  In today's world of health care, brain tumor detection has become common.
However, the manual brain tumor classification approach is time-consuming. So
Deep Convolutional Neural Network (DCNN) is used by many researchers in the
medical field for making accurate diagnoses and aiding in the patient's
treatment. The traditional techniques have problems such as overfitting and the
inability to extract necessary features. To overcome these problems, we
developed the Topological Data Analysis based Improved Persistent Homology
(TDA-IPH) and Convolutional Transfer learning and Visual Recurrent learning
with Elephant Herding Optimization hyper-parameter tuning (CTVR-EHO) models for
brain tumor segmentation and classification. Initially, the Topological Data
Analysis based Improved Persistent Homology is designed to segment the brain
tumor image. Then, from the segmented image, features are extracted using TL
via the AlexNet model and Bidirectional Visual Long Short-Term Memory
(Bi-VLSTM). Next, elephant Herding Optimization (EHO) is used to tune the
hyperparameters of both networks to get an optimal result. Finally, extracted
features are concatenated and classified using the softmax activation layer.
The simulation result of this proposed CTVR-EHO and TDA-IPH method is analyzed
based on precision, accuracy, recall, loss, and F score metrics. When compared
to other existing brain tumor segmentation and classification models, the
proposed CTVR-EHO and TDA-IPH approaches show high accuracy (99.8%), high
recall (99.23%), high precision (99.67%), and high F score (99.59%).
