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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control)
Zannatul Ferdushie
University of Frontier Technology
Computer Science & IT

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Comparative evaluation of classical and machine learning methods for medical image enhancement Md. Mehedi Hasan; Sujon Chandra Sutradhar; Zannatul Ferdushie; Rabeya Basri
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 4: August 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i4.27700

Abstract

Medical imaging is critical for diagnostic accuracy, yet raw images often suffer from noise and low contrast. This study provides a comparative evaluation of classical methods, namely the Laplace transform (LT), Sobel operator (SO), and histogram equalization (HE), against a data-driven convolutional neural network (CNN) using the musculoskeletal radiographs (MURA) and Human Metapneumovirus (HMPV) lung computed tomography (CT) datasets. While quantitative analysis shows that HE and SO significantly outperform other methods in isolated contrast enhancement and edge definition, they often introduce artifacts. In contrast, the CNN based approach demonstrates superior detail preservation and entropy, offering a more balanced and adaptive solution for diverse diagnostic requirements. Our findings statistically validate that although classical operators remain highly effective for specific boundary detection tasks, machine learning (ML) frameworks provide the most robust performance for cross-modality image enhancement, bridging the gap between raw data acquisition and clinical interpretation.
Co-Authors Md. Mehedi Hasan Rabeya Basri Sujon Chandra Sutradhar