Intelligent Ultrasound Imaging for Enhanced Breast Cancer Diagnosis: Ensemble Transfer Learning Strategies
According to WHO statistics for 2018, there are 1.2 million cases and 700,000 deaths from breast cancer (BC) each year, making it the second-highest cause of mortality for women globally. In recent years, advances in artificial (AI) intelligence and machine (ML) learning have shown incredible potent...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10414098/ |
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| author | Kuncham Sreenivasa Rao Panduranga Vital Terlapu D. Jayaram Kalidindi Kishore Raju G. Kiran Kumar Rambabu Pemula M. Venu Gopalachari S. Rakesh |
| author_facet | Kuncham Sreenivasa Rao Panduranga Vital Terlapu D. Jayaram Kalidindi Kishore Raju G. Kiran Kumar Rambabu Pemula M. Venu Gopalachari S. Rakesh |
| author_sort | Kuncham Sreenivasa Rao |
| collection | DOAJ |
| description | According to WHO statistics for 2018, there are 1.2 million cases and 700,000 deaths from breast cancer (BC) each year, making it the second-highest cause of mortality for women globally. In recent years, advances in artificial (AI) intelligence and machine (ML) learning have shown incredible potential in increasing the accuracy and efficiency of BC diagnosis. This research describes an intelligent BC image analysis system that leverages the capabilities of transfer learning (TLs) with ensemble stacking ML models. As part of this research, we created a model for analyzing ultrasound BC images using cutting-edge TL models such as Inception V3, VGG-19, and VGG-16. We have implemented stacking of ensemble ML models, including MLP (Multi-Layer Perceptron) with different architectures (10 10, 20 20, and 30 30) and Support Vector Machines (SVM) with RBF and Polynomial kernels. We analyzed the effectiveness of the proposed system in performance parameters (accuracy (CA), sensitivity, specificity, and AUC). Compared to the results with existing BC diagnostic systems, the proposed method (Inception V3 + Staking) is superior, with performance parameters 0.947 of AUC and 0.858 of CA values. The proposed BCUI analysis system consists of data collection, pre-processing, transfer learning, ensemble stacking of ML models, and performance evaluation, with comparative analysis demonstrating its superiority over existing methods. |
| first_indexed | 2024-03-08T00:25:08Z |
| format | Article |
| id | doaj.art-b2e4748009114f83b74f598e9b87536f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T00:25:08Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b2e4748009114f83b74f598e9b87536f2024-02-16T00:01:16ZengIEEEIEEE Access2169-35362024-01-0112222432226310.1109/ACCESS.2024.335844810414098Intelligent Ultrasound Imaging for Enhanced Breast Cancer Diagnosis: Ensemble Transfer Learning StrategiesKuncham Sreenivasa Rao0https://orcid.org/0000-0002-2186-0419Panduranga Vital Terlapu1https://orcid.org/0000-0002-2186-0419D. Jayaram2Kalidindi Kishore Raju3G. Kiran Kumar4Rambabu Pemula5https://orcid.org/0000-0002-6565-2822M. Venu Gopalachari6https://orcid.org/0000-0001-7310-2700S. Rakesh7https://orcid.org/0000-0001-6904-2661Department of Computer Science and Engineering, Faculty of Science and Technology (IcfaiTech), The ICFAI Foundation for Higher Education, Hyderabad, Telangana, IndiaDepartment of Computer Science and Engineering, Aditya Institute of Technology and Management, Tekkali, Srikakulam, Andhra Pradesh, IndiaDepartment of Information Technology, Chaitanya Bharathi Institute of Technology, Hyderabad, Telangana, IndiaDepartment of Information Technology, SRKR Engineering College, Bhimavaram, Andhra Pradesh, IndiaDepartment of Computer Science and Engineering, Chaitanya Bharathi Institute of Technology, Hyderabad, Telangana, IndiaDepartment of Artificial Intelligence, Vidya Jyothi Institute of Technology, Hyderabad, Telangana, IndiaDepartment of Information Technology, Chaitanya Bharathi Institute of Technology, Hyderabad, Telangana, IndiaDepartment of Information Technology, Chaitanya Bharathi Institute of Technology, Hyderabad, Telangana, IndiaAccording to WHO statistics for 2018, there are 1.2 million cases and 700,000 deaths from breast cancer (BC) each year, making it the second-highest cause of mortality for women globally. In recent years, advances in artificial (AI) intelligence and machine (ML) learning have shown incredible potential in increasing the accuracy and efficiency of BC diagnosis. This research describes an intelligent BC image analysis system that leverages the capabilities of transfer learning (TLs) with ensemble stacking ML models. As part of this research, we created a model for analyzing ultrasound BC images using cutting-edge TL models such as Inception V3, VGG-19, and VGG-16. We have implemented stacking of ensemble ML models, including MLP (Multi-Layer Perceptron) with different architectures (10 10, 20 20, and 30 30) and Support Vector Machines (SVM) with RBF and Polynomial kernels. We analyzed the effectiveness of the proposed system in performance parameters (accuracy (CA), sensitivity, specificity, and AUC). Compared to the results with existing BC diagnostic systems, the proposed method (Inception V3 + Staking) is superior, with performance parameters 0.947 of AUC and 0.858 of CA values. The proposed BCUI analysis system consists of data collection, pre-processing, transfer learning, ensemble stacking of ML models, and performance evaluation, with comparative analysis demonstrating its superiority over existing methods.https://ieeexplore.ieee.org/document/10414098/Breast cancerdeep learningmachine learningtransfer learnersultrasound images |
| spellingShingle | Kuncham Sreenivasa Rao Panduranga Vital Terlapu D. Jayaram Kalidindi Kishore Raju G. Kiran Kumar Rambabu Pemula M. Venu Gopalachari S. Rakesh Intelligent Ultrasound Imaging for Enhanced Breast Cancer Diagnosis: Ensemble Transfer Learning Strategies IEEE Access Breast cancer deep learning machine learning transfer learners ultrasound images |
| title | Intelligent Ultrasound Imaging for Enhanced Breast Cancer Diagnosis: Ensemble Transfer Learning Strategies |
| title_full | Intelligent Ultrasound Imaging for Enhanced Breast Cancer Diagnosis: Ensemble Transfer Learning Strategies |
| title_fullStr | Intelligent Ultrasound Imaging for Enhanced Breast Cancer Diagnosis: Ensemble Transfer Learning Strategies |
| title_full_unstemmed | Intelligent Ultrasound Imaging for Enhanced Breast Cancer Diagnosis: Ensemble Transfer Learning Strategies |
| title_short | Intelligent Ultrasound Imaging for Enhanced Breast Cancer Diagnosis: Ensemble Transfer Learning Strategies |
| title_sort | intelligent ultrasound imaging for enhanced breast cancer diagnosis ensemble transfer learning strategies |
| topic | Breast cancer deep learning machine learning transfer learners ultrasound images |
| url | https://ieeexplore.ieee.org/document/10414098/ |
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