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The early analysis of brain tumors plays a significant role in enhancing treatment and patient survival rates. The accurate diagnosis is critical to creating treatment plans that extend the lifetime of affected individuals. The existing methods have limitations like less accuracy and not able to learn global features. To overcome this issue, Global Self-Attention Module based Convolutional Neural Network (GSAM based CNN) is proposed for brain tumor classification. The Figshare dataset is preprocessed through data augmentation which is utilized to enhance the data size, and the median filter is employed to eliminate noise in an input image. The preprocessed images are given to Adaptive Kernel Fuzzy C Means (AKFCM) with Otsu thresholding for segmentation process. After segmentation, the Sine Cosine Reptile Search Algorithm (SCRSA) is employed for feature selection. Then, the features are provided to GSAM based CNN for brain tumor classification. The proposed model achieves better result on Figshare dataset on the metrics of accuracy, precision, sensitivity, specificity and f1-score, with values of 99.83%, 99.65%, 99.79%, 99.78% and 99.71%, correspondingly, when compared to the existing methods like Comprehensive Learning Elephant Herding Optimization based K-Nearest Neighbor (CLEHO based KNN) and Parallel Deep CNN (PDCNN) with data augmentation. [ABSTRACT FROM AUTHOR]

This paper presents an fMRI signal analysis methodology using geometric mean curve decomposition (GMCD) and mutual information-based voxel selection framework. Previously, the fMRI signal analysis has been conducted using empirical mean curve decomposition (EMCD) model and voxel selection on raw fMRI signal. The erstwhile methodology loses frequency component, while the latter methodology suffers from signal redundancy. Both challenges are addressed by our methodology in which the frequency component is considered by decomposing the raw fMRI signal using geometric mean rather than arithmetic mean and the voxels are selected from EMCD signal using GMCD components, rather than raw fMRI signal. The proposed methodologies are adopted for predicting the neural response. Experimentations are conducted in the openly available fMRI data of six subjects, and comparisons are made with existing decomposition models and voxel selection frameworks. Subsequently, the effect of degree of selected voxels and the selection constraints are analyzed. The comparative results and the analysis demonstrate the superiority and the reliability of the proposed methodology.