Detection and classification of abnormalities in biological systems are critical tasks where early identification can significantly enhance intervention strategies and outcomes. Emerging technologies such as Deep Learning (DL) have opened new avenues for more accurate prediction and classification of disease-related conditions. While several machine learning algorithms can detect abnormalities at early stages to support preventive actions, many existing models suffer from limitations such as inaccuracy, bias, and overfitting. This work presents a novel approach for improving prediction accuracy by identifying essential features using advanced deep learning architectures. A new framework is proposed, combining the Dual Path Network (DPN-131), known for its robust feature extraction capabilities, with Local Interpretable Model-Agnostic Explanations (LIME) to enhance both model predictability and interpretability. The DPN-131 model effectively captures complex patterns in high-resolution biological data, enabling precise detection and classification of various abnormal conditions for cardiovascular disease. To address the interpretability challenges often encountered in deep learning models, LIME provides localized explanations that identify influential data regions and features associated with specific predictions. Experimental results on a large-scale biological dataset demonstrate that the DPN-131 model, supported by LIME, achieves state-of-the-art classification accuracy and produces interpretable, trustworthy explanations. This method provides a powerful and explainable tool to assist intelligent decision-making processes for early detection and management of abnormalities in biological systems.
Keywords
DPN-131, LIME, Medical Image Classification, Explainable AI, Deep Learning.
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CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Chandra Babu J and Reddy Madhavi K;
Methodology: Chandra Babu J;
Visualization: Chandra Babu J;
Investigation: Chandra Babu J and Reddy Madhavi K;
Supervision: Reddy Madhavi K;
Validation: Chandra Babu J and Reddy Madhavi K;
Writing- Reviewing and Editing: Chandra Babu J and Reddy Madhavi K;
All authors reviewed the results and approved the final version of the manuscript.
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Author(s) thanks to Dr. Reddy Madhavi K for this research completion and support.
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Reddy Madhavi K
Department of Artificial Intelligence and Machine Learning, School of Computing, Mohan Babu University, Tirupati, Andhra Pradesh, India.
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Cite this article
Chandra Babu J and Reddy Madhavi K, “Local Interpretable Model Agnostic with Dual Path Network for Abnormality Detection and Classification in Biological Systems”, Journal of Machine and Computing, vol.5, no.3, pp. 1403-1416, July 2025, doi: 10.53759/7669/jmc202505111.
