Abstract:Early detection is crucial for successful cancer treatment and increasing survivability rates, particularly in the most common forms. Ten different cancers have been identified in most of these advances that effectively use CNNs (Convolutional Neural Networks) for classification. The distinct architectures of CNNs used in each study concentrate on pattern recognition for different types of cancer across various datasets. The advantages and disadvantages of each approach are identified by comparing these architectures. This study explores the potential of integrating CNNs into clinical practice to complement traditional diagnostic methods. It also identifies the top-performing CNN architectures, highlighting their role in enhancing diagnostic capabilities in healthcare.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) |
| Cite as: | arXiv:2412.17155 [cs.CV] |
| (or arXiv:2412.17155v4 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2412.17155 arXiv-issued DOI via DataCite |
Submission history
From: Hossein Molaeian [view email]
[v1]
Sun, 22 Dec 2024 20:33:59 UTC (2,101 KB)
[v2]
Tue, 24 Dec 2024 07:01:36 UTC (1,423 KB)
[v3]
Sun, 2 Feb 2025 13:54:28 UTC (1,571 KB)
[v4]
Thu, 14 May 2026 17:50:45 UTC (1,640 KB)