期刊
SOFTWARE IMPACTS
卷 13, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.simpa.2022.100340
关键词
Deep learning; Interpretability; Brain tumour; Classification; Grad-CAM; Multimodal
Tumour-Analyser is a web application that classifies brain tumours using MRI and whole slide imaging with DenseNet and ResNet models. It provides interpretability to overcome the black-box nature and lack of transparency in deep learning models.
Tumour-Analyser is a web application that classifies a brain tumour into three classes, namely, lower-grade astrocytoma (A), oligodendroglioma (O), glioblastoma & diffuse astrocytic glioma (G). We use a magnetic resonance imaging (MRI) sequence and a whole slide imaging (WSI) that are classified using DenseNet and ResNet, respectively. The tool interprets the decision-making process of each classification model. Tumour-Analyser provides a viable solution to the less human understandability of existing models due to the inherent black-box nature of deep learning models and less transparency, by applying interpretability.
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