Three‐class classification of brain magnetic resonance images using average‐pooling convolutional neural network. Issue 3 (15th February 2021)

• Record Type: Journal Article
• Authors: Kakarla, Jagadeesh; Isunuri, Bala Venkateswarlu; Doppalapudi, Krishna Sai; Bylapudi, Karthik Satya Raghuram
• Is Part Of: International journal of imaging systems and technology. Volume 31:Issue 3(2021)
• Page Start: 1731
• Page End: 1740
• Publication Date: 2021-02-15
• Subjects: average pooling -- brain tumor classification -- brain tumor dataset -- convolutional neural network -- three‐class classification; Imaging systems -- Periodicals; Image processing -- Periodicals; 621.367
• DOI: 10.1002/ima.22554
• Languages: English
• ISSNs: 0899-9457

Abstract

Abstract: Brain tumor image classification is one of the predominant tasks of brain image processing. The three‐class brain tumor classification becomes a trivial task for researchers as each tumor exhibit distinct characteristics. Existing classification models use deep neural networks and suffer from high computational cost. We have proposed an eight‐layer average‐pooling convolutional neural network to address three‐class brain tumor classification. The proposed model uses three convolution blocks along with a dense layer and a softmax layer. We have utilized N‐adam optimizer with a sparse‐categorical cross‐entropy loss function to improve the learning rate. The proposed model has been evaluated using a dataset consists of 3064 brain tumor magnetic resonance images. The proposed model outperforms state‐of‐the‐art models with 97.42% accuracy and takes lesser computation time than its competitive models.