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Convolutional Neural Network-Based Automatic Classification of Colorectal and Prostate Tumor Biopsies Using

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Area of Science:

  • Medical Imaging
  • Computational Pathology
  • Artificial Intelligence in Oncology

Background:

  • Colorectal and prostate cancers are leading male cancers globally.
  • Manual histological analysis of biopsy samples is time-consuming and prone to observer variability.
  • Current diagnostic methods for these cancers can impact reliability and efficiency.

Purpose of the Study:

  • To develop an automated computerized system for colorectal and prostate tumor diagnosis.
  • To enhance diagnostic accuracy and reduce the time associated with manual pathological analysis.
  • To leverage deep learning for improved cancer detection from biopsy images.

Main Methods:

  • A novel convolutional neural network (CNN) architecture was proposed.
  • The CNN model was designed for classifying colorectal and prostate tumors using multispectral biopsy images.
  • Key modifications included removing the last convolutional block and halving filters per layer.

Main Results:

  • The proposed CNN achieved high accuracy: 99.8% for prostate and 99.5% for colorectal datasets.
  • The system outperformed pre-trained CNNs and other classification methods.
  • It eliminated the need for preprocessing and utilized a single CNN model for the entire task.

Conclusions:

  • The developed CNN architecture demonstrated superior performance in classifying colorectal and prostate tumor images.
  • The system offers a more efficient and reliable alternative to manual pathological review.
  • The proposed CNN architecture is computationally efficient and requires no image preprocessing, making it ideal for clinical application.