Neural network prediction of relapse in breast cancer patients

<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>

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

Genes ◽

10.3390/genes12111774 ◽

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.

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Image Processing & Neural Network Based Breast Cancer Detection

Computer and Information Science ◽

10.5539/cis.v12n2p146 ◽

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.

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