  A definitive diagnosis of a brain tumour is essential for enhancing treatment
success and patient survival. However, it is difficult to manually evaluate
multiple magnetic resonance imaging (MRI) images generated in a clinic.
Therefore, more precise computer-based tumour detection methods are required.
In recent years, many efforts have investigated classical machine learning
methods to automate this process. Deep learning techniques have recently
sparked interest as a means of diagnosing brain tumours more accurately and
robustly. The goal of this study, therefore, is to employ brain MRI images to
distinguish between healthy and unhealthy patients (including tumour tissues).
As a result, an enhanced convolutional neural network is developed in this
paper for accurate brain image classification. The enhanced convolutional
neural network structure is composed of components for feature extraction and
optimal classification. Nonlinear L\'evy Chaotic Moth Flame Optimizer (NLCMFO)
optimizes hyperparameters for training convolutional neural network layers.
Using the BRATS 2015 data set and brain image datasets from Harvard Medical
School, the proposed model is assessed and compared with various optimization
techniques. The optimized CNN model outperforms other models from the
literature by providing 97.4% accuracy, 96.0% sensitivity, 98.6% specificity,
98.4% precision, and 96.6% F1-score, (the mean of the weighted harmonic value
of CNN precision and recall).
