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Classification of Illness01:17

Classification of Illness

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The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
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Related Experiment Video

Updated: Sep 20, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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Lung Cancer Detection Based on Kernel PCA-Convolution Neural Network Feature Extraction and Classification by Fast

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Summary
This summary is machine-generated.

This study introduces a novel deep learning method for lung tumor detection using histopathological images. The approach accurately distinguishes cancerous from non-cancerous cells, improving diagnostic capabilities.

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Area of Science:

  • Oncology
  • Medical Imaging
  • Computational Biology

Background:

  • Tumor histology is crucial for lung cancer prognosis and treatment.
  • Deep learning (DL) and machine learning (ML) offer advanced tools for cancer diagnosis and risk stratification.
  • Accurate lung cancer detection is vital due to increasing global mortality rates.

Purpose of the Study:

  • To propose a deep learning-based method for lung tumor detection using histopathological images.
  • To enhance the accuracy of distinguishing between tumorous and non-tumorous lung cells.
  • To evaluate the proposed method's performance against existing techniques.

Main Methods:

  • Histopathological images were preprocessed (noise removal, resizing, enhancement).
  • Feature extraction was performed using Kernel PCA integrated with a Convolutional Neural Network (KPCA-CNN).
  • Classification of extracted features was achieved using a Fast Deep Belief Neural Network (FDBNN).

Main Results:

  • The proposed KPCA-CNN and FDBNN model demonstrated enhanced accuracy in lung tumor detection.
  • Performance metrics including accuracy, precision, recall, and F-measure were evaluated.
  • Comparative analysis showed superior performance over existing methodologies across various datasets.

Conclusions:

  • The developed deep learning approach effectively detects lung tumors from histopathological images.
  • This method shows significant potential for improving the accuracy and efficiency of lung cancer diagnosis.
  • Further research can explore radiologic data integration for comprehensive disease characterization.