Brain tumor detection from images and comparison with transfer learning methods and 3-layer CNN

Abstract

Health is very important for human life. In particular, the health of the brain, which is the executive of the vital resource, is very important. Diagnosis for human health is provided by magnetic resonance imaging (MRI) devices, which help health decision makers in critical organs such as brain health. Images from these devices are a source of big data for artificial intelligence. This big data enables high performance in image processing classification problems, which is a subfield of artificial intelligence. In this study, we aim to classify brain tumors such as glioma, meningioma, and pituitary tumor from brain MR images. Convolutional Neural Network (CNN) and CNN-based inception-V3, EfficientNetB4, VGG19, transfer learning methods were used for classification. F-score, recall, imprinting and accuracy were used to evaluate these models. The best accuracy result was obtained with VGG16 with 98%, while the F-score value of the same transfer learning model was 97%, the Area Under the Curve (AUC) value was 99%, the recall value was 98%, and the precision value was 98%. CNN architecture and CNN-based transfer learning models are very important for human health in early diagnosis and rapid treatment of such diseases.

A hybrid deep learning scheme for MRI-based preliminary multiclassification diagnosis of primary brain tumors

“…Table 2 also presents the results of state-of-the-art models on the CE-MRI dataset. Among them, references ( 23 ) and ( 27 ) represent methods combining radiomics and machine learning, references ( 24 ) ( 25 ), and ( 26 ) represent cutting-edge convolutional neural network (CNN) models, and reference ( 28 ) represents an improved vision transformer model.…”