In medical image processing, Brain tumor segmentation plays an important role. Early detection of these tumors is highly required to give Treatment of patients. The patient's life chances are improved by the early detection of it. The process of diagnosing the brain tumoursby the physicians is normally carried out using a manual way of segmentation. It is time consuming and a difficult one. To solve these problems, Enhanced Convolutional Neural Networks (ECNN) is proposed with loss function optimization by BAT algorithm for automatic segmentation method. The primary aim is to present optimization based MRIs image segmentation. Small kernels allow the design in a deep architecture. It has a positive consequence with respect to overfitting provided the lesser weights are assigned to the network. Skull stripping and image enhancement algorithms are used for pre-processing. The experimental result shows the better performance while comparing with the existing methods. The compared parameters are precision, recall and accuracy. In future, different selecting schemes can be adopted to improve the accuracy.
Keywords: BAT algorithm; Brain tumor; Early detection; Hybrid CNN; Pre-processing; Segmentation; Standardizing intensity scales.