An improved diagnostic algorithm based on deep learning for ischemic stroke detection in posterior fossa

Muhd Suberi, Anis Azwani (2020) An improved diagnostic algorithm based on deep learning for ischemic stroke detection in posterior fossa. Doctoral thesis, Universiti Tun Hussein Onn Malaysia.


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Ischemic stroke is triggered by an obstruction in the blood vessel of the brain, preventing the blood to flow to the brain tissues region. Solving this is extremely beneficial as Non-enhanced Computed Tomography (NECT) has significant shortcomings for posterior fossa (PF): (i) deficient sensitivity (ii) subtle finding and (iii) radiation exposure. Consequently, PF ischemic stroke lesions are missed at the early stage which increasing the mortality rates. Nowadays, the development of Computer-Aided Diagnosis (CAD) is increasingly becoming an important area in stroke detection. Despite the rapid development of CAD in stroke diagnosis, no studies have been found on stroke detection in PF. Until today, manual delineation of ischemic stroke in PF on NECT demands dealing with a large amount of data, which leads to late prognosis. As the amount of image data generated by NECT is massive, Deep Learning (DL) solutions are among the effective ways to deal with complex and large amount of cross-sectional data. Therefore, a new diagnostic algorithm based on DL is proposed for ischemic stroke detection in PF. The algorithm framework consists of hybrid of improved Xception model and YOLO V2 detector to classify the PF slices with ischemic and localise the infarction in classified slices, respectively. Following that, a CAD system is established by integrating the proposed algorithmic framework. The performance and effectiveness of the proposed algorithmic are evaluated by the comparison with the gold standard provided by the radiologists. The proposed algorithmic framework has shown to be less prone to overfitting and simultaneously improves the detection performance than the original DL model. The results demonstrate that the performance measure of 90.77% has been recorded for detection rate with average processing time of 1.02 to 1.04 seconds per image. The developed algorithm is reported to be reliable to assist the radiologist in ischemic PF diagnosis which is important for future healthcare needs.

Item Type: Thesis (Doctoral)
Subjects: Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions: Faculty of Electrical and Electronic Engineering > Department of Electrical Engineering
Depositing User: Mrs. Sabarina Che Mat
Date Deposited: 03 Feb 2022 02:23
Last Modified: 03 Feb 2022 02:23

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