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Colon Disease Classification Method Based on Deep Learning.

Zhihe Zhao1, Zhifeng Gao1, Kun Zhang1

  • 1Hebei University of Science and Technology.

Studies in Health Technology and Informatics
|November 26, 2023
PubMed
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A deep learning model using A_Vit achieved 95.76% accuracy in classifying colon diseases from endoscopic images. This AI tool enhances diagnostic efficiency and accuracy for colorectal cancer detection.

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A_Vit network modelColorectal cancerDeep learningImage recognition

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Colorectal cancer (CRC) presents a high incidence and risk of misdiagnosis.
  • Accurate classification of endoscopic colon images is crucial for timely diagnosis and treatment.
  • Existing diagnostic methods may lack efficiency and accuracy in identifying gastrointestinal diseases.

Purpose of the Study:

  • To develop and evaluate a deep learning-based computer-aided diagnostic method for colon disease classification.
  • To improve the accuracy and efficiency of diagnosing gastrointestinal diseases using endoscopic images.
  • To provide decision-making support for clinicians in colorectal cancer detection.

Main Methods:

  • Dataset preprocessing including duplicate removal and enhancement techniques.
  • Implementation and comparison of two deep learning network architectures: A_Vit and MobileNet.
  • Model training using the Adam optimizer with consistent parameters and dataset.

Main Results:

  • The A_Vit network architecture demonstrated superior performance.
  • Achieved an accuracy rate of 95.76% and a recall rate of 97.21% with A_Vit.
  • The A_Vit model was selected as the preferred choice due to its high performance metrics.

Conclusions:

  • The proposed deep learning method significantly enhances the efficiency and accuracy of colon disease diagnosis.
  • A_Vit-based model offers a promising tool for computer-aided diagnosis in gastroenterology.
  • This approach can aid clinicians in making more informed decisions for colorectal cancer management.