Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Journal of Electronics, Electromedical Engineering, and Medical Informatics
Gangappa, Malige
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Quantum-Enhanced Brain Tumor Detection and Progression Prediction Using MRI Imaging Gangappa, Malige; Manju, D; Krishnna, Maringanti Gopi; Reddy, M. Sree Mithra; Sathish, M.; Shahabaaz, Sk; Shanthan, A.; Chaitanya, M.
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 2 (2025): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i2.720

Abstract

Brain tumor identification and change over time analysis are essential for timely diagnosis and effective treatment scheduling and planing. This study presents a hybrid quantum-classical deep learning framework integrating Quantum Convolutional Neural Networks (QCNNs) with classical CNN to improve MRI-based tumor classification. Unlike traditional CNNs, which suffer from high computational costs and limited feature extraction capabilities, the proposed Quantum-Enhanced Tumor Analysis Framework (QETAF) leverages quantum feature maps to enhance tumor localization and segmentation. This study utilizes the BraTS MRI dataset (comprising 67,000 labeled scans) and applies contrast enhancement, intensity normalization, and augmentation techniques for preprocessing. The novel hybrid model employs CNN model for extracting the essential features initially and QCNN for refined feature representation, significantly improving tumor classification accuracy. Moreover, morphological variations can be monitored using Recurrent Quantum Neural Networks (RQNNs), which have been employed to track tumor progression. According to investigational results, RQNN increases the accuracy of tumor progress prediction, whereas QCNN beats regular CNNs with an 89% Dice Coefficient. Compared to classical models, the proposed approach reduces inference time by 28% while maintaining superior classification performance. This quantum-assisted model presents a novel pathway for enhancing computational efficiency and precision in brain tumor diagnostics, covering the way for more consistent clinical diagnostics.
Secure Image Transmission using Quantum-Resilient and Gate Network for Latent-Key Generation Gangappa, Malige; Satyanarayana, Balla V V; A, Dheeraj
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 4 (2025): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i4.1156

Abstract

Recently, deep learning-based techniques have undergone rapid development, yielding promising results in various fields. For making more complex operations in day-to-day tasks, the arbitrary resolution of JPEG image data security requires more than just deep learning in this modern era. To overcome this, our research introduces a pioneering synergistic framework for a quantum-resistant deep learning technique, which is expected to provide next-generation robust security in the dynamic resolution of multi-JPEG-image-based joint compression-encryption. Our proposed framework features dual-parallel processing of a dynamic gate network, utilizing a convolutional neural network for specialization detailing and quantum-inspired transformations. These transformations leverage Riemann zeta functions for depth feature extraction, integrated with a chaotic sequence and dynamic iterations to generate a latent-fused chaotic key for image joint compression and encryption. Further, the authenticity of an encrypted image that is bound by a secure pattern derived from a random transform variance anchors cryptographic operations. Then, bound data transmitted through a Synergic Curve Key Exchange Engine fused with renowned Chen attractors to generate non-invertible keys for transmission. Finally, experimental results of the image reconstruction quality measured by the structural similarity index metric were 98.82 1.12. Security validation incorporates different metrics by addressing the entropy analysis to quantify resistance against differential and statistical attacks, with a yield of 7.9980 0.0015. In conclusion, the whole implementation uniquely combines latent-fused chaotic with improved key space analysis for discrete cosine transform quantization with authenticated encryption, establishing an adversarial-resistant pipeline that simultaneously compresses data and validates integrity through pattern-bound authentication
Co-Authors A, Dheeraj Chaitanya, M. Krishnna, Maringanti Gopi Manju, D Reddy, M. Sree Mithra Sathish, M. Satyanarayana, Balla V V Shahabaaz, Sk Shanthan, A.