Depth Wise Convolution on Chest X-Ray & Comparative Analysis With Transfer Learning
2nd IEEE International Conference on Intelligent Technologies, CONIT 2022
; 2022.
Article
in English
| Scopus | ID: covidwho-2029213
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. © 2022 IEEE.
Chest X-Ray; Deep Learning; DepthWise Convolution; Medical Image Segmentation; pneu-monia; Transfer Learning; Convolutional neural networks; Diagnosis; Image segmentation; Learning systems; Magnetic resonance imaging; Medical imaging; Positron emission tomography; Comparative analyzes; Diagnostic systems; Learning models; X-ray emission; Convolution
Full text:
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
2nd IEEE International Conference on Intelligent Technologies, CONIT 2022
Year:
2022
Document Type:
Article
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