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Early diagnosis of cardiac abnormalities plays a crucial role in preventing severe cardiovascular diseases. This paper presents a novel approach for detecting and classifying small objects, such as anomalies, in cardi...
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ISBN:
(纸本)9798350354218
Early diagnosis of cardiac abnormalities plays a crucial role in preventing severe cardiovascular diseases. This paper presents a novel approach for detecting and classifying small objects, such as anomalies, in cardiac images to facilitate early diagnosis and intervention. The proposed methodology integrates various image processing and machine learning techniques, including input image preprocessing, edge detection, boundary extraction, KAZE feature extraction, region mapping, morphological analysis, ensemble learning, and convolutional neural network (CNN) classification, followed by decision-making mechanisms. Initially, the input cardiac images undergo preprocessing to enhance quality and reduce noise, followed by edge detection to identify potential regions of interest. Subsequently, boundary extraction techniques are applied to delineate object boundaries for more accurate analysis. KAZE feature extraction is then employed to capture discriminative features from the identified regions. Next, a region mapping approach is utilized to segment and classify small objects within the cardiac images. Morphological analysis is applied to refine the detected regions and improve classification accuracy. An ensemble learning method is then employed to integrate diverse classifiers for enhanced performance. Furthermore, a CNN classifier is trained on the extracted features to classify the detected objects into relevant categories, facilitating automated diagnosis. Finally, a decision-making mechanism is employed to interpret the classification results and provide actionable insights for healthcare professionals. The proposed approach offers a robust solution for early diagnosis of cardiac abnormalities by effectively detecting and classifying small objects in cardiac images. Experimental results demonstrate the efficacy of the proposed methodology in improving diagnostic accuracy and efficiency, thereby contributing to enhanced patient care and prognosis in cardiovascular
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