A deep convolutional neural network-based automatic detection of brain metastases with and without blood vessel suppression

2022 ◽  
Author(s):  
Yoshitomo Kikuchi ◽  
Osamu Togao ◽  
Kazufumi Kikuchi ◽  
Daichi Momosaka ◽  
Makoto Obara ◽  
...  
Keyword(s):  
Neural Network ◽  
Blood Vessel ◽  
Brain Metastases ◽  
Convolutional Neural Network ◽  
Automatic Detection ◽  
Deep Convolutional Neural Network

Automatic detection and segmentation of brain metastases on multimodal MR images with a deep convolutional neural network

2018 ◽  
Vol 95 ◽  
pp. 43-54 ◽  
Author(s):  
Odelin Charron ◽  
Alex Lallement ◽  
Delphine Jarnet ◽  
Vincent Noblet ◽  
Jean-Baptiste Clavier ◽  
...  
Keyword(s):  
Neural Network ◽  
Brain Metastases ◽  
Convolutional Neural Network ◽  
Automatic Detection ◽  
Deep Convolutional Neural Network ◽  
Mr Images

AUTOMATED SCREENING OF DIABETIC RETINOPATHY WITH OPTIMIZED DEEP CONVOLUTIONAL NEURAL NETWORK: ENHANCED MOTH FLAME MODEL

2021 ◽  
Vol 21 (01) ◽  
pp. 2150005
Author(s):  
ARUN T NAIR ◽  
K. MUTHUVEL
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Blood Vessel ◽  
Convolutional Neural Network ◽  
Optimization Algorithm ◽  
Vessel Segmentation ◽  
Deep Convolutional Neural Network ◽  
Gray Level ◽  
Pass Filter ◽  
Blood Vessel Segmentation

Nowadays, analysis on retinal image exists as one of the challenging area for study. Numerous retinal diseases could be recognized by analyzing the variations taking place in retina. However, the main disadvantage among those studies is that, they do not have higher recognition accuracy. The proposed framework includes four phases namely, (i) Blood Vessel Segmentation (ii) Feature Extraction (iii) Optimal Feature Selection and (iv) Classification. Initially, the input fundus image is subjected to blood vessel segmentation from which two binary thresholded images (one from High Pass Filter (HPF) and other from top-hat reconstruction) are acquired. These two images are differentiated and the areas that are common to both are said to be the major vessels and the left over regions are fused to form vessel sub-image. These vessel sub-images are classified with Gaussian Mixture Model (GMM) classifier and the resultant is summed up with the major vessels to form the segmented blood vessels. The segmented images are subjected to feature extraction process, where the features like proposed Local Binary Pattern (LBP), Gray-Level Co-Occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRM) are extracted. As the curse of dimensionality seems to be the greatest issue, it is important to select the appropriate features from the extracted one for classification. In this paper, a new improved optimization algorithm Moth Flame with New Distance Formulation (MF-NDF) is introduced for selecting the optimal features. Finally, the selected optimal features are subjected to Deep Convolutional Neural Network (DCNN) model for classification. Further, in order to make the precise diagnosis, the weights of DCNN are optimally tuned by the same optimization algorithm. The performance of the proposed algorithm will be compared against the conventional algorithms in terms of positive and negative measures.

scholarly journals Automatic Detection of Tulip Breaking Virus (TBV) Using a Deep Convolutional Neural Network

2019 ◽  
Vol 52 (30) ◽  
pp. 12-17 ◽  
Author(s):  
Gerrit Polder ◽  
Nick van de Westeringh ◽  
Janne Kool ◽  
Haris Ahmad Khan ◽  
Gert Kootstra ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Automatic Detection ◽  
Deep Convolutional Neural Network ◽  
Tulip Breaking Virus

scholarly journals Automatic Detection of Stationary Fronts around Japan Using a Deep Convolutional Neural Network

SOLA ◽  
2019 ◽  
Vol 15 (0) ◽  
pp. 154-159 ◽  
Author(s):  
Daisuke Matsuoka ◽  
Shiori Sugimoto ◽  
Yujin Nakagawa ◽  
Shintaro Kawahara ◽  
Fumiaki Araki ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Automatic Detection ◽  
Deep Convolutional Neural Network

scholarly journals Study on automatic detection and classification of breast nodule using deep convolutional neural network system

2020 ◽  
Vol 12 (9) ◽  
pp. 4690-4701
Author(s):  
Feiqian Wang ◽  
Xiaotong Liu ◽  
Na Yuan ◽  
Buyue Qian ◽  
Litao Ruan ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Automatic Detection ◽  
Deep Convolutional Neural Network ◽  
Network System ◽  
Breast Nodule ◽  
Neural Network System

scholarly journals A deep convolutional neural network-based automatic delineation strategy for multiple brain metastases stereotactic radiosurgery

PLoS ONE ◽  
2017 ◽  
Vol 12 (10) ◽  
pp. e0185844 ◽  
Author(s):  
Yan Liu ◽  
Strahinja Stojadinovic ◽  
Brian Hrycushko ◽  
Zabi Wardak ◽  
Steven Lau ◽  
...  
Keyword(s):  
Neural Network ◽  
Brain Metastases ◽  
Convolutional Neural Network ◽  
Stereotactic Radiosurgery ◽  
Deep Convolutional Neural Network ◽  
Multiple Brain Metastases

Automatic detection of blood content in capsule endoscopy images based on a deep convolutional neural network

2019 ◽  
Vol 35 (7) ◽  
pp. 1196-1200 ◽  
Author(s):  
Tomonori Aoki ◽  
Atsuo Yamada ◽  
Yusuke Kato ◽  
Hiroaki Saito ◽  
Akiyoshi Tsuboi ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Capsule Endoscopy ◽  
Automatic Detection ◽  
Deep Convolutional Neural Network ◽  
Blood Content

Automatic detection of erosions and ulcerations in wireless capsule endoscopy images based on a deep convolutional neural network

2019 ◽  
Vol 89 (2) ◽  
pp. 357-363.e2 ◽  
Author(s):  
Tomonori Aoki ◽  
Atsuo Yamada ◽  
Kazuharu Aoyama ◽  
Hiroaki Saito ◽  
Akiyoshi Tsuboi ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Capsule Endoscopy ◽  
Automatic Detection ◽  
Wireless Capsule Endoscopy ◽  
Deep Convolutional Neural Network ◽  
Wireless Capsule
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