AI Approaches in Computer-Aided Diagnosis and Recognition of Neoplastic Changes in MRI Brain Images

• Published in: Applied sciences Vol. 12; no. 23; p. 11880
• Main Authors: Kluk, Jakub, Ogiela, Marek R.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2022
• Subjects: Algorithms; Artificial intelligence; artificial neural networks; Brain cancer; Brain tumors; cancerous diseases; Classification; computer vision; convolutional networks; Datasets; Deep learning; Diagnosis; Machine learning; Magnetic resonance imaging; Medical imaging; Methods; Nervous system; Neural networks; Signs and symptoms; Tumors; vision transformer
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app122311880

Advanced diagnosis systems provide doctors with an abundance of high-quality data, which allows for diagnosing dangerous diseases, such as brain cancers. Unfortunately, humans flooded with such plentiful information might overlook tumor symptoms. Hence, diagnostical devices are becoming more commonly combined with software systems, enhancing the decisioning process. This work picks up the subject of designing a neural network based system that allows for automatic brain tumor diagnosis from MRI images and points out important areas. The application intends to speed up the diagnosis and lower the risk of slipping up on a neoplastic lesion. The study based on two types of neural networks, Convolutional Neural Networks and Vision Transformers, aimed to assess the capabilities of the innovative ViT and its possible future evolution compared with well-known CNNs. The research reveals a tumor recognition rate as high as 90% with both architectures, while the Vision Transformer turned out to be easier to train and provided more detailed decision reasoning. The results show that computer-aided diagnosis and ViTs might be a significant part of modern medicine development in IoT and healthcare systems.