Title: Brain haemorrhage classification from CT scan images using fine-tuned transfer learning deep features

Authors: Arpita Ghosh; Badal Soni; Ujwala Baruah

Addresses: Department of Computer Science and Engineering, National Institute of Technology Silchar, Assam, India ' Department of Computer Science and Engineering, National Institute of Technology Silchar, Assam, India ' Department of Computer Science and Engineering, National Institute of Technology Silchar, Assam, India

Abstract: Classification of brain haemorrhage is a challenging task that needs to be solved to help advance medical treatment. Recently, it has been observed that efficient deep learning architectures have been developed to detect such bleeding accurately. The proposed system includes two different transfer learning strategies to train and fine-tune ImageNet pre-trained state-of-the-art architecture such as that VGG 16, Inception V3 and DenseNet121. The evaluation metrics have been calculated based on the performance analysis of the employed networks. Experimental results show that the modified fine-tuned Inception V3 performed well and achieved the highest test accuracy.

Keywords: transfer learning? VGG 16? Inception V3? DenseNet121? brain haemorrhage? ReLU? binary cross entropy.

DOI: 10.1504/IJBIDM.2024.136411

International Journal of Business Intelligence and Data Mining, 2024 Vol.24 No.2, pp.111 - 130

Received: 07 Oct 2021
Accepted: 12 Feb 2022
Published online: 01 Feb 2024 *

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