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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Enhanced Artificial Intelligence System for Diagnosing and Predicting Breast Cancer using Deep Learning

Author 1: Mona Alfifi
Author 2: Mohamad Shady Alrahhal
Author 3: Samir Bataineh
Author 4: Mohammad Mezher

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2020.0110763

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 7, 2020.

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Abstract: Breast cancer is the leading cause of death among women with cancer. Computer-aided diagnosis is an efficient method for assisting medical experts in early diagnosis, improving the chance of recovery. Employing artificial intelligence (AI) in the medical area is very crucial due to the sensitivity of this field. This means that the low accuracy of the classification methods used for cancer detection is a critical issue. This problem is accentuated when it comes to blurry mammogram images. In this paper, convolutional neural networks (CNNs) are employed to present the traditional convolutional neural network (TCNN) and supported convolutional neural network (SCNN) approaches. The TCNN and SCNN approaches contribute by overcoming the shift and scaling problems included in blurry mammogram images. In addition, the flipped rotation-based approach (FRbA) is proposed to enhance the accuracy of the prediction process (classification of the type of cancerous mass) by taking into account the different directions of the cancerous mass to extract effective features to form the map of the tumour. The proposed approaches are implemented on the MIAS medical dataset using 200 mammogram breast images. Compared to similar approaches based on KNN and RF, the proposed approaches show better performance in terms of accuracy, sensitivity, spasticity, precision, recall, time of performance, and quality of image metrics.

Keywords: Traditional Convolutional Neural Network (TCNN); Supported Convolutional Neural Network (SCNN); shift; scaling; cancer detection; mammogram; histogram equalization; adaptive median filter

Mona Alfifi, Mohamad Shady Alrahhal, Samir Bataineh and Mohammad Mezher, “Enhanced Artificial Intelligence System for Diagnosing and Predicting Breast Cancer using Deep Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 11(7), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110763

@article{Alfifi2020,
title = {Enhanced Artificial Intelligence System for Diagnosing and Predicting Breast Cancer using Deep Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110763},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110763},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {7},
author = {Mona Alfifi and Mohamad Shady Alrahhal and Samir Bataineh and Mohammad Mezher}
}


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