JoWUA

Abstract

A brain tumor is responsible for the highest number of fatalities across the globe. Identifying and diagnosing the tumor correctly at an early stage can significantly improve the chances of survival. Classifying a brain tumor can be aided by factors like type, texture, and location. In this research, we propose a robust technique for detecting sub-brain tumors using an ant colony algorithm coupled with a neural network. To achieve this, we employ an ant colony optimization algorithm (ACO) to eliminate extraneous features extracted from the image, enabling us to find the most effective representation of the image. This, in turn, assists the Neural Network (NN) in the process of classification. Our system involves a series of five steps. Initially, we perform cropping processing as the initial step to eliminate unnecessary background from the original MRI images. This enhances the overall quality of the images, thereby improving the performance of the classification method. In the next step, we conduct image preprocessing to enhance image quality, making it easier for the feature extractor to accurately extract features. The third step involves employing a feature extractor for each image. In the fourth step, we utilize the ant colony optimization algorithm (ACO) to identify the most suitable representation of the image, which further aids the NN in classification. In the fifth and final step, we utilize an NN method to classify the vector obtained from the fourth step (optimization method) to determine the subtype of the brain tumor (normal, glioma, meningioma, and pituitary). Our model's performance is evaluated using the publicly available BT-large-4c dataset, and it surpasses current state-of-the-art methods with exceptional accuracy, attaining a rate of 87.7%. The effectiveness of our approach is particularly evident in maintaining accurate classifications within MRI input images.