INCNet: Brain Tumor Detection using Inception and Optimization Techniques

Brain tumors are one of the most serious disorders that may affect humans. The early detection of a brain tumor is critical for its treatment. The precise locations of the tumor and its spread area have been discovered. A tumor is detected by observing that it has a greater intensity in its location. For the sake of this implementation, an MRI scan image is regarded to be the system's input.Most of the existing methods have suffered from the inability to detect tumors of small size and their inability to correctly identify abnormal tissue growth in the brain. To address these limitations, a method has been proposed that includes image pre-processing, post-processing, and inception-optimization are the three primary processes in the implementation of tumor detection. Image enhancement techniques such as noise reduction, high-pass filtering, median filtering, and de-blurring are employed at the preprocessing stage. Post-processing includes the use of operations such as thresholding, segmentation using the watershed technique, and morphological operations. The implemented system is trained at various angles using the inception and optimization approach, and the precise location of the tumor is determined. The performance of the proposed method is tested in terms of precision, recall, tumor size, and accuracy. The proposed method is superior to existing approaches. The entire simulation was conducted using MATLAB R2021a.