What question did this study set out to answer?

This research focuses on enhancing the segmentation and classification of brain tumors using advanced deep learning techniques.

May 25, 2026

Hierarchical Time-Frequency Convolutional Recurrent Network for Enhanced Brain Tumor Segmentation and Classification

Key Points

This research focuses on enhancing the segmentation and classification of brain tumors using advanced deep learning techniques.
Developed a Multi-Scale Time-Frequency Convolutional Recurrent Neural Network for brain tumor classification.
Applied preprocessing with Range–Doppler Matched Filter and segmentation through Sparsity Fuzzy C-Means Clustering.
Utilized Ternary Pattern with Discrete Wavelet Transforms for feature extraction before classification.
Achieved an absolute accuracy of 94.75% on the BraTS dataset.
Demonstrated performance improvements of 18.75%, 26.89%, and 32.57% over existing methods (3D-UNet-DNN-SCBT, SVM–SCBT, BTC–SAGAN–CHA–MRI).

Abstract

Early detection and diagnosis of brain tumors (BT) increases the likelihood of recovery as well as the number of medical options available to the patient. BT may be detected and diagnosed through magnetic resonance imaging (MRI). Nevertheless, in clinical practice, medical practitioners can only detect brain tumors based on time and experience with a large volume of MRI scans. The computer-aided expert systems are being more utilized to assist in diagnosis and medicine therapy recommendations. A lot of deep learning and machine learning models are applied to identify BT. A Multi-Scale Time-Frequency Convolutional Recurrent Neural Network for Segmentation and Classification of BT has been proposed in this paper (MTF–CNN–SCBT). First, the images are fed using the BraTS Dataset. To implement this, pre-processing is done on the input image by Range–Doppler Matched Filter (RDMF); after which, the processing images are further segregated with Sparsity Fuzzy C-Means Clustering (SFCMC). Ternary Pattern with Discrete Wavelet Transforms (TPDWT) is used to do feature extraction after segmentation. MTF–CNN is then fed with the extracted features to successfully classify BT among Glioma, Meningioma, Pituitary, and No Tumor. Overall, MTF–CNN fails to articulate the adjustment of optimization approaches that can identify the best options to achieve proper brain tumor classification. Therefore, the Coati Optimization Algorithm (COA) is able to optimize MTF–CNN that is able to classify the BT correctly. The suggested MTF–CNN–SCBT is then run in MATLAB, where the performance measures such as F1-score, accuracy, precision, sensitivity and comparison are compared. The MTF–CNN–SCBT proposed model has an absolute accuracy of 94.75% on the BraTS dataset. The proposed MTF–CNN–SCBT model achieves an absolute accuracy of 94.75% on the BraTS dataset. The values 18.75%, 26.89%, and 32.57% represent the percentage improvement of the proposed method over the existing 3D-UNet-DNN-SCBT, SVM–SCBT, and BTC–SAGAN–CHA–MRI methods.

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Cite This Study

Karthikeyan et al. (Fri,) studied this question.

synapsesocial.com/papers/6a13e8030e02ee3982d32b3e https://doi.org/https://doi.org/10.4015/s1016237226500043

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