Deep vein thrombosis detection via combination of neural networks

Abstract

Deep Vein Thrombosis (DVT) is the result of blood clots in the veins of the body especially in the legs. The most catastrophic importance of DVT is pulmonary embolism (PE), which is caused by a portion of the clot breaking off and entering the bloodstream and lungs. However, the clinical diagnosis of DVT is time consuming so if a computer aided system is available it will be really efficient. In this study, a novel Deep R-Belief network is proposed to identify DVT in Duplex Ultrasound (DUS) images. Initially, the input DUS images are denoised with the gaussian filter to remove the noisy distortions and Contrast Limited Adaptive Histogram Equalization (CLAHE) technique is applied to these images for improving the image quality. Then, the noise-free images are given as input to the fuzzy based threshold algorithm for segmenting the edges. The deep learning based RegNet is utilized for extracting the most relevant features from the segmented output images. After that, Deep belief Network (DBN) is applied for classifying the DUS images into coronary thrombosis, venous thrombo embolism and pulmonary embolism. The competence of the Deep R-Belief network was assessed by metrics like precision, specificity, accuracy, recall, and F1 score. From the experimental findings, the accuracy of the proposed Deep R-Belief network method is 98.63%. The efficiency of the Deep R-Belief network advances the overall accuracy value by 9.7%, 18.8% and 15.8% better than DL-CNN, SESARF and XGBoost respectively.