March 13, 2024, 4:42 a.m. | Walid El Maouaki, Taoufik Said, Mohamed Bennai

cs.LG updates on arXiv.org arxiv.org

arXiv:2403.07856v1 Announce Type: new
Abstract: This study addresses the urgent need for improved prostate cancer detection methods by harnessing the power of advanced technological solutions. We introduce the application of Quantum Support Vector Machine (QSVM) to this critical healthcare challenge, showcasing an enhancement in diagnostic performance over the classical Support Vector Machine (SVM) approach. Our study not only outlines the remarkable improvements in diagnostic performance made by QSVM over the classic SVM technique, but it delves into the advancements brought …

abstract advanced analysis application arxiv cancer cancer detection challenge cs.lg detection detection methods diagnostic healthcare machine performance performance analysis power quant-ph quantum solutions study support type vector

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