Brain tumors pose a significant health risk and require immediate attention. Despite progress, accurately classifying these tumors remains challenging due to their location, shape, and size variability. This has led to exploring deep learning and machine learning in biomedical imaging, particularly in processing and analyzing Magnetic Resonance Imaging (MRI) data. This study compared a newly developed Convolutional Neural Network model to pre‐trained models using transfer learning, focusing on a comprehensive comparison involving VGG‐16, ResNet‐50, AlexNet, and Inception‐v3. VGG‐16 model outperformed all other models with 95.52% test accuracy, 99.87% training accuracy, and 0.2348 validation loss. ResNet‐50 model got 93.31% test accuracy, 98.78% training accuracy, and 0.6327 validation loss. The CNN model has a 0.2960 validation loss, 92.59% test accuracy, and 98.11% training accuracy. The worst model seemed to be Inception‐v3, with 89.40% test accuracy, 97.89% training accuracy, and 0.4418 validation loss. This approach facilitates deep‐learning researchers in identifying and categorizing brain cancers by comparing recent papers and assessing deep‐learning methodologies.

The present work deals with the investigation of the time‐fractional Klein–Gordon (K‐G) model, which has great importance in theoretical physics with applications in various fields, including quantum mechanics and field theory. The main motivation of this work is to analyze modulation instability and soliton solution of the time‐fractional K‐G model. Comparative studies are investigated by β‐fraction derivative and M‐fractional derivative. For this purpose, we used unified and advanced exp(−ϕ(ξ))‐expansion approaches that are highly important tools to solve the fractional model and are used to create nonlinear wave pattern (both solitary and periodic wave) solutions for the time‐fractional K‐G model. For the special values of constraints, the periodic waves, lumps with cross‐periodic waves, periodic rogue waves, singular soliton, bright bell shape, dark bell shape, kink and antikink shape, and periodic wave behaviors are some of the outcomes attained from the obtained analytic solutions. The acquired results will be useful in comprehending the time‐fractional K‐G model’s dynamical framework concerning associated physical events. By giving specific values to the fractional parameters, graphs are created to compare the fractional effects for the β‐fraction derivative and M‐fractional derivative. Additionally, the modulation instability spectrum is expressed utilizing a linear analysis technique, and the modulation instability bands are shown to be influenced by the third‐order dispersion. The findings indicate that the modulation instability disappears for negative values of the fourth order in a typical dispersion regime. Consequently, it was shown that the techniques mentioned previously could be an effective tool to generate unique, precise soliton solutions for numerous uses, which are crucial to theoretical physics. This work provided the effect of the recently updated two fraction forms, and in the future, we will integrate the space–time M‐fractional form of the governing model by using the extended form of the Kudryashov method. Maple 18 is utilized as the simulation tool.