International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Volume 8 Issue 5
September-October 2026

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A Review of Entropy-Guided Latent Risk State Discovery for Thyroid Nodule Classification using Deep Learning and Gaussian Hidden Markov Models

Author(s) Mr. Swapnil Banerjee, Dr. Kakali Karmakar(Sur)
Country India
Abstract Conventional thyroid nodule classification studies primarily focus on observable diagnostic states, namely benign and malignant classes. While such approaches provide useful classification outcomes, they offer limited insight into diagnostic uncertainty and hidden structures present within the data. In this work, an entropy-guided latent risk-state discov-ery framework is proposed by integrating deep learning, Shannon entropy analysis, and Gaussian Hidden Markov Models (HMMs). A convolutional neural network is employed to estimate benign and malignant probabilities from thyroid ultrasound images. Shannon entropy is subsequently computed to quantify prediction uncertainty. The resulting probability and entropy values are combined into observation vectors and analysed using a Gaus-sian Hidden Markov Model. The proposed framework discovers four latent risk states and reveals a maximum-uncertainty state characterised by nearly equal benign and malignant probabilities and entropy approaching its theoretical maximum. The results demonstrate that entropy-enhanced latent-state modelling provides richer diagnostic interpretation than conventional binary classification and offers a promising direction for uncertainty-aware clinical decision support.
Keywords Shannon Entropy, Hidden Markov Model, Thyroid Nodule Classification, Deep Learning, Uncertainty Quantification, Latent Risk States
Field Mathematics > Statistics
Published In Volume 8, Issue 5, September-October 2026
Published On 2026-09-22
DOI https://doi.org/10.36948/ijfmr.2026.v08i05.87774
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E-ISSN 2582-2160
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CrossRef DOI prefix of IJFMR is 10.36948/ijfmr

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