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 - 6 | Issue - 1 | march 2024
Published
06 April, 2024
Lung cancer remains a significant global health challenge, demanding early detection for improved patient outcomes. In recent years, deep learning, notably Convolutional Neural Networks (CNNs), has emerged as a potent tool for lung cancer detection and diagnosis from medical imaging data. This research offers an extensive review of CNN-based approaches for lung cancer detection, highlighting their strengths, limitations, and potential clinical impact. The study discusses the methodology, covering data collection, preprocessing, model architecture selection, training, evaluation, and validation, alongside future directions and clinical implications. CNNs offer researchers and healthcare professionals avenues to augment early detection, personalized treatment planning, and ultimately, enhance patient care in lung cancer management. Through rigorous development and evaluation, CNN models trained on diverse datasets of chest X-rays or CT scans have demonstrated remarkable accuracy in identifying suspicious lung lesions indicative of cancer, often outperforming conventional methods. The proposed study utilizes the GoogleNet (Inception v1) CNN model to detect lung cancer. The performance of GoogleNet improved the accuracy of detection by approximately 4.29% compared to existing methods.
KeywordsLung cancer Convolutional Neural Networks (CNNs) Medical imaging GoogleNet (Inception v1)
Full Article PDF