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Deep learning based Diagnosis of diseases using Image Classification

机译:基于深度学习的图像分类疾病的诊断

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Any disease whether it is curable or not, it must be diagnosed properly with some time in hand to take the appropriate actions in time. As it is popularly said that early detection of any disease is half cured. For detection of the diseases like Pneumonia, Covid-19, Brain Tumor Radiography, Computed Tomography are a technique popularly used nowadays. The motivation towards the study of the topic was that due to our country’s population density, the vulnerability of getting infected and not being treated nicely was exposed during the outbreak of Covid-19. The ratio of doctors in India is nearly 1:1456 which means 1 doctor has approximately 1456 patients to deal with. That also results in a lot of time being wasted on diagnosis, scheduling appointments, collection of reports, etc. which could prove to be critical for a patient. To reduce all the time being wasted, with the help of machine learning we intend to learn if we can predict if the patient is infected with a certain disease or not. How do we do that is by using deep learning models and analyze on the basis of a few X-Ray scan of the Chest to detect Pneumonia.
机译:无论是可固化的任何疾病是否是可固化的,必须在手中与一段时间合理地诊断,以便及时采取适当的行动。由于人们普遍表示,早期检测任何疾病都是半固化的。对于肺炎等疾病的检测,Covid-19,脑肿瘤造影,计算断层扫描是目前普遍使用的技术。研究主题研究的动机是,由于我国的人口密度,在Covid-19爆发期间暴露了被感染和未被治疗的脆弱性。印度医生的比例近1:1456,这意味着1名医生有大约1456名患者处理。这也导致令人损害了很多时间,以诊断,调度约会,报告的收集等,这可能证明对患者至关重要。为了减少浪费的一切浪费,在机器学习的帮助下,我们打算学习是否可以预测患者是否感染某种疾病。我们如何通过深入学习模型来使用深度学习模型,并根据胸部的几个X射线扫描来检测肺炎。

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