使用医学图像的机器学习进行疾病诊断

Shakir M. Abas, Omer Mohammed Salih Hassan, Imad Manaf Ali, Safin Saber Nori, Hamza Sardar Hassan
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引用次数: 0

摘要

近年来,由于人类的生存环境和环境的变化,各种疾病正在感染人类。早期识别和预测这些疾病对预防这些疾病的爆发具有重要意义。由医生手动识别这些疾病是困难的。影响人类的慢性病有很多。其中一种疾病是脑肿瘤,它是由脑细胞的异常生长和分裂引起的,从而导致脑癌。计算机视觉在人类健康领域发挥着重要的作用,它能给出准确的结果,帮助人类做出真正的决策。此外,传统工艺耗时长,成本高,解决问题需要专业知识。本研究的重点是使用简单的深度学习架构并获得准确的结果。此外,采用卷积神经网络(CNN)算法对脑肿瘤图像进行可靠分类。所提出的模型在脑肿瘤检测1数据集的脑MRI图像上显示出非常好的结果,准确率接近96.4%。
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Diseases Diagnosis Using Machine Learning of Medical Images
Recently, the various diseases are infecting the humans due to their living environmental and the changes of the environmental. It is much important to identification and prediction of such diseases at earlier stages to prevent the outbreak these diseases. The identification of these diseases manually by the doctors is difficult. There are many of the chronic diseases that affect human. One of these diseases is the brain tumors that arise by the abnormal growth and division of brain cells which leads to brain cancer. The computer vision plays important role in human health field which gives accurate results that helps the human to tack the true decision. In addition, traditional technics are time consuming, expensive and addressed problem requires expert knowledge. This research aims to focus on the using simple deep learning architecture with accurate results. Moreover, the Convolution Neural Network (CNN) algorithm is used for reliable Classification of the brain tumor Image. The proposed models are showed very good results and reached almost 96.4% accuracy on Brain MRI Images for Brain Tumor Detection1 dataset.
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