Quantum computational infusion in extreme learning machines for early multi-cancer detection

A timely and accurate cancer diagnosis is essential for improving treatment outcomes. This study presents a hybrid model integrating Extreme Learning Machine (ELM) with FuNet transfer learning, applied on a multi-cancer dataset and optimized using the Quantum-Genetic Binary Grey Wolf Optimizer (Q-GBGWO). This model leverages a diverse feature fusion strategy, enhancing the extraction of critical imaging features, while Q-GBGWO optimizes ELM parameters to achieve superior classification performance. Results demonstrate that Q-GBGWO-ELM improves diagnostic accuracy by an average of 6.5% compared to traditional methods, with notable accuracy rates across various cancers: 98.80% for breast cancer, 92.30% for brain tumors, 97.00% for skin cancer, and 96.98% for lung cancer. The model integrates advanced feature extraction and optimization techniques, indicating significant potential for early cancer detection. The proposed Q-GBGWO-ELM model contributes to a more innovative diagnostic approach in clinical practice by offering enhanced precision, efficiency, and adaptability across multiple cancer types. This advancement supports a shift toward more personalized and rapid diagnostic procedures, aiming to improve patient outcomes and reshape current cancer care practices with AI-driven accuracy and efficiency.