Precise Classification of Brain Magnetic Resonance Imaging (MRIs) using COOT optimization

Abstract

This research demonstrated a hybrid approach that combined machine learning algorithms with expert medical knowledge to achieve precise classification of brain MRIs. By harnessing the capabilities of artificial intelligence, these computer-aided methods have the potential to assist healthcare professionals in making well-informed decisions, leading to better patient outcomes. One of the primary advantages of computer-aided diagnosis is its ability to rapidly and efficiently analyze large volumes of medical data. This technology can process numerous MRI scans in a fraction of the time it would take for a human expert to review them individually. This not only saves valuable time but also minimizes the risk of human error or oversight. Furthermore, computer-aided diagnosis systems can detect subtle patterns or abnormalities that might elude even the most experienced radiologists. By analyzing thousands of MRI images and comparing them with established patterns associated with different diseases, these systems can identify potential indicators that might be missed by human observers alone. This heightened accuracy can significantly enhance early detection rates and facilitate timely intervention, ultimately saving lives. The proposed classification system utilized GLCM to extract a comprehensive set of features that capture the spatial relationships between pixels in an image. These features effectively represent significant patterns and structures in the image, thereby facilitating accurate classification. Additionally, the approach incorporated COOT optimization, an optimization technique, to enhance the feature extraction process beyond mere feature selection. By leveraging a cooperative and coordinated framework involving multiple agents, the variables were fine-tuned, leading to improved quality in the extracted features. Consequently, this approach achieved superior feature extraction results, yielding better and more precise features for a given image. Subsequently, CNNs were employed for image classification by training a deep neural network on a labeled dataset and optimizing its parameters. Leveraging the learned patterns and features from the training data, the trained model exhibited the ability to classify new images, resulting in enhanced accuracy for MRI classification