Development of an unsupervised pseudo-deep approach for brain tumor detection in magnetic resonance images

Brain tumor detection has long been a significant and challenging issue in medical research. In this study, we propose an unsupervised pseudo-deep method for detecting brain tumors in Magnetic Resonance (MR) images. Our approach utilizes iterative spectral Co-Clustering and Fuzzy C-means techniques to achieve precise segmentation results. In each iteration, the algorithm recognizes the tumor block from its input image using spectral Co-Clustering. The selected block from the previous iteration serves as the input for the subsequent iteration. In the final iteration, Fuzzy C-Means is applied to the last selected block to extract the tumor, and scheme is proposed to determine the location of the tumor in the original image. Through this iterative pseudodeep method, our approach exhibits a layered-like structure, resembling deep learning architectures, within the context of unsupervised methods. We evaluated our approach using the BraTS2020 and BraTS2021 datasets and assessed its performance using key metrics. Our method achieved values of 99.12% and 81.42% for accuracy and dice coefficient on BraTS2020 and also 99.21% and 82.03% on the BraTS2021 dataset, surpassing the performance of existing unsupervised methods reported in the literature. Moreover, our approach demonstrates notably superior performance when dealing with complex images. The proposed method offers a unique perspective in the application of unsupervised techniques for brain tumor detection using a pseudo-deep structure.