Adaptive Coronavirus Mask Protection Algorithm Enabled Deep Learning for Brain Tumor Detection and Classification

Brain tumor (BT) is a dangerous disease and the process of detecting BT is difficult. Early detection of this disease plays a critical role in protecting the life of humans. Hence, this paper introduced an Adaptive Coronavirus Mask Protection Algorithm (ACMPA)-enabled deep learning technique for detecting and categorizing BT. First, the Magnetic Resonance Image (MRI) brain images are pre-processed using Kalman filtering. After that, BT is segmented by utilizing LadderNet, and the features are extracted which include mean, tumor size, entropy, kurtosis, variance, Haralick texture features, namely Angular second moment (ASM), contrast and Spider Local Image Feature (SLIF). Following this, BT is detected by the Deep Kronecker Network (DKN), where BT is categorized into normal or abnormal. If the detection is abnormal, then BT is categorized into Meningiomas, Gliomas, and pituitary tumors using DKN, which is tuned by the ACMPA. The ACMPA is obtained by integrating the Adaptive concept and Coronavirus Mask Protection Algorithm (CMPA). Furthermore, the proposed ACMPA_DKN acquired the value of accuracy to 90.4%, and obtained the value of TPR and TNR to 91.6% and 92.5%.