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Identification of Anomalies in Lung and Colon Cancer Using Computer Vision-Based Swin Transformer with Ensemble Model

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Summary

This study introduces a novel AI technique for diagnosing lung and colon cancer (LCC) from histopathological images. The Lung and Colon Cancer using a Swin Transformer with an Ensemble Model on the Histopathological Images (LCCST-EMHI) method shows promising accuracy in early LCC detection.

Keywords:
ensemble learninghistopathological imageslung and colon cancerswin transformerwalrus optimization algorithm

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Area of Science:

  • Medical imaging
  • Artificial Intelligence in Oncology
  • Computational Pathology

Background:

  • Lung and colon cancer (LCC) pose significant health risks, necessitating accurate and timely diagnosis.
  • Conventional diagnostic methods for LCC face limitations in efficiency and accuracy, impacting early detection and treatment.
  • Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), offers advanced capabilities for analyzing histopathological images.

Purpose of the Study:

  • To develop and evaluate a novel deep learning model, LCCST-EMHI, for the accurate diagnosis and classification of LCC using histopathological images.
  • To enhance the precision of LCC diagnosis through advanced image processing and feature extraction techniques.
  • To leverage an ensemble of DL models optimized by a metaheuristic algorithm for robust LCC detection.

Main Methods:

  • A novel technique, Lung and Colon Cancer using a Swin Transformer with an Ensemble Model on the Histopathological Images (LCCST-EMHI), was proposed.
  • Histopathological images were preprocessed using bilateral filtering (BF) to reduce noise.
  • Feature extraction was performed using the Swin Transformer (ST) model.
  • An ensemble classifier combining bidirectional long short-term memory with multi-head attention (BiLSTM-MHA), Double Deep Q-Network (DDQN), and sparse stacked autoencoder (SSAE) was employed.
  • Hyperparameter optimization for the DL models was achieved using the walrus optimization algorithm (WaOA).

Main Results:

  • The LCCST-EMHI approach demonstrated promising diagnostic and classification performance on a benchmark dataset.
  • Extensive simulation analyses confirmed the effectiveness of the proposed method.
  • The LCCST-EMHI approach outperformed other recent methods in LCC detection and classification.

Conclusions:

  • The LCCST-EMHI method presents a highly effective deep learning-based solution for LCC diagnosis from histopathological images.
  • The integration of Swin Transformer and an ensemble of DL classifiers significantly improves diagnostic accuracy.
  • This AI-driven approach holds potential for advancing early and precise detection of lung and colon cancer.