Breast Cancer Classification through Meta-Learning Ensemble Technique Using Convolution Neural Networks
This study aims to develop an efficient and accurate breast cancer classification model using meta-learning approaches and multiple convolutional neural networks. This Breast Ultrasound Images (BUSI) dataset contains various types of breast lesions. The goal is to classify these lesions as benign or...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/13/13/2242 |
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| author | Muhammad Danish Ali Adnan Saleem Hubaib Elahi Muhammad Amir Khan Muhammad Ijaz Khan Muhammad Mateen Yaqoob Umar Farooq Khattak Amal Al-Rasheed |
| author_facet | Muhammad Danish Ali Adnan Saleem Hubaib Elahi Muhammad Amir Khan Muhammad Ijaz Khan Muhammad Mateen Yaqoob Umar Farooq Khattak Amal Al-Rasheed |
| author_sort | Muhammad Danish Ali |
| collection | DOAJ |
| description | This study aims to develop an efficient and accurate breast cancer classification model using meta-learning approaches and multiple convolutional neural networks. This Breast Ultrasound Images (BUSI) dataset contains various types of breast lesions. The goal is to classify these lesions as benign or malignant, which is crucial for the early detection and treatment of breast cancer. The problem is that traditional machine learning and deep learning approaches often fail to accurately classify these images due to their complex and diverse nature. In this research, to address this problem, the proposed model used several advanced techniques, including meta-learning ensemble technique, transfer learning, and data augmentation. Meta-learning will optimize the model’s learning process, allowing it to adapt to new and unseen datasets quickly. Transfer learning will leverage the pre-trained models such as Inception, ResNet50, and DenseNet121 to enhance the model’s feature extraction ability. Data augmentation techniques will be applied to artificially generate new training images, increasing the size and diversity of the dataset. Meta ensemble learning techniques will combine the outputs of multiple CNNs, improving the model’s classification accuracy. The proposed work will be investigated by pre-processing the BUSI dataset first, then training and evaluating multiple CNNs using different architectures and pre-trained models. Then, a meta-learning algorithm will be applied to optimize the learning process, and ensemble learning will be used to combine the outputs of multiple CNN. Additionally, the evaluation results indicate that the model is highly effective with high accuracy. Finally, the proposed model’s performance will be compared with state-of-the-art approaches in other existing systems’ accuracy, precision, recall, and F1 score. |
| first_indexed | 2024-03-11T01:43:50Z |
| format | Article |
| id | doaj.art-3e524d6f6a1841af9f7b81b0efbb2b71 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-11T01:43:50Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-3e524d6f6a1841af9f7b81b0efbb2b712023-11-18T16:22:00ZengMDPI AGDiagnostics2075-44182023-06-011313224210.3390/diagnostics13132242Breast Cancer Classification through Meta-Learning Ensemble Technique Using Convolution Neural NetworksMuhammad Danish Ali0Adnan Saleem1Hubaib Elahi2Muhammad Amir Khan3Muhammad Ijaz Khan4Muhammad Mateen Yaqoob5Umar Farooq Khattak6Amal Al-Rasheed7Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad 22060, PakistanDepartment of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad 22060, PakistanDepartment of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad 22060, PakistanDepartment of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad 22060, PakistanInstitute of Computing and Information Technology, Gomal University, Dera Ismail Khan 29220, PakistanDepartment of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad 22060, PakistanSchool of Information Technology, UNITAR International University, Kelana Jaya, Petaling Jaya 47301, MalaysiaDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh 11671, Saudi ArabiaThis study aims to develop an efficient and accurate breast cancer classification model using meta-learning approaches and multiple convolutional neural networks. This Breast Ultrasound Images (BUSI) dataset contains various types of breast lesions. The goal is to classify these lesions as benign or malignant, which is crucial for the early detection and treatment of breast cancer. The problem is that traditional machine learning and deep learning approaches often fail to accurately classify these images due to their complex and diverse nature. In this research, to address this problem, the proposed model used several advanced techniques, including meta-learning ensemble technique, transfer learning, and data augmentation. Meta-learning will optimize the model’s learning process, allowing it to adapt to new and unseen datasets quickly. Transfer learning will leverage the pre-trained models such as Inception, ResNet50, and DenseNet121 to enhance the model’s feature extraction ability. Data augmentation techniques will be applied to artificially generate new training images, increasing the size and diversity of the dataset. Meta ensemble learning techniques will combine the outputs of multiple CNNs, improving the model’s classification accuracy. The proposed work will be investigated by pre-processing the BUSI dataset first, then training and evaluating multiple CNNs using different architectures and pre-trained models. Then, a meta-learning algorithm will be applied to optimize the learning process, and ensemble learning will be used to combine the outputs of multiple CNN. Additionally, the evaluation results indicate that the model is highly effective with high accuracy. Finally, the proposed model’s performance will be compared with state-of-the-art approaches in other existing systems’ accuracy, precision, recall, and F1 score.https://www.mdpi.com/2075-4418/13/13/2242artificial intelligencemachine learningmeta-learning ensemble techniqueconvolutional neural networksbreast cancerdeep learning |
| spellingShingle | Muhammad Danish Ali Adnan Saleem Hubaib Elahi Muhammad Amir Khan Muhammad Ijaz Khan Muhammad Mateen Yaqoob Umar Farooq Khattak Amal Al-Rasheed Breast Cancer Classification through Meta-Learning Ensemble Technique Using Convolution Neural Networks Diagnostics artificial intelligence machine learning meta-learning ensemble technique convolutional neural networks breast cancer deep learning |
| title | Breast Cancer Classification through Meta-Learning Ensemble Technique Using Convolution Neural Networks |
| title_full | Breast Cancer Classification through Meta-Learning Ensemble Technique Using Convolution Neural Networks |
| title_fullStr | Breast Cancer Classification through Meta-Learning Ensemble Technique Using Convolution Neural Networks |
| title_full_unstemmed | Breast Cancer Classification through Meta-Learning Ensemble Technique Using Convolution Neural Networks |
| title_short | Breast Cancer Classification through Meta-Learning Ensemble Technique Using Convolution Neural Networks |
| title_sort | breast cancer classification through meta learning ensemble technique using convolution neural networks |
| topic | artificial intelligence machine learning meta-learning ensemble technique convolutional neural networks breast cancer deep learning |
| url | https://www.mdpi.com/2075-4418/13/13/2242 |
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