Depth Wise Convolution on Chest X-Ray & Comparative Analysis With Transfer Learning

Jayant Rathi, Kalp Talwadia, Hritvik Jamwal, Shailender Kumar
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Abstract

Medical Image Segmentation has an imperative job in diagnostic systems on various applications. Ultrasounds, X-rays, MRI, CAT and PET (Positron Emission Tomography) are dynamic and developing domains for research especially in image-processing techniques and algorithms. This field has also attracted significant investments and developments in recent times. Deep Learning models, specifically the Convolutional Neural Network Models (CNN) are state-of-art technologies for identifying medical ailments through visual imagery. The objective of this research is to develop and implement a DepthWise Convolution model that provides high accuracy in detecting Covid 19 Pneumonia from lung x-rays. We also juxtapose it with other models which have great accuracy i.e Transfer Learning Models.
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胸部x射线的深度智能卷积及其与迁移学习的比较分析
医学图像分割在诊断系统的各种应用中是一项必不可少的工作。超声波、x射线、核磁共振、CAT和PET(正电子发射断层扫描)是动态的和发展中的研究领域,特别是在图像处理技术和算法方面。近年来,这一领域也吸引了大量投资和发展。深度学习模型,特别是卷积神经网络模型(CNN)是通过视觉图像识别疾病的最先进技术。本研究的目的是开发和实现一个深度卷积模型,该模型可以从肺部x射线中高精度地检测Covid - 19肺炎。我们还将其与其他具有很高准确性的模型(如迁移学习模型)并置。
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