Lung Cancer Prediction and Classification Using Decision Tree and VGG16 Convolutional Neural Networks DOI Open Access

S. Udit Krishna,

A. N. Barath Lakshman,

T. Archana

et al.

The Open Biomedical Engineering Journal, Journal Year: 2024, Volume and Issue: 18(1)

Published: April 22, 2024

Introduction A malignant abnormal growth that starts in the tissues of lungs is called Lung Cancer. It ranks among most common and lethal cancers globally. Cancer particularly dangerous because its aggressive nature how quickly it can extend to other areas body. We propose a two-step verification architecture check presence The model proposed by this paper first assesses patient based on few questions about patient's symptoms medical background. Then, algorithm determines whether has low, medium, or high risk developing lung cancer diagnosing response using “Decision Tree” classification at an accuracy 99.67%. If medium risk, we further validate finding examining CT scan image “VGG16” CNN 92.53%. Background One key research prediction identify patients history. Its subjective makes challenging apply real-world scenarios. Another area field involves forecasting cells imagery, providing accuracy. However, requires physician intervention not appropriate for early-stage prediction. Objective This aims forecast severity analyzing with regarding past conditions. examine their scan, result also predict type Methodology uses Customised implementation. used analyze answers given distinguish use Convolution Neural Networks image, categorize Results approach yields customized indicate suffered 92.53% Conclusion indicates our technique provides greater than prior approaches problem extensive prognosis

Language: Английский

Enhancing lung cancer prediction through crow search, artificial bee colony algorithms, and support vector machine DOI
Samira Tared,

Latifa Khaouane,

Salah Hanini

et al.

International Journal of Information Technology, Journal Year: 2024, Volume and Issue: 16(5), P. 2863 - 2873

Published: March 4, 2024

Language: Английский

Citations

4
Hepatitis C prediction using SVM, logistic regression and decision tree DOI Creative Commons

Anjuman Ara,

Anhar Sami,

D. Michael

et al.

World Journal of Advanced Research and Reviews, Journal Year: 2024, Volume and Issue: 22(2), P. 926 - 936

Published: May 16, 2024

Hepatitis C is an infection of the liver brought on by HCV virus. In this condition, early diagnosis challenging because delayed onset symptoms. Predicting well enough can spare patients from permeant damage. The primary goal work to use several machine learning methods forecast disease based widely available and reasonably priced blood test data in order diagnose treat on. Three techniques support vector (SVM), logistic regression, decision tree, has been applied one dataset work. To find a suitable approach for illness prediction, confusion matrix, precision, recall, F1 score, accuracy, receiver operating characteristics (ROC), performances different strategies have assessed. SVM model's overall accuracy 0.92, highest among three models.

Language: Английский

Citations

4
Lung Cancer Diagnosis Using Image Enhancement and Machine Learning Methods DOI
Tarannum Khan, Swaleha Zubair

Communications in computer and information science, Journal Year: 2025, Volume and Issue: unknown, P. 197 - 208

Published: Jan. 1, 2025

Language: Английский

Citations

0
Multiclass Classification of Lung Cancer with SVM and XGBoost with 20+ Features DOI
Zahereel Ishwar Abdul Khalib, Nur Hafizah Ghazali, SC Wong

et al.

Studies in computational intelligence, Journal Year: 2025, Volume and Issue: unknown, P. 317 - 327

Published: Jan. 1, 2025

Language: Английский

Citations

0
Machine Learning Approaches for Lung Cancer Prediction DOI

A. Nagamuruganandam,

C. P. Chandran,

S. Rajathi

et al.

Communications in computer and information science, Journal Year: 2025, Volume and Issue: unknown, P. 184 - 198

Published: Jan. 1, 2025

Language: Английский

Citations

0
Lung Diseases Classification Using Artificial Neural Network With Ant Colony Optimization DOI

T. Aarthi,

P. Dhamayanthi,

S. Indhumathi

et al.

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 223 - 248

Published: March 7, 2025

The classification of medical data is the most difficult problem to solve among all research problems since it has more commercial significance in context health analytics. Several researchers have looked into using Artificial Intelligence (AI) for lung disease classification. This paper proposed a novel algorithm diagnosis various diseases. Already known existing algorithms some drawback noise removal and process. In this approach, better technique used remove unwanted noises input image. Hybridization Neural Network with Ant Colony Optimization based predict accurate obtain efficiency. suggested HANNACO was evaluated qualitatively obtained 95.30% accuracy, 93.72% minimum time duration 18 ms over current approaches such as Decision Tree, SVM, KNN.

Language: Английский

Citations

0
The efficacy of machine learning models in lung cancer risk prediction with explainability DOI Creative Commons
Refat Khan Pathan, Israt Jahan Shorna,

Md. Sayem Hossain

et al.

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(6), P. e0305035 - e0305035

Published: June 13, 2024

Among many types of cancers, to date, lung cancer remains one the deadliest cancers around world. Many researchers, scientists, doctors, and people from other fields continuously contribute this subject regarding early prediction diagnosis. One significant problems in is black-box nature machine learning models. Though detection rate comparatively satisfactory, have yet learn how a model came that decision, causing trust issues among patients healthcare workers. This work uses multiple models on numerical dataset cancer-relevant parameters compares performance accuracy. After comparison, each has been explained using different methods. The main contribution research give logical explanations why reached particular decision achieve trust. also compared with previous study worked similar took expert opinions their proposed model. We showed our achieved better results than specialist opinion hyperparameter tuning, having an improved accuracy almost 100% all four

Language: Английский

Citations

3
TPOT with SVM hybrid machine learning model for lung cancer classification using CT image DOI

Nayana N. Murthy,

K Thippeswamy

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 104, P. 107465 - 107465

Published: Jan. 18, 2025

Language: Английский

Citations

0
Optimizing non small cell lung cancer detection with convolutional neural networks and differential augmentation DOI Creative Commons

Vahiduddin Shariff,

Chiranjeevi Paritala,

Krishna Mohan Ankala

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: May 5, 2025

Language: Английский

Citations

0
A Hybrid Data Mining Model for Early Detection of Lung Cancer Utilizing Supervised Feature Extraction DOI
Inssaf El Guabassi, Zakaria Bousalem,

Rim Marah

et al.

Lecture notes in networks and systems, Journal Year: 2025, Volume and Issue: unknown, P. 42 - 52

Published: Jan. 1, 2025

Language: Английский

Citations

0