Optimized attention-based lightweight CNN using particle swarm optimization for brain tumor classification

Timely detection of brain tumors is crucial for developing effective treatment strategies and improving the overall well-being of patients. We introduced an innovative approach in this work for classifying and diagnosing brain tumors with the help of magnetic resonance imaging and a deep learning model. In the proposed method, various attention mechanisms that allow the model to assign different degrees of importance to certain inputs are used, and their performances are compared. Additionally, the Particle Swarm Optimization algorithm is employed to find the optimal hyperparameter values for the Convolutional Neural Network model that incorporates attention mechanisms. A four-class public dataset from the Kaggle website was used to evaluate the effectiveness of the proposed method. A maximum accuracy of 99%, precision of 99.02%, recall of 99%, and F1 score of 99.01% were obtained on the Kaggle test dataset. In addition, to assess the model’s adaptability and robustness, salt-and-pepper noise was introduced to the same test dataset at various rates, and the models’ performance was re-evaluated. A maximum accuracy of 97.78% was obtained on the test data set with 1% noise, 95.04% on the test data set with 2% noise, and 88.10% on the test data set with 3% noise. When the results obtained are analyzed, it is concluded that the proposed model can be successfully used in brain tumor classification and can assist doctors in making diagnostic decisions.