An EEG spike detection algorithm using artificial neural network with multi-channel correlation

An artificial neural network (ANN) is used to resolve problems related to complex scenarios and logical thinking. Nowadays, a cause for concern is the mortality rate among women due to cancer. Generally, women to around 45 years old are the most vulnerable to this disease. Early detection is the only hope for the patient to survive, otherwise it may reach an unrecoverable stage. Currently, there are numerous techniques available for the diagnosis of such diseases out of which mammography is the most trustworthy method for detecting early stage cancer. The analysis of these mammogram images is always difficult to analyze due to low contrast and non-uniform background. The mammogram images are scanned, digitized for processing, nut that further reduces the contrast between region of interest (ROI) and the background. Furthermore, presence of noise, glands, and muscles leads to background contrast variations. The boundaries of the suspected tumor area are always fuzzy and improper. The aim of this article is to develop a robust edge detection technique which works optimally on mammogram images to segment a tumor area.

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Research on the improvement of image edge detection algorithm based on artificial neural network

Optik ◽

10.1016/j.ijleo.2015.07.023 ◽

2015 ◽

Vol 126 (21) ◽

pp. 2974-2978 ◽

Cited By ~ 15

Author(s):

Jinan Gu ◽

Yuelong Pan ◽

Hongmei Wang

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Edge Detection ◽

Detection Algorithm ◽

Edge Detection Algorithm ◽

Image Edge Detection ◽

Image Edge ◽

Artificial Neural

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Counter-propagation artificial neural network-based motion detection algorithm for static-camera surveillance scenarios

Neurocomputing ◽

10.1016/j.neucom.2017.08.002 ◽

2018 ◽

Vol 273 ◽

pp. 481-493 ◽

Cited By ~ 7

Author(s):

Bo-Hao Chen ◽

Shih-Chia Huang ◽

Jui-Yu Yen

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Motion Detection ◽

Detection Algorithm ◽

Artificial Neural ◽

Camera Surveillance

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Developing Arabic License Plate Recognition System Using Artificial Neural Network and Canny Edge Detection

Baghdad Science Journal ◽

10.21123/bsj.2020.17.3.0909 ◽

2020 ◽

Vol 17 (3) ◽

pp. 0909

Author(s):

Bydaa Ali Hussain ◽

Mohammed Sadoon Hathal

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Edge Detection ◽

Recognition System ◽

Detection Algorithm ◽

License Plate ◽

License Plate Recognition ◽

Canny Edge Detection ◽

Canny Edge ◽

Artificial Neural

In recent years, there has been expanding development in the vehicular part and the number of vehicles moving on the roads in all the sections of the country. Arabic vehicle number plate identification based on image processing is a dynamic area of this work; this technique is used for security purposes such as tracking of stolen cars and access control to restricted areas. The License Plate Recognition System (LPRS) exploits a digital camera to capture vehicle plate numbers is used as input to the proposed recognition system. Basically, the proposed system consists of three phases, vehicle license plate localization, character segmentation, and character recognition, the License Plate (LP) detection is presented using canny edge detection algorithm, Connect Component Analysis (CCA) have been exploited for segmenting characters. Finally, a Multi-Layer Perceptron Artificial Neural Network (MLPANN) model is utilized to identify and detect the vehicle license plate characters, and hence the results are displayed as a text on GUI. The proposed system successfully detects LP and recognizes multi-style Arabic characters with rates of 96% and 97.872% respectively under different conditions.

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To Design a Mammogram Edge Detection Algorithm Using an Artificial Neural Network (ANN)

International Journal of Distributed Artificial Intelligence ◽

10.4018/ijdai.2019010104 ◽

2019 ◽

Vol 11 (1) ◽

pp. 34-43

Author(s):

Alankrita Aggarwal ◽

Deepak Chatha

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Edge Detection ◽

Early Stage ◽

Detection Algorithm ◽

Low Contrast ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Mammogram Images ◽

Tumor Area

An artificial neural network (ANN) is used to resolve problems related to complex scenarios and logical thinking. Nowadays, a cause for concern is the mortality rate among women due to cancer. Generally, women to around 45 years old are the most vulnerable to this disease. Early detection is the only hope for the patient to survive, otherwise it may reach an unrecoverable stage. Currently, there are numerous techniques available for the diagnosis of such diseases out of which mammography is the most trustworthy method for detecting early stage cancer. The analysis of these mammogram images is always difficult to analyze due to low contrast and non-uniform background. The mammogram images are scanned, digitized for processing, nut that further reduces the contrast between region of interest (ROI) and the background. Furthermore, presence of noise, glands, and muscles leads to background contrast variations. The boundaries of the suspected tumor area are always fuzzy and improper. The aim of this article is to develop a robust edge detection technique which works optimally on mammogram images to segment a tumor area.

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Improved cloud detection for the Aura Microwave Limb Sounder: Training an artificial neural network on colocated MLS and Aqua-MODIS data

10.5194/amt-2021-146 ◽

2021 ◽

Author(s):

Frank Werner ◽

Nathaniel Livesey ◽

Michael Schwartz ◽

William Read ◽

Michelle Santee ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Detection Algorithm ◽

Classification Performance ◽

Cloud Detection ◽

Data Set ◽

Modis Data ◽

Artificial Neural ◽

Cloud Top Pressure ◽

Level 2

Abstract. An improved cloud detection algorithm for the Aura Microwave Limb Sounder (MLS) is presented. This new algorithm is based on a feedforward artificial neural network and uses as input, for each MLS limb scan, a vector consisting of 1,710 brightness temperatures provided by MLS observations from 15 different tangent altitudes and up to 13 spectral channels in each of 10 different MLS bands. The model has been trained on global cloud properties reported by Aqua’s Moderate Resolution Imaging Spectroradiometer (MODIS). In total, the colocated MLS-MODIS data set consists of 162,117 combined scenes sampled on 208 days over 2005–2020. We show that the algorithm can correctly classify > 96 % of cloudy and clear instances for previously unseen MLS scans. A comparison to the current MLS cloudiness flag used in “Level 2” processing reveals a huge improvement in classification performance. For all profiles in the colocated MLS-MODIS data set, the algorithm successfully detects 97.8 % of profiles affected by clouds, up from 15.8 % for the Level 2 flagging. Meanwhile, false positives reported for actually clear profiles are reduced to 1.7 %, down from 6.2 % in Level 2. The classification performance is not dependent on geolocation. The new cloudiness flag is applied to determine average global cloud cover between 2015 and 2019, successfully reproducing the spatial patterns of mid-level to high clouds reported in previous studies. It is also applied to four example cloud fields to illustrate the reliable performance for different cloud structures with varying degrees of complexity. Training a similar model on MODIS-retrieved cloud top pressure yields reliable predictions with correlation coefficients greater than 0.99. The combination of cloudiness flag and predicted cloud top pressure provides the means to identify MLS profiles in the presence of high-reaching convection.

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