X Ray Coronary Angiographic Image based Aortic Stenosis Detection using Matlab Simulation
Title: - Multi Scale Feature Entrenched Aortic Stenosis Detection Using Deep Learning and X-Ray Coronary Angiographic Imaging
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Install ----- Required software
Note:
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Give our file path after the project copied into some specified external disk change the file path as like as given below
Copy the ZIP file and paste in to the E drive and extract it.
Then Copy the Folder "Multi_Scale_Feature_Entrenched_Aortic_Stenosis_Detection" under the code folder and paste into the E drive. Then refer the Screenshot.
Implementation Plan:
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Step1: Initially, we load the chest X-rays image from the Test dataset. Randomly 20 % of images were selected for the test dataset; the remaining 80% for Training the dataset.
Step2: Next, we Perform three level Data Pre-processing , like, Noise reduction,Augmentation and Contrast enhancement based on the Homomorphic
filter process.
Step3: Next, To enhance the accuracy of stenosis detection perform the key frame detection process by using GAN algorithm.
Step4: Then, perform the feature extraction and classification process based on multi scale feature pyramid fusion module process. Finally, based on the extracted features of X-ray coronary angiographic images are classified into two classes such as stenosis disease and normal.
Step5: Finally, we plot the following performance metrics, Accuracy,Recall,Precision and F1 score.
Software Requirement
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1. Python – 3.8.2
2. OS: Windows 10 (64 bit)
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