ENHANCED DENSE CONVOLUTIONAL NETWORK FOR BRAIN TUMOR CLASSIFICATION USING MODIFIED BIRCH SEGMENTATION

The ability and experience of a radiologist is essential for the long and challenging process for detecting brain tumors. With the increasing number of patients, the number of data is maximized, making the fastest processes more expensive and inefficient. Many researchers have looked at many faster, more accurate methods to identify brain tumor classifications. In particular, Deep Learning techniques are detected to create automated systems that can achieve proper identification or segmentation of brain tumors in a shorter period of time. This article suggests a classification of four stages of brain tumors based on Edenet. The specified images are processed in the first stage using GF, and the pre-processed images are segmented using modified birch segmentation in the second stage. After segmentation, features such as shape functions (moments, area, scopes, epsilons, convex) are extracted as a third stage as new variants of texture functions (improvements in LGTP), MBP, and residual functions. In the final phase of the specified model, Edenet-based classification is performed for the classification of brain tumors, and its performance is verified using traditional methods in terms of various performance measurements.