AI-Powered Early Detection of Critical Diseases using Image Processing and Audio Analysis

• URL: http://arxiv.org/abs/2510.25199v1
• Date: Wed, 29 Oct 2025 06:09:17 GMT
• Title: AI-Powered Early Detection of Critical Diseases using Image Processing and Audio Analysis
• Authors: Manisha More, Kavya Bhand, Kaustubh Mukdam, Kavya Sharma, Manas Kawtikwar, Hridayansh Kaware, Prajwal Kavhar,
• Abstract summary: This paper presents a multimodal artificial intelligence (AI) diagnostic framework integrating image analysis, thermal imaging, and audio signal processing.<n>A fine-tuned MobileNetV2 convolutional neural network was trained on the ISIC 2019 dataset for skin lesion classification.<n>A support vector machine (SVM) with handcrafted features was employed for thermal clot detection, achieving 86.4% accuracy.<n>The framework provides a promising step toward scalable, real-time, and accessible AI-based pre-diagnostic healthcare solutions.
• Score: 0.4356470230135012
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Early diagnosis of critical diseases can significantly improve patient survival and reduce treatment costs. However, existing diagnostic techniques are often costly, invasive, and inaccessible in low-resource regions. This paper presents a multimodal artificial intelligence (AI) diagnostic framework integrating image analysis, thermal imaging, and audio signal processing for early detection of three major health conditions: skin cancer, vascular blood clots, and cardiopulmonary abnormalities. A fine-tuned MobileNetV2 convolutional neural network was trained on the ISIC 2019 dataset for skin lesion classification, achieving 89.3% accuracy, 91.6% sensitivity, and 88.2% specificity. A support vector machine (SVM) with handcrafted features was employed for thermal clot detection, achieving 86.4% accuracy (AUC = 0.89) on synthetic and clinical data. For cardiopulmonary analysis, lung and heart sound datasets from PhysioNet and Pascal were processed using Mel-Frequency Cepstral Coefficients (MFCC) and classified via Random Forest, reaching 87.2% accuracy and 85.7% sensitivity. Comparative evaluation against state-of-the-art models demonstrates that the proposed system achieves competitive results while remaining lightweight and deployable on low-cost devices. The framework provides a promising step toward scalable, real-time, and accessible AI-based pre-diagnostic healthcare solutions.

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