Detection of Brain Tumor Types Based on FANET Segmentation and Hybrid Squeeze Excitation Network with KNN

Brain tumors are fatal worldwide and are difficult to treat. The process requires time and is prone to error for medical professionals to examine the scans and identify tumor locations. To overcome the limitations, an efficient tumor detection and classification method is necessary for obtaining robust features as well as perform proper disease classification. This paper proposes a multiclass brain tumor classification based on hybrid Squeeze-and-Excitation Networks (SENET) with K-Nearest Neighbour (KNN) Algorithm. To improve poor contrast and raise the quality of the input images, the proposed design gathers and pre-processes the MRI images of brain tumors using the Recursively Separated Exposure Based Sub-Image Histogram Equalization (RS-ESIHE) technique. Following image enhancement, these images are given into FANET segmentation method to segment based on feedback mechanism during training and then using a hybrid SNET with KNN classification technique extracted significant features and classified brain tumor types. Accuracy, F1_score, precision, sensitivity and kappa are some of the metrics used to measure performance, and the results are 97.5%, 94.74%, 95.16%, 94.74% and 96.53%. As a result, the experimental findings for the proposed technique are superior to those of the other existing methods.