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All Journal Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Abeer M Mahmoud
‎1Faculty of computer and information sciences, Princess Nourah bint ‎Abdulrahman University, Riyadh, KSA.‎ ‎2Faculty of computer and information sciences, Ain Shams University, Cairo, ‎Egypt.‎
Computer Science & IT Electrical & Electronics Engineering

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Deep Learning-aided Brain Tumor Detection: An Initial ‎Experience based Cloud Framework ‎ Safia Abbas; Abeer M Mahmoud
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 8, No 4: December 2020
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v8i4.2436

Abstract

Lately, the uncertainty of diagnosing diseases increased and spread due to the huge intertwined and ambiguity of symptoms, that leads to overwhelming and hindering the reliability of the diagnosis ‎process. Since tumor detection from ‎MRI scans depends mainly on the specialist experience, ‎misdetection will result an inaccurate curing that might cause ‎critical harm consequent results. In this paper, detection service for brain tumors is introduced as ‎an aiding function for both patients and specialist. The ‎paper focuses on automatic MRI brain tumor detection under a cloud based framework for multi-medical diagnosed services. The proposed CNN-aided deep architecture contains two phases: the features extraction phase followed by a detection phase. The contour ‎detection and binary segmentation were applied to extract the region ‎of interest and reduce the unnecessary information before injecting the data into the model for training. The brain tumor ‎data was obtained from Kaggle datasets, it contains 2062 cases, ‎‎1083 tumorous and 979 non-tumorous after preprocessing and ‎augmentation phases. The training and validation phases have been ‎done using different images’ sizes varied between (16, 16) to ‎‎ (128,128). The experimental results show 97.3% for detection ‎accuracy, 96.9% for Sensitivity, and 96.1% specificity. Moreover, ‎using small filters with such type of images ensures better and faster ‎performance with more deep learning.‎
Co-Authors Safia Abbas