An optimal segmentation with deep learning based inception network model for intracranial hemorrhage diagnosis

• Published in: Neural computing & applications Vol. 33; no. 20; pp. 13831 - 13843
• Main Authors: Mansour, Romany F., Aljehane, Nojood O.
• Format: Journal Article
• Language: English
• Published: London Springer London 01.10.2021; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Computational Biology/Bioinformatics; Computational Science and Engineering; Computed tomography; Computer Science; Data Mining and Knowledge Discovery; Deep learning; Diagnosis; Feature extraction; Format; Head injuries; Hemorrhage; Image classification; Image compression; Image Processing and Computer Vision; Image segmentation; Machine learning; Medical imaging; Multilayer perceptrons; Optimization; Original Article; Probability and Statistics in Computer Science; Traumatic brain injury; Deep learning; EHO algorithm; Inception network; Kapur’s thresholding; Intracranial hemorrhages
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-021-06020-8

Traumatic Brain Injury (TBI) leads to intracranial hemorrhages (ICH), which is a severe illness resulted in death if it is not properly diagnosed and treated in the earlier stage. Presently, computer tomography (CT) images are widely used by radiologists to identify and locate the regions of ICH. But it is a tedious task and mainly depends on the professional radiologists. This paper develops new deep learning (DL)-based ICH diagnosis and classification (DL-ICH) model using optimal image segmentation with Inception Network. The proposed DL-ICH model involves preprocessing, segmentation, feature extraction, and classification. Firstly, the input data undergo format conversion where the NIfTI files are converted into JPEG format. Next, Kapur's thresholding with an elephant herd optimization (EHO) algorithm called KT-EHO is employed for image segmentation. Then, DL based Inception v4 network is applied as a feature extractor to extract a useful set of features and a multilayer perceptron (MLP) is used for classification. An extensive set of simulations takes place to ensure the effective diagnostic performance of the DL-ICH model and the results are investigated under diverse dimensions. The experimental results achieved better accuracy rate.