Application of deep transfer learning for automated brain abnormality classification using MR images

By Muhammed Talo, Ulas Baran Baloglu, Ozal Yildirim, U. R. Acharya. Cognitive Systems Research, 2019

456 citations recorded by the source corpus.

Abstract Magnetic resonance imaging (MRI) is the most common imaging technique used to detect abnormal brain tumors. Traditionally, MRI images are analyzed manually by radiologists to detect the abnormal conditions in the brain. Manual interpretation of huge volume of images is time consuming and difficult. Hence, computer-based detection helps in accurate and fast diagnosis. In this study, we proposed an approach that uses deep transfer learning to automatically classify normal and abnormal brain MR images. Convolutional neural network (CNN) based ResNet34 model is used as a deep learning model. We have used current deep learning techniques such as data augmentation, optimal learning rate finder and fine-tuning to train the model. The proposed model achieved 5-fold classification accuracy of 100% on 613 MR images. Our developed system is ready to test on huge database and can assist the radiologists in their daily screening of MR images.

Subfields: Artificial Intelligence, Neurology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/14ab9fe14d9d7ac9f60b2a4dbaa2f4ca6c77ce20

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In Artificial Intelligence

1. Shahab S. Band
2. Kamran Shaukat

Citations in the corpus

• Accurate brain tumor detection using deep convolutional neural network (2022, background): “They are more biased towards classes with a larger number of samples [36][38][56].”
• A Deep Learning Model Based on Concatenation Approach for the Diagnosis of Brain Tumor (2020, background): “Application of deep learning approaches in pattern recognition is noticeable as deep learning is nowadays used in many domains like medical analyzation, object recognition system, and object detection [11]–[18].”
• Deep Transfer Learning Approaches in Performance Analysis of Brain Tumor Classification Using MRI Images (2022, methodology): “It is a noninvasive technique used for accurate image data analysis of human brain tumors in determining the patient’s condition [4].”
• Brain tumor classification using deep CNN features via transfer learning (2019, unknown): “[14] achieved remarkable classification performance with deep transfer learning in their work on brain abnormality classification.”