Aneurysms in the brain occur often; the frequency is around 4%. The mass effect is mostly responsible for the symptoms of unruptured aneurysms. The actual risk arises if the aneurysm bursts and results in a subarachnoid haemorrhage, though. The majority of aneurysms are asymptomatic and do not burst, although even minor aneurysms can do so due to the unpredictable growth of aneurysms. Imaging methods including intra-arterial digital subtraction angiography, computed tomography angiography, and magnetic resonance angiography are used to diagnose and track intracranial aneurysms. In this paper, a deep learning approach is proposed to detect and classify the brain aneurysm. Initially, the MRI images are skull stripped and the images augmented and reduce the noise using Kalman filter in the pre-process phase. The segmentation can be done by the firefly optimization algorithm. The segment nodules are classified into three classes by using the spiking neural network. The proposed model achieves the highest level of classification accuracy, which is 99.80%. As a result, when compared to other models currently in use, classification using BSF yields results that are much higher in efficiency and accuracy.