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Detecting of breast cancer, a crucial problem all over the world, relies on new and efficient approaches. Besides, the traditional test methods such as mammograms, ultrasound, and biopsy and other tests are complemented by this study with a deep learning approach toward breast cancer detection via histopathological images. The study developed an architecture of a CNN model to classify images into; cancer and health. This model comprises of Decision Trees, Stochastic Gradient Descent, K-Nearest Neighbors, Support Vector Machines, and Convolutional Neural Networks gymnastics. To determine an accurate method of breast cancer classification the study analyzed the performance of each given approach. In view of the above-stated findings, the current study may aid the advancement of CAD systems for breast cancer with an outstanding possibility of enhancing the diagnostic rate and quality.

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Comparative Analysis of Machine Learning and Deep Learning Models for Detection of Breast Cancer Using Histopathological Images

  • 会议论文

  • 发表日期：

    2025年

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  • Sneha Kishor Patle,
  • Prateek Verma,
  • Akshay Deshmukh,
  • Ajay Thatere,
  • Rakesh Shahu

摘要

Detecting of breast cancer, a crucial problem all over the world, relies on new and efficient approaches. Besides, the traditional test methods such as mammograms, ultrasound, and biopsy and other tests are complemented by this study with a deep learning approach toward breast cancer detection via histopathological images. The study developed an architecture of a CNN model to classify images into; cancer and health. This model comprises of Decision Trees, Stochastic Gradient Descent, K-Nearest Neighbors, Support Vector Machines, and Convolutional Neural Networks gymnastics. To determine an accurate method of breast cancer classification the study analyzed the performance of each given approach. In view of the above-stated findings, the current study may aid the advancement of CAD systems for breast cancer with an outstanding possibility of enhancing the diagnostic rate and quality.