Brain haemorrhage classification from CT scan images using fine-Tuned transfer learning deep features

被引：0

作者：

Ghosh A. ^{[1
]}

Soni B. ^{[1
]}

Baruah U. ^{[1
]}

机构：

[1] Department of Computer Science and Engineering, National Institute of Technology, Assam, Silchar

来源：

International Journal of Business Intelligence and Data Mining | 2024年 / 24卷 / 02期

关键词：

Binary cross entropy; Brain haemorrhage; DenseNet121; Inception V3; ReLU; Transfer learning; VGG; 16;

D O I：

10.1504/IJBIDM.2024.136411

中图分类号：

学科分类号：

摘要：

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. © 2024 Inderscience Enterprises Ltd.. All rights reserved.

引用

页码：111 / 130

页数：19

共 40 条

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