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The significance of fiducial marker detection in neuroimaging cannot be overstated, as these markers serve as vital reference points for accurate spatial alignment during image registration. Our proposed model addresses challenges in consistent marker placement and variability during MRI scanning, ensuring reliable localization for subsequent analysis. This paper introduces an innovative approach to fiducial marker detection in T1-weighted MRI volumes, specifically targeting the Left Preauricular Point (LPA), Right Pre-auricular Point (RPA), and Nasion. The implementation employs a 3D Convolutional Neural Network (CNN) to achieve precise localization of these crucial anatomical landmarks. Operating on a high-performance system our algorithm demonstrated exceptional accuracy and sensitivity using MATLAB R2023a as the primary tool for development and evaluation. Rigorous experiments on a diverse dataset showcased the algorithm’s robust performance. For RPA detection, the model achieved 96.55% accuracy, emphasizing sensitivity (96.78% recall) and precision (96.35%). LPA detection demonstrated an impressive accuracy of 96.88%, with heightened sensitivity (96.95%) and precision (96.83%). The nasion detection process exhibited precise localization, with a Mean Square Error (MSE) of 0.3439 for 36 volume data. These results highlight the algorithm’s potential to enhance accuracy and efficiency in fiducial point detection for improved neuroimaging studies.

[Display omitted] • To work with 2D MR images, it is necessary to choose the slice that best shows the brain tumor. Therefore, 2D deep learning models are not practical. • 3D deep learning models are amenable to practical applications. However, since it contains many slice images without the tumor region, the classification performance is low. • Well-designed customized 3D deep learning-based approaches can perform well in classifying brain tumors. Many machine learning-based studies have been carried out in the literature for the detection of brain tumors using MRI data and most of what has been done in the last 6 years is based on deep learning. Most of them have been designed to work with 2D data. Since many tumor-free slice images are in the models designed in 3D, the classification performance is less than in the 2D models. However, 2D models are unsuitable for practical applications as they use the slice image representing the best tumor image. Therefore, in this study, for brain tumor classification, a new 3 (Attention-Convolutional-LSTM) 3ACL deep learning model that will work with MRI data is presented. Attention, convolutional, and LSTM structures were designed in the same learning architecture in the 3 ACL models, which had an end-to-end learning strategy. Thus, the representation power of the features was increased. In addition, since the proposed model was designed in 3 dimensions, 3D MR images were used directly in the 3ACL model without transforming the 3D MR images into 2D data. Highly representative deep features are extracted from the fully connected layer of the 3ACL model. The feature set is passed to the SVM. Besides, the weighted majority vote technique, which used SVM prediction results conveyed from all slices, improved classification achievement. BRATS 2015 and 2018 datasets were used in this study. For the BRATS 2015 and 2018 datasets, the proposed approach gave 98.90% and 99.29% accuracies, respectively. [ABSTRACT FROM AUTHOR]