Brain Tumor Classification Using Convolutional Neural Networks

By J. Seetha, S. Raja. Biomedical & Pharmacology Journal, 2018

353 citations recorded by the source corpus.

The brain tumors, are the most common and aggressive disease, leading to a very short life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the quality of life of patients. Generally, various image techniques such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and ultrasound image are used to evaluate the tumor in a brain, lung, liver, breast, prostate…etc. Especially, in this work MRI images are used to diagnose tumor in the brain. However the huge amount of data generated by MRI scan thwarts manual classification of tumor vs non-tumor in a particular time. But it having some limitation (i.e) accurate quantitative measurements is provided for limited number of images. Hence trusted and automatic classification scheme are essential to prevent the death rate of human. The automatic brain tumor classification is very challenging task in large spatial and structural variability of surrounding region of brain tumor. In this work, automatic brain tumor detection is proposed by using Convolutional Neural Networks (CNN) classification. The deeper architecture design is performed by using small kernels. The weight of the neuron is given as small. Experimental results show that the CNN archives rate of 97.5% accuracy with low complexity and compared with the all other state of arts methods.

Subfields: Computer Vision and Pattern Recognition, Neurology

View Paper: https://www.semanticscholar.org/paper/79461362af5899a45b420993af9ad9025dc6aedf

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. M. O. Khairandish
2. N. Z. Jhanjhi
3. Meenakshi Sharma
4. Amjad Rehman
5. Jyotir Moy Chatterjee

In Neurology

1. M. O. Khairandish
2. N. Z. Jhanjhi
3. Meenakshi Sharma
4. Jyotir Moy Chatterjee

Citations in the corpus

• Brain Tumor Detection Using Convolutional Neural Network (2019, neutral): “In Seetha et al. [17], researchers got 83.0% accuracy using SVM based classification and 97.5% accuracy using CNN.”
• A Hybrid CNN-SVM Threshold Segmentation Approach for Tumor Detection and Classification of MRI Brain Images (2021, methodology): “6 [23] Convolutional Neural Networks (CNN) 97.”
• Multimodal Brain Tumor Classification Using Deep Learning and Robust Feature Selection: A Machine Learning Application for Radiologists (2020, background): “In [25], the authors presented a CNN-based scheme for the classification of brain tumors.”