Enhancing Brain Tumor Detection Classification by Computational Intelligence

The need for early detection and treatment makes brain tumour possibly the most critical illness for which radiologists and neuro subject-matter professionals must be skilled. In recent years, computational intelligence (CI) and machine learning (ML) methods have evolved into effective smart instruments for the early detection of brain tumors. Computer-aided diagnosis (CAD) methods primarily rely on various CI approaches in all phases of brain image analysis to build a smart consulting platform that can support radiologists in the detection and identification of brain tumors by offering a second option. Detection and classification of a brain tumour require time and are mainly reliant on the radiologist's skills and expertise. As the patient population has expanded, so has the quantity of data that should be processed, making previous techniques both expensive and inefficient. Deep Learning (DL) methods have lately gained popularity in the field of CI for creating automated methods that can quickly and effectively detect and classify brain tumors. This study suggested a CNN-based brain cancer classification model (BCM-CNN) since DL makes it possible to use a pre-trained CNN framework for medical images, particularly for classifying brain tumors. After hyperparameter adjustment, an Inception-ResnetV2 training framework is developed. To enhance brain tumor detection, the approach uses widely employed pre-trained methods (Inception-ResnetV2), and its outcome is a binary 0 or 1 (Normal-0, Tumor-1). The empirical outcomes demonstrate that the BCM-CNN classifier outperformed the existing methods since the performance of CNN is enhanced through the CNN optimization's hyperparameters. Using the Task 1 dataset of BRaTS 2021, the BCM-CNN obtained 99.98% accuracy.