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Accurate classification of brain tumors from MRI is critical for effective diagnosis and treatment. In this study, we introduce Trans-EffNet, a hybrid model combining pre-trained EfficientNet architectures with a transformer encoder to enhance brain tumor classification accuracy. By leveraging EfficientNet's deep CNN capabilities for localized feature extraction and the transformer encoder for capturing global contextual relationships, our model improves the identification of intricate tumor characteristics. Fine-tuned with ImageNet-derived weights and utilizing extensive data augmentation, Trans-EffNet was validated on both multi-class and binary datasets. Trans-EffNetB1 achieved 99.49 % accuracy on the multi-class dataset, while Trans-EffNetB2 recorded 99.83 % accuracy on the binary dataset, with perfect precision, recall, and F1-Score. These results underscore Trans-EffNet's robustness and potential as a significant advancement in brain tumor detection and classification.
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<jats:p> This work compares the performance of different algorithms — quantum Fourier transform, Gaussian–Newton method, hyperfast, metropolis-adjusted Langevin algorithm, and nonparametric classification and regression trees — for the classification of fetal health states from FHR signals. In the conducted research, the effectiveness of each algorithm was measured using confusion matrices, which gave information about class precision, recall, and total accuracy in three classes: Normal, Suspect, and Pathological. The QFT algorithm gives an overall accuracy of 90%, where it is highly reliable in recognizing Normal (94% F1-score) and Pathological states (91% F1-score), but performs poorly regarding the Suspect cases, at 58% F1-score. On the other hand, using the GNM method gives an accuracy of 88%, whereby it performed well on Normal cases, at 93% F1-score, and poor performance with Suspect, at 50% F1-score, and Pathological classifications, at 82% F1-score. The hyperfast algorithm yielded an accuracy of 89%, thus performing well on Normal classifications with an F1-score of 93%, but less well on the Suspect states with an F1-score of 56%. The MALA algorithm outperformed all other algorithms tested in this study, giving an overall accuracy of 91% and adequately classifying Normal, Suspect, and Pathological states with corresponding F1-scores of 94%, 63%, and 90%, respectively; therefore, the algorithm is quite robust and reliable for fetal health monitoring. The NCART algorithm achieved an accuracy of 89%, thus showing great capability for classification in Normal cases with 94% F1-score and in Pathological cases with 88% F1-score; this is moderate for Suspect cases with 53% F1-score. Overall, while all algorithms exhibit potential for fetal health classification, MALA stands out as the most effective, offering reliable classification across all health states. These findings highlight the need for further refinement, particularly in enhancing the detection of Suspect conditions, to ensure comprehensive and accurate fetal health monitoring. </jats:p>
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