MUMBAI, India, July 24 -- Intellectual Property India has published a patent application (202641086112 A) filed by Vallurupalli Nageswara Rao Vignana Jyothi Institute Of Engineering And on July 14, 2026, for Hybrid Quantum Deep Learning System For Automated Renal Cyst Detection From Computed Tomography Images.
Inventors include Dr. B. V. Kiranmayee; Dr. D. N. Vasundhara; and S. Sudeshna.
The application for the patent was published on July 17, 2026, under issue no. 29/2026.
Abstract: ABSTRACT Renal cysts which refers to fluid sacs that are developed in the kidneys and were frequently identified during simple imaging tests. Although most of the cysts are benign in nature, few may be evidence of underlying disease conditions or gets complicated if they are left undiagnosed. Conventional diagnosis from computed tomography (CT) images is dependent on radiologist and is a time-consuming process, emphasising the design of automated and intelligent diagnostic tools. In this work, we introduce a new hybrid Quantum Deep Learning (QDL) model for automatic renal cyst identification from CT images. The proposed model combines classical deep learning with quantum neural networks to improve diagnostic accuracy. The dataset consists of labelled images of normal and cystic kidneys, which are pre-processed through Z-score normalization, stratified splitting, and data augmentation methods. A combination of hybrid convolutional neural network (CNN) with a Quantum Convolutional Neural Network (QCNN) is proposed for efficient feature extraction and classification. This approach yields a remarkable test accuracy of 96% and validation accuracy of 95%, which is superior to that of the standard deep learning methods. In addition, demographic correlations including age and gender are examined to gain more insights into patterns of prevalence and cyst characteristics.
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