Hybrid Archimedes Sine Cosine optimization enabled Deep Learning for multilevel brain tumor classification using MRI images

The most terrible form of cancer caused by uncontrolled and aberrant cell division is the Brain Tumor (BT). The current methodologies are insufficient for precise categorization due to the variety of tumor sizes, forms, and placements in the brain. The main objective of the proposed method is to classify the presence of a brain tumor and also classify its type. It is necessary for effective treatment, and recovery and it improves the survival rate. The Sine cosine Archimedes optimization algorithm (SCAOA) is used in this study to construct a highly successful model for BT classification. Primarily, input Magnetic Resonance Imaging (MRI) brain image is taken from databases, which is later promoted to pre-processing phase. Thereafter, in this pre-processing phase, a Gaussian filter is used to eliminate undesirable noises in the image. The BT segmentation is done by using SegNet, which is tuned by using SCAOA. The proposed SCAOA is established by the combination of Archimedes Optimization Algorithm (AOA) and Sine Cosine Algorithm (SCA). The segmented image samples are then exposed to the feature extraction phase. Features are sent to BT detection and then ShCNN is applied to detect BT with features obtained. If the detected output is found as a tumor, then the BT image is classified as Pituitary tumors, Gliomas, and Meningiomas using DenseNet, which is tuned by using the proposed SCAOA. Finally, SCAOA_DenseNet attained high accuracy of 93.0%, sensitivity of 92.3%, and specificity of 92.0%.