| Abstrakt: |
In the rapidly evolving landscape of medical imaging, our proposed work presents an innovative and efficient approach to brain tumor detection through advanced deep learning methodologies. Central to our methodology is the strategic utilization of pre-trained weights from the formidable MBConv-Finetuned-B0 model, initially honed on the expansive ImageNet dataset, providing a foundation rich in general visual knowledge. Our subsequent fine-tuning process targets specific layers relevant to brain tumor detection, introducing two distinct convolutional layers, MBConv 6, 55, and MBConv 6, 30, meticulously added to the MBConv-Finetuned-B0 base model. These layers are intricately designed to extract and refine features specific to brain tumors, ensuring a nuanced understanding of pathology and enhancing the model's discrimination and accuracy. The flexibility of our methodology is exemplified by the thoughtful consideration of two fine-tuning options: one that adjusts all layers of the model and another that selectively fine-tunes only the proposed layers. We conduct a detailed comparative analysis, including homogeneity and median feature values, placing our work in direct comparison with established techniques such as Ensemble Transfer Learning and Quantum Variational Classifier (ETL & QVC), Ultra-Light Deep Learning (ULDL) Model, Deep Convolutional Neural Network (DCNN), and Deep Learning and Image Processing (DLIP). The results showcase the model's proficiency, achieving an accuracy of 94%, precision of 84%, recall of 92%, F1 score of 88%, and an AUC-ROC of 96%. Notably, our model demonstrates superior performance in terms of homogeneity (vE Homogeneity: 0.93, vN Homogeneity: 0.91, Enhancement Homogeneity: 0.97) and median feature values (Median vE Feature Value: 0.82, Median vN Feature Value: 0.87, Median Enhancement Feature Value: 0.80), providing a comprehensive understanding of its effectiveness in capturing subtle nuances in brain tumor images. [ABSTRACT FROM AUTHOR] |
