An enhanced deep learning approach for brain cancer MRI images classification using residual networks

By Sarah Ali Abdelaziz Ismael, Ammar Mohammed, H. Hefny. Artif. Intell. Medicine, 2020

346 citations recorded by the source corpus.

Cancer is the second leading cause of death after cardiovascular diseases. Out of all types of cancer, brain cancer has the lowest survival rate. Brain tumors can have different types depending on their shape, texture, and location. Proper diagnosis of the tumor type enables the doctor to make the correct treatment choice and help save the patient's life. There is a high need in the Artificial Intelligence field for a Computer Assisted Diagnosis (CAD) system to assist doctors and radiologists with the diagnosis and classification of tumors. Over recent years, deep learning has shown an optimistic performance in computer vision systems. In this paper, we propose an enhanced approach for classifying brain tumor types using Residual Networks. We evaluate the proposed model on a benchmark dataset containing 3064 MRI images of 3 brain tumor types (Meningiomas, Gliomas, and Pituitary tumors). We have achieved the highest accuracy of 99% outperforming the other previous work on the same dataset.

Subfields: Computer Vision and Pattern Recognition, Neurology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/8099292419d09d5a24db9f5d3bb72c3251a030fc

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In Computer Vision and Pattern Recognition

In Neurology

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Data Augmentation in Classification and Segmentation: A Survey and New Strategies (2023, methodology): “[76] employed data augmentation to solve the problem of insufficient training data and imbalanced classes in the MRI image classification task for brain cancer.”