BAT Algorithm With fuzzy C-Ordered Means (BAFCOM) Clustering Segmentation and Enhanced Capsule Networks (ECN) for Brain Cancer MRI Images Classification

Abstract

Cancer is a second foremost life-threatening disease next to cardiovascular diseases. In particular, brain cancer holds the least rate of survival than all other cancer types. The categorization of a brain tumor depends upon the various factors such as texture, shape and location. The medical experts have preferred the appropriate treatment to the patients, based on the accurate identification of tumor type. The process of segmenting the Magnetic Resonance Imaging (MRI) has high complicacy during the analysis of brain tumor, owing to its variable shape, location, size, and texture. The physicians and radiologists can easily detect and categorize the tumors if there exists a system by combining Computer Assisted Diagnosis (CAD) as well as Artificial Intelligence (AI). An approach of automated segmentation has proposed in this paper, which enables the segmentation of tumor out of MRI images, besides enhances the efficiency of segmentation and classification. The initial functions of this approach include preprocessing and segmentation processes for segmenting tumor or tissue of benign and malignant by expanding a range of data and clustering. A modern learning-based approach has suggested in this study, in order to process the automated segmentation in multimodal MRI images to identify brain tumor, hence the clustering algorithm of Bat Algorithm with Fuzzy C-Ordered Means (BAFCOM) has recommended segmenting the tumor. The Bat Algorithm calculates the initial centroids and distance within the pixels in the clustering algorithm of BAFCOM, which also acquires the tumor through determining the distance among tumor Region of Interest (RoI) and non-tumor RoI. Afterwards, the MRI image has analyzed by the Enhanced Capsule Networks (ECN) method to categorize it as normal and brain tumor. Ultimately, the algorithm of ECN has assessed the performance of proposed approach by distinguishing the two categories of the tumor over MRI images, besides the suggested ECN classifier has assessed by the measurement factors of accuracy, precision, recall, and F1-score. In addition, the genetic algorithm has applied to process the automatic tumor stage classification, which in turn classification accuracy enhanced.INDEX TERMS Machine learning, enhanced capsule networks (ECN), brain tumor, bat algorithm with fuzzy c-ordered means (BAFCOM), magnetic resonance imaging (MRI) images.

Computational Intelligence and Metaheuristic Techniques for Brain Tumor Detection through IoMT-Enabled MRI Devices

“…Each virtual bat flies in a unique direction and speed, with a unique pitch, wavelength, and volume. As it searches for and locates prey, it alters the frequency, loudness, and rate of pulse emission [54]. It is computed by using Equations ( 29)- (31).…”

Intuitionistic Fuzzy c-Ordered Means Clustering Algorithm

“…Then, each data-item is assigned a membership value while exploiting norm function in MATLAB software 50 times. Further, the non-membership values and hesitancy values are deduced using (35) and (36). Furthermore, the numbers of outliers located at point (0, 1) T are varying in step size of 5 units from 0 to 30.…”

“…It may be a line of action to improve IFCOM for image segmentation. Some of the well known algorithms for image segmentation are proposed in [36] and [26].…”

An improved framework for brain tumor analysis using MRI based on YOLOv2 and convolutional neural network

“…Sauwen et al [48] proposed different methodologies to analyze tumor segmentation results [26]. Goswami and Bhaiya [6] presented a hybrid framework consisting of fuzzy logic and neural network for tumor detection and classification [51]. A semiautomatic method with spatial features is applied for tumor detection [24].…”

“…The pre-trained AlexNet has been utilized for glioma detection for the prediction of patient's survival rate [53]. CNN model is trained on brain imaging data and classified input data into five classes, such as multiform glioma, astrocytoma, shapeless tumor, normal brain tissues, and oligodendroglioma [6]. M-net segmentation model has been utilized for features extraction and fed into the pre-trained VGG-16 for the classification of three different types of the tumor [63].…”