scholarly journals Image Processing & Neural Network Based Breast Cancer Detection

2019 ◽  
Vol 12 (2) ◽  
pp. 146
Author(s):  
Marwan Abo Zanona
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Image Processing ◽  
Cancer Patients ◽  
Human Perception ◽  
Discrete Wavelet ◽  
Breast Cancer Patients ◽  
Input Material ◽  
Processing Techniques ◽  
Mammogram Images

A large percentage of cancer patients are breast cancer patients. The main available methodology to examine the breast cancer is the Mammography. It detects the signs of breast cancer as different signs supports the experts’ decision. Actually, the Mammography is based on human perception and observations. So, build an AI computerized system will take major role in early signs detection. This paper presents an image processing with aid of artificial neural networks computations for computerized signs detection and exploration of breast cancer. The input material is the mammogram images, and the output helps the pathologists to take a decision. A set of input mammogram images was used for development, testing, and evaluation. The mammographic image will be preprocessed and then the features will be extracted using discrete wavelet transformation with aid of Weiner filtration. A historical data of extracted features were used to train a neural network, while the historical extracted features contains both Cancer and non-Cancer images. The combination of neural network machine learning, and rigid image processing techniques resulted accurate outputs. The methodology and results are showed and discussed later in this paper.

Neural network prediction of relapse in breast cancer patients

1996 ◽  
Vol 4 (2) ◽  
pp. 105-113 ◽  
Author(s):  
L. Tarassenko ◽  
R. Whitehouse ◽  
G. Gasparini ◽  
A. L. Harris
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Cancer Patients ◽  
Breast Cancer Patients ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Prediction Of Relapse

scholarly journals Automated breast cancer detection system from breast mammogram using deep neural network

2022 ◽  
Vol 25 (1) ◽  
pp. 580
Author(s):  
Suneetha Chittineni ◽  
Sai Sandeep Edara
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Cancer Patients ◽  
Optimization Algorithm ◽  
Deep Neural Network ◽  
Detection System ◽  
Early Stage ◽  
Screening Methods ◽  
Breast Cancer Patients ◽  
Data Set

<p>All over the world breast cancer is a major disease which mostly affects the women and it may also cause death if it is not diagnosed in its early stage. But nowadays, several screening methods like magnetic resonance imaging (MRI), ultrasound imaging, thermography and mammography are available to detect the breast cancer. In this article mammography images are used to detect the breast cancer. In mammography image the cancerous lumps/microcalcifications are seen to be tiny with low contrast therefore it is difficult for the doctors/radiologist to detect it. Hence, to help the doctors/radiologist a novel system based on deep neural network is introduced in this article that detects the cancerous lumps/microcalcifications automatically from the mammogram images. The system acquires the mammographic images from the mammographic image analysis society (MIAS) data set. After pre-processing these images by 2D median image filter, cancerous features are extracted from the images by the hybridization of convolutional neural network with rat swarm optimization algorithm. Finally, the breast cancer patients are classified by integrating random forest with arithmetic optimization algorithm. This system identifies the breast cancer patients accurately and its performance is relatively high compared to other approaches.</p>

scholarly journals BRCA Variations Risk Assessment in Breast Cancers Using Different Artificial Intelligence Models

Genes ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1774
Author(s):  
Niyazi Senturk ◽  
Gulten Tuncel ◽  
Berkcan Dogan ◽  
Lamiya Aliyeva ◽  
Mehmet Sait Dundar ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Artificial Intelligence ◽  
Risk Factors ◽  
Risk Assessment ◽  
Cancer Patients ◽  
Breast Cancers ◽  
Breast Cancer Patients ◽  
Intelligence Models ◽  
Artificial Intelligence Models

Artificial intelligence provides modelling on machines by simulating the human brain using learning and decision-making abilities. Early diagnosis is highly effective in reducing mortality in cancer. This study aimed to combine cancer-associated risk factors including genetic variations and design an artificial intelligence system for risk assessment. Data from a total of 268 breast cancer patients have been analysed for 16 different risk factors including genetic variant classifications. In total, 61 BRCA1, 128 BRCA2 and 11 both BRCA1 and BRCA2 genes associated breast cancer patients’ data were used to train the system using Mamdani’s Fuzzy Inference Method and Feed-Forward Neural Network Method as the model softwares on MATLAB. Sixteen different tests were performed on twelve different subjects who had not been introduced to the system before. The rates for neural network were 99.9% for training success, 99.6% for validation success and 99.7% for test success. Despite neural network’s overall success was slightly higher than fuzzy logic accuracy, the results from developed systems were similar (99.9% and 95.5%, respectively). The developed models make predictions from a wider perspective using more risk factors including genetic variation data compared with similar studies in the literature. Overall, this artificial intelligence models present promising results for BRCA variations’ risk assessment in breast cancers as well as a unique tool for personalized medicine software.

scholarly journals Breast Cancer Diagnosis by Convolutional Neural Network and Advanced Thermal Exchange Optimization Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xiuzhen Cai ◽  
Xia Li ◽  
Navid Razmjooy ◽  
Noradin Ghadimi
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Image Processing ◽  
Convolutional Neural Network ◽  
Color Space ◽  
Breast Cancer Diagnosis ◽  
Discrete Wavelet ◽  
Human Errors ◽  
Thermal Exchange ◽  
Mammographic Images

A common gynecological disease in the world is breast cancer that early diagnosis of this disease can be very effective in its treatment. The use of image processing methods and pattern recognition techniques in automatic breast detection from mammographic images decreases human errors and increments the rapidity of diagnosis. In this paper, mammographic images are analyzed using image processing techniques and a pipeline structure for the diagnosis of the cancerous masses. In the first stage, the quality of mammogram images and the contrast of abnormal areas in the image are improved by using image contrast improvement and a noise decline. A method based on color space is then used for image segmentation that is followed by mathematical morphology. Then, for feature image extraction, a combined gray-level cooccurrence matrix (GLCM) and discrete wavelet transform (DWT) method is used. At last, a new optimized version of convolutional neural network (CNN) and a new improved metaheuristic, called Advanced Thermal Exchange Optimizer, are used for the classification of the features. A comparison of the simulations of the proposed technique with three different techniques from the literature applied on the MIAS mammogram database is performed to show its superiority. Results show that the accuracy of diagnosing cancer cases for the proposed method and applied on the MIAS database is 93.79%, and sensitivity and specificity are obtained 96.89% and 67.7%, respectively.

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