Akademik Veri Yönetim Sistemi
Expert Systems with Applications, cilt.241, 2024 (SCI-Expanded)
Early and accurate detection and classification of glioma types is of paramount importance in determining treatment planning and increasing the survival rate of patients. At present, diagnosis in neuropathology is based on molecular and histological characteristic information provided with microscopic visual examinations of biopsies. However, the traditional method is not only laborious, and time-consuming but also needs experience. Furthermore, the subjective diagnosis causes inter-/intra variability and late or inaccurate diagnosis. To overcome those issues by automated methods, Convolutional Neural Networks (CNNs) and, more recently, transformer-based models have been used. However, they have their own drawbacks. For instance, CNNs ignore global information by focusing on pixel-wise information, although they are good at the extraction of local characteristic features using several convolution and pooling layers. Vision transformers are problematic in the extraction of details and local features, although they are good in the extraction of global features using global receptive fields in the early layers. Therefore, in this work, their advantages have been utilized in designing a new architecture to classify gliomas. Obtaining high performance from the proposed architecture has been achieved by (i) using a combined version of CNN and transformer stages, and (ii) integrating effectively designed feature-combining and smart-joining modules appropriately. Experiments have indicated the effectiveness of the proposed approach in classifying four glioma subtypes from histopathological images in terms of several evaluation metrics (i.e., accuracy (96.75%), recall (97.00%), precision (96.75%), F1-score (96.80%)). Comparative evaluations of the performances of the state-of-the-art techniques have shown better capability of the proposed approach.