BC-QNet: A quantum-infused ELM model for breast cancer diagnosis

• Published in: Computers in biology and medicine 2024-06, Vol.175, p.108483, Article 108483
• Main Authors: Bilal, Anas, Imran, Azhar, Liu, Xiaowen, Liu, Xiling, Ahmad, Zohaib, Shafiq, Muhammad, El-Sherbeeny, Ahmed M., Long, Haixia
• Format: Article
• Language: English
• Subjects: Accuracy; Artificial intelligence; Artificial neural networks; Biopsy; Breast cancer; Breast cancer diagnosis; Breast Neoplasms - diagnostic imaging; Classification; Datasets; Deep learning; Diagnosis; Diagnosis, Computer-Assisted - methods; Enhanced diagnostic accuracy; Extreme learning machine (ELM); Feature extraction; Feature fusion; Female; Humans; Machine Learning; Mammography; Mammography - methods; Medical prognosis; Optimization; Transfer learning
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2024.108483

The timely and accurate diagnosis of breast cancer is pivotal for effective treatment, but current automated mammography classification methods have their constraints. In this study, we introduce an innovative hybrid model that marries the power of the Extreme Learning Machine (ELM) with FuNet transfer learning, harnessing the potential of the MIAS dataset. This novel approach leverages an Enhanced Quantum-Genetic Binary Grey Wolf Optimizer (Q-GBGWO) within the ELM framework, elevating its performance. Our contributions are twofold: firstly, we employ a feature fusion strategy to optimize feature extraction, significantly enhancing breast cancer classification accuracy. The proposed methodological motivation stems from optimizing feature extraction for improved breast cancer classification accuracy. The Q-GBGWO optimizes ELM parameters, demonstrating its efficacy within the ELM classifier. This innovation marks a considerable advancement beyond traditional methods. Through comparative evaluations against various optimization techniques, the exceptional performance of our Q-GBGWO-ELM model becomes evident. The classification accuracy of the model is exceptionally high, with rates of 96.54 % for Normal, 97.24 % for Benign, and 98.01 % for Malignant classes. Additionally, the model demonstrates a high sensitivity with rates of 96.02 % for Normal, 96.54 % for Benign, and 97.75 % for Malignant classes, and it exhibits impressive specificity with rates of 96.69 % for Normal, 97.38 % for Benign, and 98.16 % for Malignant classes. These metrics are reflected in its ability to classify three different types of breast cancer accurately. Our approach highlights the innovative integration of image data, deep feature extraction, and optimized ELM classification, marking a transformative step in advancing early breast cancer detection and enhancing patient outcomes. •Hybrid ELM and FuNet model utilizes MIAS for breast cancer classification.•Enhanced Q-GBGWO boosts ELM classification accuracy.•Feature fusion strategy enhances breast cancer detection.•Q-GBGWO optimizes ELM, outperforming traditional methods.•High accuracy rates: 96.54% Normal, 97.24% Benign, 98.01% Malignant.