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

TL;DR

This paper introduces a multimodal AI framework combining image, thermal, and audio analysis for early detection of critical diseases, aiming to improve accessibility and reduce costs in healthcare diagnostics.

Contribution

It presents a novel integrated AI diagnostic system that combines multiple modalities and achieves high accuracy on diverse health conditions, suitable for low-resource settings.

Findings

Achieved 89.3% accuracy in skin cancer detection with MobileNetV2.

Supported thermal clot detection with 86.4% accuracy using SVM.

Classified cardiopulmonary sounds with 87.2% accuracy using Random Forest.

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.