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Related Concept Videos

Flame Photometry: Overview01:02

Flame Photometry: Overview

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Flame photometry, also known as flame emission spectrometry, is a technique used for the qualitative and quantitative analysis of elements present in a sample using a flame as the source of excitation energy. The concept of flame photometry was realized in the early 1860s by Kirchhoff and Bunsen, who discovered that specific elements emit characteristic radiation when excited in flames. The first instrument developed for this purpose was used to measure sodium (Na) in plant ash using a Bunsen...
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Flame Photometry: Lab01:16

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In a flame photometer, when a solution like potassium chloride is aspirated into the flame, the solvent evaporates, leaving behind dehydrated salt. This salt dissociates into free gaseous atoms in their ground state. Some of these atoms absorb energy from the flame, leading to their excitation. The excited atoms return to the ground state, emitting photons at characteristic wavelengths. Because only electronic transitions are involved, the resulting emission lines are very narrow. The intensity...
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Lung Nodule Detection via Optimized Convolutional Neural Network: Impact of Improved Moth Flame Algorithm.

Anuja Eliza Sebastian1, Disha Dua2

  • 1T. John Institute of Technology, Bangalore, India.

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|March 20, 2023
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Summary

This study introduces an advanced lung nodule detection model for early lung cancer identification. The novel approach significantly improves detection accuracy, aiding radiologists and enhancing patient survival rates.

Keywords:
LBP featuresLung cancerMeasuresOptimal CNNPre-processingSegmentation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Oncology

Background:

  • Lung cancer poses a global health risk, with lung nodules being a primary indicator of early-stage disease.
  • Early lung cancer detection is crucial for improving patient survival rates.
  • Automated lung nodule detection systems can reduce radiologist workload and minimize diagnostic errors.

Purpose of the Study:

  • To develop a novel, accurate, and efficient automated lung nodule detection model.
  • To enhance the early identification of lung cancer through improved nodule recognition.
  • To reduce the incidence of misdiagnosis and missed diagnoses in lung cancer screening.

Main Methods:

  • A four-stage model was developed: image pre-processing, segmentation using Otsu Thresholding, Local Binary Pattern (LBP) feature extraction, and classification.
  • Classification was performed using a Convolutional Neural Network (CNN) optimized by the Improved Moth Flame Optimization (IMFO) algorithm.
  • The IMFO algorithm optimally tuned the activation function and convolutional layer count of the CNN.

Main Results:

  • The proposed model demonstrated superior accuracy compared to existing methods.
  • Accuracy improvements were 6.85% over SVM, 2.91% over KNN, 1.75% over CNN, 0.73% over MFO, 1.83% over WTEEB, and 4.05% over GWO+FRVM.
  • The model's effectiveness was validated through comprehensive performance analysis.

Conclusions:

  • The developed lung nodule detection model offers a significant advancement in early lung cancer diagnosis.
  • The integration of IMFO for CNN optimization enhances detection accuracy and efficiency.
  • This automated system holds potential for improving lung cancer screening protocols and patient outcomes.