Puzzle sine cosine optimization-based secure communication and brain tumor classification in IoT‐healthcare system

Abstract

A brain tumor (BT) refers to an irregular accumulation of cells within the brain that proliferates uncontrollably, resulting in the formation of a mass. The accurate classification and early detection are important for effective treatment. In previous researches, the BT exhibited diverse features in terms of size, shape, and location. Moreover, the images used for segmentation, which suffered from image noise, low contrast, and shifting intensities within tissues. These issues are overcome by developing an effective method in this paper named Puzzle Sine Cosine Optimization enabled Deep Kronecker Network (PSCO-DKN) for classifying BT in the Internet of Things (IoT) healthcare system. Firstly, an IoT network is simulated, where the IoT device is used to capture the patient’s Magnetic Resonance Imaging (MRI) images. Further, the images are routed to the Base Station (BS) by employing PSCO. The routing is accomplished by contemplating several fitness parameters including delay, energy, and distance. At the BS, the process for BT classification is implemented as follows. Initially, the pre-processing is done by utilizing the median filter. Afterwards, the segmentation process is done by applying Spatial Attention U-Net (SA-Unet). After that, Statistical features and Shape Local Binary Texture (SLBT) are extracted. At last, BT classification is performed by utilizing the DKN, which is structurally optimized by using PSCO developed by the hybridization of Puzzle Optimization Algorithm (POA) and Sine Cosine Algorithm (SCA). Finally, PSCO-DKN attained superior outcomes of True Negative Rate (TNR) at 90.9 %, True Positive Rate (TPR) at 92.6 %, and accuracy at 87.7 %.