The Potential of Convolutional Neural Networks for Cancer Detection
Journal:
arXiv
Published Date:
Dec 22, 2024
Abstract
Early detection is a prime requisite for successful cancer treatment and
increasing its survivability rates, particularly in the most common forms. CNNs
(Convolutional Neural Networks) are very potent tools for the analysis and
classification of medical images, with particular reference to the early
detection of different types of cancer. Ten different cancers have been
identified in most of these advances that use CNN techniques for
classification. The unique architectures of CNNs employed in each study are
focused on pattern recognition for each type of cancer through different
datasets. By comparing and analyzing these architectures, the strengths and
drawbacks of each approach are pointed out in terms of their efforts toward
improving the earlier detection of cancer. The opportunity to embrace CNNs
within the clinical sphere was interrogated as support or potential
substitution of traditional diagnostic techniques. Furthermore, challenges such
as integrating diverse data, how to interpret the results, and ethical dilemmas
continue to stalk this field with inconceivable hindrances. This study
identifies those CNN architectures that carry out the best work and offers a
comparative analysis that reveals to researchers the impact of CNNs on cancer
detection in the leap toward boosting diagnostic capabilities in health.
