Deep learning-based robust brain tumor detection via fuzzy C-means and LSTM networks

AI-based segmentation enables longitudinal tracking of tumor growth and changes over time. This helps clinicians monitor disease progression, assess treatment efficacy, and make informed decisions regarding patient management. The combination of fuzzy C-means (FCM) clustering and short-term memory (LSTM) networks enhances the robustness of brain tumor segmentation in MR images by leveraging the complementary strengths of both techniques. FCM clustering provides initial segmentation based on intensity distributions, while LSTM networks refine the segmentation by incorporating contextual information from neighboring regions, making the segmentation more robust to noise and artifacts in the images. To evaluate our segmentation system, we conducted experiments on the BraTS benchmark using different distance metrics. The True positive rate and True negative rate are highest on Mahalanobis, moreover, false positive rate and false negative rate are highest on Euclidean distance, and these results show that the Mahalanobis distance is more suitable for the proposed hybrid approach compared to the Euclidean distance. The experimental findings demonstrate that our own approach can yield comparable results to the ground truth segmentation of brain tumors, the proposed computer-aided diagnosis (CAD) system achieved promising results in the detection and segmentation of brain tumors on MR images, and demonstrated a Dice similarity coefficient of 91.8% and 91.5%. The integration of FCM-clustering and LSTM-networks often leads to improved segmentation accuracy compared to traditional methods. This improvement is due to the ability of FCM to handle intensity variations and spatial characteristics of the tumor while LSTM networks refine the segmentation by incorporating contextual information.