Authors:
Krishna Kant Agrawal, Amit Yadav, Madhumita Madhumita, Anil Kumar Yadav, Charul Nigam, Dimitrios A. Karras

Addresses:
School of Computer Science and Engineering, Galgotias University, Gautam Buddh Nagar, Uttar Pradesh, India. Department of Computer Application, PSIT College of Higher Education, Kanpur, Uttar Pradesh, India. School of Information Technology, AIMS Institutes, Bengaluru, Karnataka, India. Department of Computer Application, Chaudhary Charan Singh University, Meerut, Uttar Pradesh, India. Department of Computer Applications, Maharaja Agrasen Institute of Management Studies, Rohini, Delhi, India. Department of Computer Engineering, EPOKA University, Tirane-Rinas, Tirana, Albania.

Abstract:

The development of systems for the recognition of digital images utilizing fuzzy logic and sparse representation (Robust Fuzzy and Sparse Multispectral Digital Image Recognition Systems, RF-SMDRS) can be very beneficial to healthcare. These systems combine fuzzy logic and sparse representation to achieve greater accuracy and reliability in the identification of digital images. The combination of fuzzy logic and sparse representation enables the management of the variations and complexities that naturally occur in digital imaging of the human body. Both fuzzy logic and sparse representation can improve the performance of RF-SMDRS across many types of healthcare applications. Fuzzy logic enables these systems to process images containing imprecise or fuzzy data. As such, fuzzy logic is most beneficial for processing digital medical images, where the shapes, textures, and intensities can vary greatly. By incorporating fuzzy logic into the design of RF-SMDRS, these systems can accurately and consistently identify and classify medical conditions, thereby assisting healthcare professionals in diagnosing and treating their patients. Sparse representation reduces the dimensionality of digital images, thereby speeding up RF-SMDRS image detection. By enabling faster and more accurate diagnosis of medical issues, RF-SMDRS may greatly improve patient outcomes in healthcare, which demands fast response times. RF-SMDRS's accurate, quick diagnosis of medical diseases could transform healthcare delivery. This research proposal will advance medical imaging and healthcare delivery.

Keywords: Fuzzy Logic; Sparse Multispectral; Patient Treatment; Cancer Detection; Image Recognition; Healthcare Application; Rapid Identification; Medical Conditions; Dimensionality Reduction.

Received on: 02/02/2025, Revised on: 29/04/2025, Accepted on: 04/07/2025, Published on: 11/01/2026

DOI: 10.69888/FTSCS.2026.000606

FMDB Transactions on Sustainable Computing Systems, 2026 Vol. 4 No. 1, Pages: 1-21

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