Year: 2022
Type: article
Abstract: Now a day’s brain tumor detection is an important matter in computer aided diagnosis for medical applications. The classification process is important and plays a vital role to diagnosis the brain tumors. The existing method concentrates on brain tumor detection, features extraction and classification. Though the traditional classification Algorithms is suffered from less accuracy and takes more time for execution. To enhance the premature stage brain tumor detection and classification, the main intension of this paper is to extract features, classification of brain tumor using VGG-16 Architecture. By using this method we achieved maximum accuracy of 99%, minimum loss, less time for execution,F1 score, sensitivity etc., as compare to CNN, for this method execution we collected the data from kaggle dataset, Brain MRI images of 2065 patients, out of which 1445 images are cancerous and 620 images are non cancerous.
Authors: A. Vinisha, Ravi Boda
Institution: Koneru Lakshmaiah Education Foundation
Language: English
Subfield: Neurology
Field: Neuroscience
Domain: Life Sciences
Open Access status: diamond
OpenAlex ID: W4296175915