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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/myj7vzp6s5dvratkkqupv2ibgq" style="color: black;">Indonesian Journal of Electrical Engineering and Informatics (IJEEI)</a>
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<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.52549/ijeei.v8i4.2436">doi:10.52549/ijeei.v8i4.2436</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/x6hdow4gbbeynicdomh2v7oolm">fatcat:x6hdow4gbbeynicdomh2v7oolm</a> </span>
more »... ist. 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.
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