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Journal of Information and Optimization Sciences

(Since 1980)
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
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Journal of Information and Optimization Sciences
Open Access Research Article

A hybrid deep unified model on diverse datasets

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pp. 1557–1566Vol. 47Issue 4April 2026DOI: 10.47974/JIOS-2242XML
Received:
01 Dec 2025
Published Online:
04 Apr 2026
Article type:
Research Article
Language:
EN
Article no.:
JIOS-2242
Pages:
1557–1566

Abstract

Deep learning is very efficient in image classification, but the classical feature extraction algorithms are understandable and robust. The categorization of images serves as a foundation for diagnosing, planning treatment, and analyzing images. Powerful algorithms handle various types of data effectively, emphasizing the importance of robust and understandable methods in the field of image processing. The HDUM designed in the present study combines ResNet, DenseNet, and InceptionNet deep learning methods. The first step is in image classification, which identifies high-level features in the raw images by picking up local and global patterns. Furthermore, HDUM uses classical techniques of feature extraction to obtain auxiliary information. Additionally, the fused features are given to a classifier to generate an effective final classification. The model constitutes a unique attention mechanism whereby resources are allocated dynamically with attention depend on salient parts of images to be processed. The single design of HDUM that incorporates different types of data sets and at the same time guarantees interpretability and performance makes it stand out from the rest. Based on various research studies conducted on standard datasets, HDUM is the most suitable technique regarding accuracy in classification, ease of use, and data heterogeneity. This comparison attributes HDUM to CNNs, ResNet, DenseNet, InceptionNet, VGG16, and the hybrid CNN model, showing superior results in various performance metrics. HDUM excels in practical applications due to its superior generalizability and resilience to input data changes. This study improves the classification of pictures using a combination of deep learning and classical feature extraction methods by exploiting the beneficial synergistic im-pact of the implemented strategies.

Keywords

Image classificationCNNResNetDenseNetInceptionNetHybrid deep unified model

Subject Classifications

Primarily 62M4562M40Secondary 54H3065D1868U10

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