Authors :
Krishna Dawalkar; Omkar Joshi; Priti Mantri; Vaishnao Wankar; M.S. Bhosale
Volume/Issue :
Volume 8 - 2023, Issue 3 - March
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://bit.ly/3FGB0Zk
DOI :
https://doi.org/10.5281/zenodo.7758421
Abstract :
Early detection of lung cancer is crucial for
improving patient outcomes, but traditional methods of
diagnosis have limitations in terms of accuracy. The
Automatic Lung Cancer Detection and Classification
(ALCDC) system is an advanced approach that utilizes
Convolutional Neural Network (CNN) for detecting and
classifying lung cancer. The system was trained using a
large dataset of lung CT images and achieved high
accuracy, sensitivity, and specificity in detecting and
classifying lung cancer cases.
The ALCDC system has several advantages over
traditional methods, including automation, higher
sensitivity and specificity, non-invasiveness, and potential
reduction of the workload of radiologists. Additionally, the
system can potentially reduce the number of false positive
and false negative cases, leading to improved patient
outcomes.
In conclusion, the ALCDC system utilizing
Convolutional Neural Network is a promising approach
for enhancing early detection of lung cancer. The system
has the potential to improve the accuracy of lung cancer
diagnosis, reduce the workload of radiologists, and
ultimately improve patient outcomes. Further research is
needed to validate the system's performance in clinical
settings and investigate its potential impact on patient care.
Keywords :
Lung Cancer, Classification Of Lung Cancer, Machine Learning, Deep Learning, CNN Algorithm
Early detection of lung cancer is crucial for
improving patient outcomes, but traditional methods of
diagnosis have limitations in terms of accuracy. The
Automatic Lung Cancer Detection and Classification
(ALCDC) system is an advanced approach that utilizes
Convolutional Neural Network (CNN) for detecting and
classifying lung cancer. The system was trained using a
large dataset of lung CT images and achieved high
accuracy, sensitivity, and specificity in detecting and
classifying lung cancer cases.
The ALCDC system has several advantages over
traditional methods, including automation, higher
sensitivity and specificity, non-invasiveness, and potential
reduction of the workload of radiologists. Additionally, the
system can potentially reduce the number of false positive
and false negative cases, leading to improved patient
outcomes.
In conclusion, the ALCDC system utilizing
Convolutional Neural Network is a promising approach
for enhancing early detection of lung cancer. The system
has the potential to improve the accuracy of lung cancer
diagnosis, reduce the workload of radiologists, and
ultimately improve patient outcomes. Further research is
needed to validate the system's performance in clinical
settings and investigate its potential impact on patient care.
Keywords :
Lung Cancer, Classification Of Lung Cancer, Machine Learning, Deep Learning, CNN Algorithm
