Kolmogorov–Arnold Networks for Automated Diagnosis of Urinary Tract Infections
Medical diagnostics is an important step in the identification and detection of any disease. Generally, diagnosis requires expert supervision, but in recent times, the evolving emergence of machine intelligence and its widespread applications has necessitated the integration of machine intelligence...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
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| Series: | Journal of Molecular Pathology |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-5261/6/1/6 |
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| Summary: | Medical diagnostics is an important step in the identification and detection of any disease. Generally, diagnosis requires expert supervision, but in recent times, the evolving emergence of machine intelligence and its widespread applications has necessitated the integration of machine intelligence with pathological expert supervision. This research aims to mitigate the diagnostics of urinary tract infections (UTIs) by visual recognition of Colony-Forming Units (CFUs) in urine culture. Recognizing the patterns specific to positive, negative, or uncertain UTI suspicion has been complemented with several neural networks inheriting the Multi-Layered Perceptron (MLP) architecture, like Vision Transformer, Class-Attention in Vision Transformers, etc., to name a few. In contrast to the fixed model edge weights of MLPs, the novel Kolmogorov–Arnold Network (KAN) architecture considers a set of trainable activation functions on the edges, therefore enabling better extraction of features. Inheriting the novel KAN architecture, this research proposes a set of three deep learning models, namely, K<sup>2</sup>AN, KAN-C-Norm, and KAN-C-MLP. These models, experimented on an open-source pathological dataset, outperforms the state-of-the-art deep learning models (particularly those inheriting the MLP architecture) by nearly <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>7.8361</mn><mo>%</mo></mrow></semantics></math></inline-formula>. By rapid UTI detection, the proposed methodology reduces diagnostic delays, minimizes human error, and streamlines laboratory workflows. Further, preliminary results can complement (expert-supervised) molecular testing by enabling them to focus only on clinically important cases, reducing stress on traditional approaches. |
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| ISSN: | 2673-5261 |