018; 13(1):e0191493. PubMed ID: 29352285
[TBL] [Abstract][Full Text] [Related]
39. Automatic detection of erosions and ulcerations in wireless capsule endoscopy images based on a deep convolutional neural network.
Aoki T; Yamada A; Aoyama K; Saito H; Tsuboi A; Nakada A; Niikura R; Fujishiro M; Oka S; Ishihara S; Matsuda T; Tanaka S; Koike K; Tada T
Gastrointest Endosc; 2019 Feb; 89(2):357-363.e2. PubMed ID: 30670179
[TBL] [Abstract][Full Text] [Related]
40. Computer-aided diagnosis of endobronchial ultrasound images using convolutional neural network.
Chen CH; Lee YW; Huang YS; Lan WR; Chang RF; Tu CY; Chen CY; Liao WC
Comput Methods Programs Biomed; 2019 Aug; 177():175-182. PubMed ID: 31319946
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]