ItemArticle

Stroke disease classification from computed tomography images using Inception Harmonic LeNet and wavelet- symmetrically weighted local gradient pattern features

Title
Stroke disease classification from computed tomography images using Inception Harmonic LeNet and wavelet- symmetrically weighted local gradient pattern features
Creator
Tamilarasi, S.; Jingle. I, Diana Jeba; Paul. P, Mano; Thavasilingam, Daniya
Description
Stroke is a leading cause of mortality, making prompt and precise diagnosis essential for effective treatment. Computed Tomography (CT) screening is crucial in identifying stroke types, particularly ischemic and hemorrhagic strokes. Existing automated methods lack the accuracy and consistency required for reliable stroke diagnosis. Therefore, a novel Inception Harmonic LeNet (InHLeNet) approach is devised for stroke disease classification. Initially, CT scans are collected and subjected to preprocessing, which is done using guided filtering and an improved Non-Subsampled Shearlet Transform (NSST) threshold. The filtered images are then segmented using the Dimension fusion U-Net (D-UNet). Subsequently, augmentation is performed by local augmentation and self-augmentation, where local augmentation introduces localized variations within each CT image, and self-augmentation generates feature-guided transformations of lesion regions. Further, the wavelet transforms with Symmetrically Weighted Local Gradient Pattern (Wavelet-SWLGP) features are extracted. Lastly, stroke disease is classified using InHLeNet, which merges InceptionV3Net, LeNet, and Harmonic analysis. The performance of InHLeNet is assessed using several evaluation metrics, including accuracy, True Positive Rate (TPR), True Negative Rate (TNR), and Matthews Correlation Coefficient (MCC). The results attained using the InHLeNet model is accuracy of 96.888%, TNR of 96.381%, MCC of 96.777%, and TPR of 97.988%, with image size, highlighting its effectiveness. 2026 Elsevier Ltd
Source
Computational Biology and Chemistry;Volume;124;Issue;;Article No.;109052;
Date
01-01-2026
Publisher
Elsevier Ltd
Subject
Computed tomography; Deep learning; Inception Harmonic LeNet; Stroke disease classification; Stroke lesion segmentation
Coverage
Tamilarasi S., Department of Electronics and Communication Engineering, Mahendra Institute of Technology, Mahendrapuri, Mallasamudram, Namakkal DT, Tamil Nadu, 637503, India; Jingle. I D.J., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Kanmanike, Bengaluru, 560074, India; Paul. P M., Department of Computer Science and Engineering Artificial Intelligence, Dayananda Sagar Academy of Technology and Management (DSATM), Udaypura, Kanakpura Road, Bengaluru, 560082, India; Thavasilingam D., Department of CSE(AI&ML), GMR Institute of Technology (GMRIT) -Deemed to be University, Andhra Pradesh, Rajam, 532127, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 14769271;
Format
online
Language
English
Type
Article

Citation

Tamilarasi, S.; Jingle. I, Diana Jeba; Paul. P, Mano; Thavasilingam, Daniya, “Stroke disease classification from computed tomography images using Inception Harmonic LeNet and wavelet- symmetrically weighted local gradient pattern features,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/22232.

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