Vol. 5 No. 1 (2025): Issue 5
Articles

Deep Learning-based Medical Image Segmentation for Early Cancer Detection

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  • Yuhan Dai
DOI: https://doi.org/10.71070/oaml.v5i1.144

Published 2025-09-01

Keywords

  • Deep Learning,
  • Medical Image Segmentation,
  • Cancer Detection,
  • Neural Network Architectures,
  • Feature Engineering

How to Cite

Dai, Y. (2025). Deep Learning-based Medical Image Segmentation for Early Cancer Detection. Optimizations in Applied Machine Learning, 5(1). https://doi.org/10.71070/oaml.v5i1.144

Copyright (c) 2025 Yuhan Dai

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Abstract

This paper addresses the pressing need for improved early cancer detection through the development of a deep learning-based medical image segmentation approach. Despite significant advancements in medical imaging technology, accurate and efficient segmentation of cancerous regions remains a challenging task. Current research efforts have primarily focused on traditional segmentation methods, which are often limited by their reliance on manual feature engineering and lack of adaptability to diverse medical image datasets. In response to these challenges, this study proposes a novel deep learning framework tailored specifically for medical image segmentation tasks. By integrating advanced neural network architectures and optimization techniques, our approach aims to enhance the accuracy and speed of cancer detection in medical images. Through extensive experimentation and comparative analysis, this paper demonstrates the effectiveness and potential of the proposed method in improving early cancer detection, thereby contributing to the ongoing efforts in advancing medical image analysis for clinical applications.

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