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Endoscopic Image Classification Based on Explainable Deep Learning.

Doniyorjon Mukhtorov1, Madinakhon Rakhmonova1, Shakhnoza Muksimova1

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Explainable artificial intelligence (XAI) enhances deep learning for medical diagnostics. A ResNet152 model with Grad-CAM achieved 93.46% accuracy in classifying endoscopy images, improving diagnostic transparency.

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Area of Science:

  • Medical Diagnostics
  • Artificial Intelligence
  • Computer Vision

Background:

  • Deep learning models demonstrate high accuracy in medical diagnostics but often function as "black boxes", lacking interpretability.
  • Explainable Artificial Intelligence (XAI) aims to demystify AI decision-making processes, enabling informed clinical support.

Purpose of the Study:

  • To develop and evaluate an explainable deep learning method for endoscopy image classification.
  • To enhance the transparency and trustworthiness of AI-driven diagnostic tools in gastroenterology.

Main Methods:

  • Implemented a deep learning model using ResNet152 architecture.
  • Integrated Grad-CAM (Gradient-weighted Class Activation Mapping) for generating heatmaps to visualize classification reasoning.
  • Utilized the KVASIR dataset, comprising 8000 wireless capsule endoscopy images, with an efficient augmentation strategy.

Main Results:

  • Achieved high accuracy in medical image classification: 98.28% on the training set and 93.46% on the validation set.
  • Grad-CAM heatmaps provided visual explanations for the model's classification decisions.
  • The explainable approach successfully enhanced the interpretability of the deep learning model.

Conclusions:

  • The proposed explainable deep learning method effectively classifies endoscopy images with high accuracy.
  • XAI techniques, like Grad-CAM, are crucial for understanding and trusting AI in medical diagnostics.
  • This approach offers a pathway for more transparent and reliable AI-assisted medical diagnoses.