Diabetic Retinopathy Detection Using Machine Learning
Volume-4 | Issue-1
Monocular Depth Estimation using a Multi-grid Attention-based Model
Volume-4 | Issue-3
Speedy Image Crowd Counting by Light Weight Convolutional Neural Network
Volume-3 | Issue-3
Construction of Efficient Smart Voting Machine with Liveness Detection Module
Volume-3 | Issue-3
An Economical Robotic Arm for Playing Chess Using Visual Servoing
Volume-2 | Issue-3
Triplet loss for Chromosome Classification
Volume-4 | Issue-1
Unstructured Noise Removal for Industrial Sensor Imaging Unit by Hybrid Adaptive Median Algorithm
Volume-3 | Issue-4
Real Time Sign Language Recognition and Speech Generation
Volume-2 | Issue-2
Analysis of Artificial Intelligence based Image Classification Techniques
Volume-2 | Issue-1
Design of ANN Based Machine Learning Method for Crop Prediction
Volume-3 | Issue-3
A REVIEW ON IOT BASED MEDICAL IMAGING TECHNOLOGY FOR HEALTHCARE APPLICATIONS
Volume-1 | Issue-1
COMPUTER VISION BASED TRAFFIC SIGN SENSING FOR SMART TRANSPORT
Volume-1 | Issue-1
Diabetic Retinopathy Detection Using Machine Learning
Volume-4 | Issue-1
Accurate Segmentation for Low Resolution Satellite images by Discriminative Generative Adversarial Network for Identifying Agriculture Fields
Volume-3 | Issue-4
Deep Learning based Handwriting Recognition with Adversarial Feature Deformation and Regularization
Volume-3 | Issue-4
State of Art Survey on Plant Leaf Disease Detection
Volume-4 | Issue-2
Optimal Compression of Remote Sensing Images Using Deep Learning during Transmission of Data
Volume-3 | Issue-4
OverFeat Network Algorithm for Fabric Defect Detection in Textile Industry
Volume-3 | Issue-4
VIRTUAL RESTORATION OF DAMAGED ARCHEOLOGICAL ARTIFACTS OBTAINED FROM EXPEDITIONS USING 3D VISUALIZATION
Volume-1 | Issue-2
Two-Stage Frame Extraction in Video Analysis for Accurate Prediction of Object Tracking by Improved Deep Learning
Volume-3 | Issue-4
Volume - 5 | Issue - 4 | december 2023
Published
22 November, 2023
According to World Health Organisation (WHO), lung cancer is the leading cause of cancer-related fatalities in both genders and has the highest fatality rate. Early detection of pulmonary nodules is essential to improving the significant survival rate of lung cancer due to the typical proliferation of lung cells. Studies on lung cancer indicate that smoking is the primary cause of this disease, which is more common in women nowadays and causes more deaths than breast cancer. Age, gender, race, socioeconomic status, exposure to the environment, air pollution, alcohol consumption, and second-hand smoking are a few more factors that could be significant in causing lung cancer. Early detection of lung cancer is achieved through a variety of image processing techniques, such as computed tomography (CT), bone scanning, magnetic resonance imaging (MRI), Positron Emission Tomography, PET-CT, and X-ray scanning. These techniques are combined with machine learning algorithms, data mining, and artificial intelligence-based detection techniques, which improve detection through efficient computing systems known as computer assisted diagnosis (CAD). Since practically all lung cancer screening and detection is dependent on image processing, this article will serve as a reference for aspiring researchers to understand the many detection strategies in effectively identifying lung cancer. Additionally, five distinct methods are evaluated and critically analysed, along with their benefits and drawbacks, taking into account the present and potential future developments in early lung cancer diagnosis for human survival.
KeywordsMachine Learning Image processing Lung Cancer Communication Signal Processing Cancer Health Safety
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