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

Abstract

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.

Advancements in Telehealth: Enhancing Breast Cancer Detection and Health Automation through Smart Integration of IoT and CNN Deep Learning in Residential and Healthcare Settings

“…Moreover, integrating breast cancer into the IoT framework holds significant potential for early detection and monitoring. Although [10][11][12] have delved into IoT applications in educational settings, none of these works addressed Electronic Health Records (EHRs), nor did they highlight the appropriateness of addressing health-related issues in institutional homes through the implementation of health sensors, as proposed in their research.…”

“…Previous studies have outlined challenges, including limitations in representative datasets, poor kernel performance, and heavyweight CNN models, prompting future efforts to optimize datasets, employ augmentation algorithms, and implement segmentation techniques with effective kernels to build lightweight CNN models [23,29,38,39]. While earlier studies indicated 100% accuracy in breast cancer prediction [14], subsequent investigations by Gupta and colleagues [10,11,29,40] reported reduced accuracy ranging from 76% to 96.88%. This study seeks to assess the effectiveness of associated models in identifying the parameters that contribute most significantly to the proposed breast cancer detection system.…”

Fine tuning deep learning models for breast tumor classification

“…Ali et al 26 enhanced BC classification precision by merging meta-learning with CNNs. Using the BUSI dataset to categorize breast lesions, they overcame traditional method challenges with models like Inception V3, ResNet50, and DenseNet121, combined with preprocessing.…”

Intelligent Ultrasound Imaging for Enhanced Breast Cancer Diagnosis: Ensemble Transfer Learning Strategies

“…SVM is a ML algorithm utilized for regression as well as classification tasks. It depicts data points in highdimensional space to locate an optimal hyperplane that distinguishes various classes or forecasts continuous values [26] [27]. In SVM, the choice of kernel function determines how the information is converted into a higher-dimensional space.…”