Detection of varicose vein disease using optimized kernel Boosted ResNet-Dropped long Short term Memory

Abstract

Varicose Veins (V2) are the most harmful chronic disease when blood circulation of leg veins is not working precisely. This results in serious blood circulation problems from the heart to the leg. Diagnosing the V2 problem is highly cost-effective; earlier detection is necessary for the patients to relieve pain effectively. Hence, this article brings a novel hybridized deep learning (DL) technique for efficiently detecting the presence/absence of V2. The Median Kuan Filtering (Me_KF) based pre-processing technique is introduced to filter the noises from the raw leg vein images. After pre-processing, the Extended Twofold Autoencoder (ExT_AE) based DL technique is proposed to extract leg vein features. Then, Tent Chaotic Zebra Optimizer (TCh-ZO) algorithm is proposed to eliminate redundant features and enhance accuracy performance. Finally, this study introduces the Hybridized Kernel Boosted ResNet-Dropped Long Short Term Memory (HyKBRNet-DLSTM) based DL technique to detect the presence and absence of varicose veins. The experiment is processed with 11,350 leg vein images, and measures like accuracy, precision, kappa, mean square error (MSE) and time complexity are analyzed. The experimental section proposes an accuracy of 98.5%, a precision of 98.69%, a kappa of 95.77%, an MSE of 1.73 and a time complexity of 25.36 ms.