Analysing DCE-MRI scans using hybrid techniques for early detection of prostate cancer based on fusion features of handcrafted and deep learning features.

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Abstract

Prostate cancer is among the most diagnosed malignancies in men worldwide and a leading cause of cancer-related mortality. Early and accurate diagnosis is critical to improve patient outcomes and reduce the risks of overtreatment or missed detection. Conventional diagnostic approaches, including prostate-specific antigen (PSA) testing, digital rectal examination (DRE), and histopathological analysis, often suffer from limited sensitivity and specificity, leading to false positive or delayed diagnosis. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has recently emerged as an effective modality for prostate cancer detection, providing complementary anatomical and functional information. This study proposes a novel hybrid diagnostic framework that integrates Generalized Quantum Gamma Polynomial (GQGP) features, kinetic signal intensity features, and deep learning-based representations. GQGP features capture subtle intensity variations and quantum-inspired statistical characteristics, while kinetic features quantify contrast-enhancement dynamics to discriminate malignant from benign tissues. These handcrafted descriptors are fused with high-level features extracted using convolutional neural networks (CNNs) to construct a comprehensive feature representation. Experimental evaluation on publicly available prostate imaging datasets demonstrates that the proposed fusion framework significantly outperforms single-feature and traditional methods, achieving a classification accuracy of 97.32%. The results highlight the effectiveness of combining mathematical modeling, radiomics, and artificial intelligence for improved prostate cancer diagnosis.

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