Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Tamilarasi, S.; Jingle. I, Diana Jeba; Paul. P, Mano; Thavasilingam, Daniya Title A name given to the resource Stroke disease classification from computed tomography images using Inception Harmonic LeNet and wavelet- symmetrically weighted local gradient pattern features Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Computational Biology and Chemistry;Volume;124;Issue;;Article No.;109052; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.compbiolchem.2026.109052" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.compbiolchem.2026.109052</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105036020230?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105036020230?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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&amp;ML), GMR Institute of Technology (GMRIT) -Deemed to be University, Andhra Pradesh, Rajam, 532127, India Description An account of the resource 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 Subject The topic of the resource Computed tomography; Deep learning; Inception Harmonic LeNet; Stroke disease classification; Stroke lesion segmentation Publisher An entity responsible for making the resource available Elsevier Ltd Relation A related resource ISSN: 14769271; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online