Work

Mutual information-based radiomic feature selection with SHAP explainability for breast cancer diagnosis

Year: 2024

Type: article

Abstract: Breast cancer is a prevalent concern for women globally, with misdiagnosis potentially leading to detrimental outcomes. Early detection is crucial, often reliant on medical imaging analysis. Digital Breast Tomosynthesis (DBT) is a promising modality, addressing limitations of traditional mammograms. However, diagnosing breast cancer involves subjective visual examination, leading to inaccuracies. Radiomics , applied in various imaging modalities such as MRI , and digital mammography , remains underutilized in DBT. This study introduces a Mutual Information-based Radiomic Feature Selection (MIRFS) framework for DBT breast cancer evaluation followed by SHAP explanations. Selected features were assessed using machine learning algorithms, with Random Forest achieving 92% accuracy in lesion classification. MIRFS demonstrates significant performance improvements, addressing subjectivity and enhancing diagnostic accuracy through explainability. SHAP methodology elucidates feature importance, aiding model interpretation. Compared to deep learning methods, MIRFS outperforms both deep learning and existing machine learning approaches, promising advancements in breast cancer diagnosis and treatment.

Source: Results in Engineering

DOI: 10.1016/j.rineng.2024.103071

Authors: Oladosu Oyebisi Oladimeji, Hamail Ayaz, Ian McLoughlin, Saritha Unnikrishnan

Institution: Atlantic Technological University

Language: English

Topic: Radiomics and Machine Learning in Medical Imaging

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,490