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An Improved Machine Learning Model for Diagnostic Cancer Recognition Using Artificial Intelligence.

N Arivazhagan1, J Venkatesh2, K Somasundaram3

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This summary is machine-generated.

This study introduces an artificial intelligence (AI) machine learning model to aid physicians in cancer diagnosis and treatment. The AI model demonstrates high accuracy in image recognition and classification, saving valuable physician time.

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Area of Science:

  • Medical technology
  • Artificial intelligence in oncology
  • Machine learning for cancer diagnosis

Background:

  • Physicians require advanced technological assistance for cancer diagnosis, classification, dosage determination, and treatment planning.
  • Current medical practices necessitate careful handling, particularly for cancer patients.

Purpose of the Study:

  • To propose an artificial intelligence (AI) based machine learning method to assist physicians in cancer diagnosis and treatment.
  • To develop a model capable of analyzing complex cancerous inputs for type and dosage description.
  • To provide recommendations on cancer effects and appropriate medical procedures.

Main Methods:

  • Development of a machine learning model utilizing artificial intelligence.
  • Design focused on processing complex cancerous inputs.
  • Implementation for image recognition, classification, and treatment recommendation.

Main Results:

  • Achieved 93.31% image recognition and 6.69% image rejection.
  • Attained 94.22% accuracy, 92.42% precision, 93.94% recall rate, and 92.6% F1-score.
  • Demonstrated a computational speed of 2178 ms, outperforming existing methods.

Conclusions:

  • The proposed AI machine learning model effectively assists physicians in cancer-related tasks.
  • The model shows strong performance metrics, indicating its potential for clinical application.
  • This technology offers significant time-saving benefits for medical professionals in oncology.