Breast Cancer Classification through Meta-Learning Ensemble Technique Using Convolution Neural Networks

• Published in: Diagnostics
• Main Author: Ali M.D.; Saleem A.; Elahi H.; Khan M.A.; Khan M.I.; Yaqoob M.M.; Farooq Khattak U.; Al-Rasheed A.
• Format: Article
• Language: English
• Published: Multidisciplinary Digital Publishing Institute (MDPI) 2023
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164711241&doi=10.3390%2fdiagnostics13132242&partnerID=40&md5=0966e06b9cf7e380adfd999fb53afa6a

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. © 2023 by the authors.