Cross-Domain Image Classification via Multimodal Augmentations and Bacterial Foraging Optimization

Pushpendra Singh, Arun Kumar Singh, Pushpa Choudhary, Mahesh Kumar Singh, Ahmad Taher Azar, Zeeshan Haider and Mohamed Tounsi
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Pushpendra Singh: Department of Computer Science and Engineering, SRM Institute of Science and Technology, India
Arun Kumar Singh: Computer Science and Engineering, Greater Noida Institute of Technology, Greater Noida, India
Pushpa Choudhary: Computer Science and Engineering Department, Galgotias University, Greater Noida, India
Mahesh Kumar Singh: Department of Computer Science and Engineering, Dronacharya Group of Institutions, Greater Noida, India
Ahmad Taher Azar: College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia & Automated Systems and Soft Computing Lab (ASSCL), Prince Sultan University, Riyadh, Saudi Arabia & Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt
Zeeshan Haider: College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia & Automated Systems and Soft Computing Lab (ASSCL), Prince Sultan University, Riyadh, Saudi Arabia
Mohamed Tounsi: College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia & Automated Systems and Soft Computing Lab (ASSCL), Prince Sultan University, Riyadh, Saudi Arabia

International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), 2024, vol. 15, issue 1, 1-24

Abstract: Identification of diseases from body scans requires design of pre-processing, filtering, segmentation, feature representation, classification & post-processing operations. Existing deep learning-based classification models use context-specific segmentation followed by convolutions or transformer-based classification techniques. However, the majority of these models do not perform correlative analysis between different disease types. This limitation restricts their scalability and applicability in clinical scenarios. To address these limitations, this research work proposed IMAC-MOC, a model that integrates multimodal augmentations to enhance performance in cross-domain and multi-organ image classification. The proposed model initially collects large-scale scans at organ-level like MRI, CT scan, Kidney Scans, etc. and represents them via multimodal feature sets. These feature maps are combined and processed via a Bacterial Foraging Optimizer (BFO) which assists in identification of high variance inter-class feature sets.

Date: 2024
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