IV3TM: Inception V3 enabled bidirectional long short-term memory network for brain tumor classification

Abstract

A brain tumor is one of the life-threatening neurological conditions affecting millions of people worldwide. Early diagnosis and classification of brain tumor types facilitate prompt treatment, thereby increasing the patient's chances of survival. The advent of Deep Learning methods has significantly improved the field of medical image classification and aids neurologists in brain tumor diagnosis. However, the existing methods using Magnetic Resonance Imaging (MRI) face significant difficulties due to the complexities of brain tumors and the variability in tumor characteristics. Consequently, this research proposes the Inception V3 enabled Bidirectional Long Short Term Memory Network (IV3TM) for Brain Tumor Classification. In the proposed approach, the preprocessing and data augmentation techniques are presented to enhance classification performance. At the pre-processing stage, an iterative weighted-mean Filter approach is utilized to cope with bias field-effect fluctuations, noise, and blurring in input images to enhance the edges. Further, the data augmentation strategy increases the size of the available training data. SqueezeNet is used to segment images for further classification operations. Further, the proposed model combines the strengths of Inception V3 and BiLSTM to learn the sequential dependencies significant for understanding the intricate structural relationships in brain MRI data. The effectiveness of the proposed method is evaluated using several metrics, including specificity, accuracy, precision, F1-score, and sensitivity. Furthermore, the proposed method's error is evaluated using root mean square error (RMSE). Experiments using the Brain Magnetic Resonance Imaging (MRI) images dataset and Figshare brain tumor datasets have shown encouraging results.