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

2022 ◽  
pp. 340-349
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.

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

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.

Comparative Analysis of Proposed Artificial Neural Network (ANN) Algorithm With Other Techniques

2020 ◽  
Vol 12 (1) ◽  
pp. 29-35
Author(s):  
Deepak Chatha ◽  
Alankrita Aggarwal ◽  
Rajender Kumar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Early Cancer ◽  
Region Of Interest ◽  
Cancer Stage ◽  
Low Contrast ◽  
Artificial Neural Network Ann ◽  
Mammogram Images ◽  
Background Contrast ◽  
Tumor Area

The mortality rate among women is increasing progressively due to cancer. Generally, women around 45 years old are vulnerable from this disease. Early detection is hope for patients to survive otherwise it may reach to unrecoverable stage. Currently, there are numerous techniques available for diagnosis of such a disease out of which mammography is the most trustworthy method for detecting early cancer stage. The analysis of these mammogram images are difficult to analyze due to low contrast and nonuniform background. The mammogram images are scanned and digitized for processing that further reduces the contrast between Region of Interest and background. Presence of noise, glands and muscles leads to background contrast variations. Boundaries of suspected tumor area are fuzzy & improper. Aim of paper is to develop robust edge detection technique which works optimally on mammogram images to segment tumor area. Output results of proposed technique on different mammogram images of MIAS database are presented and compared with existing techniques in terms of both Qualitative & Quantitative parameters.

Comparative Analysis of Proposed Artificial Neural Network (ANN) Algorithm With Other Techniques

2022 ◽  
pp. 1218-1223
Author(s):  
Deepak Chatha ◽  
Alankrita Aggarwal ◽  
Rajender Kumar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Comparative Analysis ◽  
Region Of Interest ◽  
Cancer Stage ◽  
Low Contrast ◽  
Artificial Neural Network Ann ◽  
Mammogram Images ◽  
Background Contrast ◽  
Tumor Area

The mortality rate among women is increasing progressively due to cancer. Generally, women around 45 years old are vulnerable from this disease. Early detection is hope for patients to survive otherwise it may reach to unrecoverable stage. Currently, there are numerous techniques available for diagnosis of such a disease out of which mammography is the most trustworthy method for detecting early cancer stage. The analysis of these mammogram images are difficult to analyze due to low contrast and nonuniform background. The mammogram images are scanned and digitized for processing that further reduces the contrast between Region of Interest and background. Presence of noise, glands and muscles leads to background contrast variations. Boundaries of suspected tumor area are fuzzy & improper. Aim of paper is to develop robust edge detection technique which works optimally on mammogram images to segment tumor area. Output results of proposed technique on different mammogram images of MIAS database are presented and compared with existing techniques in terms of both Qualitative & Quantitative parameters.

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

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.

scholarly journals PREDICTING STUDENTS«¤?? GRADE SCORES USING TRAINING FUNCTIONS OF ARTIFICIAL NEURAL NETWORK

2016 ◽  
Vol 14 (1) ◽  
pp. 27-45
Author(s):  
B. A. KALEJAYE ◽  
O. FOLORUNSO ◽  
O. L. USMAN
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Information Science ◽  
Early Stage ◽  
Poor Quality ◽  
Ann Model ◽  
Final Grade ◽  
Ogun State ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The observed poor quality of graduates of some Nigerian Universities in recent times has been traced to non-availability of adequate mechanism. This mechanism is expected to assist the policy maker project into the future performance of students, in order to discover at the early stage, students who have no tendency of doing well in school. This study focuses on the use of artificial neural network (ANN) model for predicting students«¤?? academic performance in a University System, based on the previous datasets. The domain used in the study consists of sixty (60) students in the Department of Computer and Information Science, Tai Solarin University of Education in Ogun State, who have completed four academic sessions from the university. The codes were written and executed using MATLAB format. The students«¤?? CGPA from first year through their third year were used as the inputs to train the ANN models constructed using nntool and the Final Grades (CGPA) served as a target output. The output predicted by the networks is expressed in-line with the current grading system of the case study. CGPA values simulated by the network are compared with the actual final CGPA to determine the efficacy of each of the three feed-forward neural networks used. Test data evaluations showed that the ANN model is able to predict correctly, the final grade of students with 91.7% accuracy.ª¤?

Application of the Artificial Neural Network (ANN) Method as MPPT Photovoltaic for DC Source Storage

2019 ◽  
Vol 12 (3) ◽  
pp. 145 ◽  
Author(s):  
Epyk Sunarno ◽  
Ramadhan Bilal Assidiq ◽  
Syechu Dwitya Nugraha ◽  
Indhana Sudiharto ◽  
Ony Asrarul Qudsi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann
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