Quantum computational infusion in extreme learning machines for early multi-cancer detection
Abstract 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 Optimi...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-02-01
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| Series: | Journal of Big Data |
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| Online Access: | https://doi.org/10.1186/s40537-024-01050-0 |
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| _version_ | 1823861943414292480 |
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| author | Anas Bilal Muhammad Shafiq Waeal J. Obidallah Yousef A. Alduraywish Haixia Long |
| author_facet | Anas Bilal Muhammad Shafiq Waeal J. Obidallah Yousef A. Alduraywish Haixia Long |
| author_sort | Anas Bilal |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-1c2c3c93210e439aa9ac3e1e3f26d4a7 |
| institution | Kabale University |
| issn | 2196-1115 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj-art-1c2c3c93210e439aa9ac3e1e3f26d4a72025-02-09T12:41:17ZengSpringerOpenJournal of Big Data2196-11152025-02-0112114810.1186/s40537-024-01050-0Quantum computational infusion in extreme learning machines for early multi-cancer detectionAnas Bilal0Muhammad Shafiq1Waeal J. Obidallah2Yousef A. Alduraywish3Haixia Long4College of Information Science and Technology, Hainan Normal UniversitySchool of Information Engineering, Qujing Normal UniversityCollege of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU)College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU)College of Information Science and Technology, Hainan Normal UniversityAbstract 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.https://doi.org/10.1186/s40537-024-01050-0Multi cancer diagnosisGrey Wolf OptimizationExtreme learning machine (ELM)Feature fusion |
| spellingShingle | Anas Bilal Muhammad Shafiq Waeal J. Obidallah Yousef A. Alduraywish Haixia Long Quantum computational infusion in extreme learning machines for early multi-cancer detection Journal of Big Data Multi cancer diagnosis Grey Wolf Optimization Extreme learning machine (ELM) Feature fusion |
| title | Quantum computational infusion in extreme learning machines for early multi-cancer detection |
| title_full | Quantum computational infusion in extreme learning machines for early multi-cancer detection |
| title_fullStr | Quantum computational infusion in extreme learning machines for early multi-cancer detection |
| title_full_unstemmed | Quantum computational infusion in extreme learning machines for early multi-cancer detection |
| title_short | Quantum computational infusion in extreme learning machines for early multi-cancer detection |
| title_sort | quantum computational infusion in extreme learning machines for early multi cancer detection |
| topic | Multi cancer diagnosis Grey Wolf Optimization Extreme learning machine (ELM) Feature fusion |
| url | https://doi.org/10.1186/s40537-024-01050-0 |
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