Lalasa Quantum Computing Method: A Unique Quantum Convolutional Neural Network Architecture
subject: artificial intelligence; cervical cancer; Quantum computing; quantum machine learning; Quantum neural networks
description: A novel Lalasa Quantum Computing Method architecture is presented in this paper for classification of medical images, aiming to enable efficient early detection of cancer. The proposed framework integrates a custom preprocessing pipeline that removes specular reflections using a SWIN Transformer and segments regions of interest via an Enhanced Gaussian Mixture Model. The quantum classification module employs amplitude encoding to map classical image data into quantum states, enabling structured feature extraction through a sequence of quantum convolutional layers with trainable variational circuits. The model was implemented using IBM Qiskit and trained on the publicly available Intel & Mobile ODT Cervical Cancer Screening dataset. Experimental results show a high overall classification accuracy of 98.58%, with moderate performance on class-specific F1-scores, recall, and precision. These results demonstrate the feasibility and effectiveness of quantum-classical hybrid models for medical image analysis, particularly in high-dimensional, low-sample scenarios. The study sets stage for future advancements in quantum machine learning applications in healthcare, with potential extensions involving real quantum hardware deployment and multiclass classification improvements. 2025 IEEE.
creator: Mukku, Lalasa; SivaChitraLakshmi, S.
source: 2025 IEEE 2nd International Conference on Electrical, Electronics, Communication and Computers, ELEXCOM 2025;
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 01-01-2025
rights: Restricted Access; Hardcopy may be available in the library
relation: ISBN: 979-833159078-9;
language: English
type: Conference paper
coverage: Mukku L., Department Of Computer Science and Engineering, CHRIST(Deemed to be University), Bengaluru, 560074, India; SivaChitraLakshmi S., Department Of Computer Science and Engineering(Emerging Technologies), SRM Institute of Science and Technology, Vadapalani, chennai, 600026, India