Mobile Application for Breast Cancer Diagnosis Using Morphological Associative Memories Implemented on a Cloud Platform
Sergio Cerón-Figueroa, CIC, INstituto Politécnico Nacional, CDMX, México.
Manuscript received on November 19, 2019. | Revised Manuscript received on 28 November, 2019. | Manuscript published on December 10, 2019. | PP: 3763-3769 | Volume-9 Issue-2, December 2019. | Retrieval Number: B6234129219/2019©BEIESP | DOI: 10.35940/ijitee.B6234.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The astounding advances that have been observed in mobile device technologies and their underlying algorithms have prompted a worldwide surge in attention to their capabilities and potential for improving different human activities. The present work is framed by the academic cooperation process between Mexico and Saudi Arabia; it consists of the description of the design and development of a mobile information system aimed at performing diagnosis and verification of breast cancer using an application for mobile devices. The problem to be solved is represented as a binary classification problem between healthy patients and people that have been confirmed as control cases. The classification algorithm is a hybrid model, consisting of Morphological Associative Memories and the k-Nearest Neighbor classifier. The hybrid model improves upon the performance of its components. The proposed model was implemented on a cloud computing platform in order to optimize the response time for the diagnosis. A comparative study of our proposal and the state of the art shows that the proposed mobile information system has a high classification performance as well as a low false positive rate.
Keywords: Mobile Communications, Morphological Associative Memories, Early Diagnosis of Breast Cancer, Pattern Classification.
Scope of the Article: Classification