Implementation of Deep Learning for Brain Tumor Classification from Magnetic Resonance Imaging

Nur Alfa Husna; Desvita Hendri; Muhammad Farid Wajdi; Ella Silvana Ginting; Chintya Harum Pramesthi

doi:10.57152/predatecs.v3i1.1570

Penulis

Nur Alfa Husna Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia
Desvita Hendri Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia
Muhammad Farid Wajdi University of Sussex, United Kingdom (UK)
Ella Silvana Ginting University Tun Hussein Onn Malaysia, Malaysia
Chintya Harum Pramesthi Yangtze University, China

DOI:

https://doi.org/10.57152/predatecs.v3i1.1570

Kata Kunci:

Brain Tumor, Convolution Neutral Network, Magnetic Resonance Imaging, MobileNetV2, ResNet50

Abstrak

Brain tumours are a medical problem that causes many people to die in the world due to brain cancer. Brain tumours are one of the dangerous types of brain cancer. MRI is well proven in the assessment of brain tumours, although conventional imaging has limitations in evaluating the extent of the tumour. In the field of medicine, there has been an increase in large amounts of data and traditional models cannot manage such data efficiently. So there is a need for medical image analysis that can store and analyse large medical data efficiently. This research will adopt a deep understanding transfer learning approach with four models namely VGG16, VGG19, MobileNetV2 and ResNet50 to classify 2 types of image shapes that detect whether a person has a brain tumour or not using Magnetic Resonance Imaging (MRI) data with Convolution Neutral Network (CNN). The number of datasets used is 4600 MRI images with 2 classes namely Brain Tumour and Health. The hyperparameters used are image size 224x224 pixels, training data ratio 70%, test data 30%, using Adam optimizer, learning rate 0.0001, using batch size 64 and epoch value 50. The best results in this study were obtained by MobileNetV2 architecture with an accuracy of 88.77%.

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