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.
Highlights
Medical misdaignose has spreaded widely these days and lots of people suffering directly or indirectly from such misdiagnosing. passing away and permanent disabilities are consequences of misdiagnosis that results from misanalysis for medical conditions and wrong medication treatment
The paper focuses on automatic MRI brain tumor detection under a cloud-based framework for multi-medical diagnosed services
Reasons for misdaiagnosis and mistreatment varied widely including but not limited to, insufficient knowledge about certain diseases, the often symptoms overlapping of different diseases, and mis-ordering of laboratory tests. Researchers dedicate their efforts to harness data science and Internet technologies searching for solutions for such misdiagnosis disaster that increases by time and by discovering of new diseases
Summary
Medical misdaignose has spreaded widely these days and lots of people suffering directly or indirectly from such misdiagnosing. passing away and permanent disabilities are consequences of misdiagnosis that results from misanalysis for medical conditions and wrong medication treatment. The service contains several layers and uses multiple classifier agents to assure accurate decisions It aims to enrich the eHealth domain, by providing a new layer in the SaaS cloud architecture, introducing a proactive monitoring diagnostic system that collects different information continuously about patients, processing various types of input data either analogue or digital (numeric, continuous, images), deliver an interactive system, for diagnosis and for knowledge education about different types of chronic diseases, that aids in the management and diagnose processes. In [10], authors focused on providing a home care service for precaution knowledge as a protection phase before getting badly infected or sick depending on cloud Lucene ditributed cluster.The application provides an on-demand data storage model as well as an elastic scalable model that manage the rush hour access It uses bloom filter signature, and formal concept computation for data analysis. After pre-processing, training, and validation phases using different images’ sizes, the model shows better results than other researches that have been done on the same dataset, where, a higher accuracy, specificity, and sensitivity and lower loss is obtained
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