Abstract
In this research work, detection of cardio diseases using Object detection techniques from 12 Lead ECG images is proposed. To detect the object in an image with different aspect ratios and with different sizes is one of the main challenges, this issue may lead to the wrong prediction of the diseases. To overcome those challenges, MobileNet with Feature Pyramid Network (FPN) feature extractor is used to extract the feature maps in different aspect ratios. By using the feature maps, the object is detected using the Single Shot Detector (SSD) technique. In addition, weighted sigmoid Focal Loss is adopted to diminish the imbalance among foreground and background samples to enrich detector outcomes. To endorse the effectiveness of the method proposed, a dataset is collected are Abnormal Heart Beat, Covid, Myocardial Infarction, Normal and Previous History of MI. Using the dataset collected, the proposed method gives a mAP accuracy of 95.88% in detection.
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