A novel approach for breast cancer detection using optimized ensemble learning framework and XAI

• Published in: Image and vision computing 2024-02, Vol.142, p.104910, Article 104910
• Main Authors: Munshi, Raafat M., Cascone, Lucia, Alturki, Nazik, Saidani, Oumaima, Alshardan, Amal, Umer, Muhammad
• Format: Article
• Language: English
• Subjects: Breast cancer detection; Deep convoluted features; Ensemble learning; Healthcare; Image processing; Transfer learning
• ISSN: 0262-8856; 1872-8138
• DOI: 10.1016/j.imavis.2024.104910

Breast cancer (BC) is a common and highly lethal ailment. It stands as the second leading contributor to cancer-related deaths in women worldwide. The timely identification of this condition is of utmost importance in mitigating mortality rates. This research paper presents a novel framework for the precise identification of BC, utilising a combination of image and numerical data features with explainable Artificial Intelligence (XAI). The utilisation of the U-NET transfer learning model is employed for image-based prediction. Additionally, an ensemble model is constructed by integrating characteristics from a customised convolutional neural network (CNN) model with an ensemble comprising random forest (RF) and support vector machine (SVM). The experiments aim to evaluate the influence of original features compared to convoluted features. A comparative analysis is carried out to assess the efficacy of various classifiers in accurately detecting BC, utilising the Wisconsin dataset. The model under consideration exhibits promising capabilities in enhancing BC diagnosis, with a remarkable accuracy rate of 99.99%. The present study contributes to the advancement of BC diagnosis by introducing a novel strategy based on machine learning and discussing the interpretation of the variables using XAI. The primary objective of this approach is to get a notable level of precision, hence facilitating the early and reliable identification of BC. Ultimately, the implementation of this approach is expected to enhance patient outcomes. •This study presents a framework for breast cancer diagnosis using image & numerical features.•For image-based prognosis, the predictive model employs U-NET transfer learning.•An ensemble strategy (RF + SVM) is proposed using CNN features for breast cancer detection•The experiments involve both numerical features of dataset and those derived from the CNN model.•Proposed model is assessed by comparing its performance with state-of-the-art methodologies.