TY  - JOUR
T1  - A Lightweight Convolutional Neural Network for Classification of Brain Tumors Using Magnetic Resonance Imaging
AU  - Özatılgan, Alper
AU  - Kaya, Mahir
PY  - 2024
DA  - December
Y2  - 2024
DO  - 10.35377/saucis...1518139
JF  - Sakarya University Journal of Computer and Information Sciences
JO  - SAUCIS
PB  - Sakarya University
WT  - DergiPark
SN  - 2636-8129
SP  - 482
EP  - 493
VL  - 7
IS  - 3
LA  - en
AB  - The brain, which controls important vital functions such as vision, hearing and movement, negatively affects our lives when it is sick. Of these diseases, the deadliest is undoubtedly the brain tumor, which can occur in all age groups and can be benign or malignant. Therefore, early diagnosis and prognosis are very important. Magnetic Resonance (MR) images are used for the detection and treatment of brain tumor types. Successful results in the detection of diseases from medical images with Convolutional Neural Networks (CNN) depend on the optimum creation of the number of layers and other hyper-parameters. In this study, we propose a CNN model that will achieve the highest accuracy with the least number of layers. A public data set consisting of 4 different classes (Meningioma, Glioma, Pituitary and Normal) obtained for use in the training of CNN models was trained and tested with 50 different deep learning models designed, and a better result was obtained when compared with the existing studies in the literature with 99.47% accuracy and 99.44% F1 score values.
KW  - Lightweight model
KW  - Brain tumor classification
KW  - Convolutional neural network
KW  - Deep learning
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