Multi-level thresholding segmentation for brain tumor detection using optimized deep learning approach - Ewees, Ahmed A.; Ismail, Fatma H.; Labeeb, Nada S.; Gaheen, Marwa A. (Neural computing & applications, 2025)
Medical image segmentation and classification are essential for diagnosing brain tumors, which carry a high risk due to the intricate nature of the brain. In this study, we introduce a multi-level thresholding (MLT) approach to achieve high-quality segmentation. The proposed method utilizes deep learning techniques, employing the weighted means of vectors optimization (INFO) algorithm to determine MLT threshold values. Additionally, we fine-tune the hyperparameters of the long Short-term memory (LSTM) neural network, a powerful deep learning architecture, using the INFO algorithm. This LSTM network is employed for classifying the segmented images and detecting brain tumors. The effectiveness of the proposed deep learning approach is assessed on a Kaggle dataset comprising 253 MRI images, including 98 non-tumor and 155 tumor images. We employ performance metrics, including peak signal-to-noise ratio, structural similarity Index, sensitivity, specificity, and accuracy, to evaluate the quality of the deep learning-based segmentation and classification process. The proposed deep learning-based approach consistently outperforms other methods in terms of producing high-quality segmented tumor images and exhibits enhanced detection and classification performance. These findings highlight the effectiveness of the proposed approach in segmenting and classifying brain tumor images.