Instruct: Given the title of a scientific paper, retrieve paper titles that are cited by the given paper.
Query: Performance analysis of various machine learning-based approaches for detection and classification of lung cancer in humans
Lung Cancer Detection using Image Processing Techniques
Deep Learning for the Classification of Lung Nodules
Lung Nodule Detection and Classification
Diagnosis of Lung Cancer Prediction System Using Data Mining Classification Techniques
Learning Deep Spatial Lung Features by 3D Convolutional Neural Network for Early Cancer Detection
Using Deep Learning for Classification of Lung Nodules on Computed Tomography Images
machine learning approaches to lung cancer prediction from mass spectra .
Lung Cancer Detection Using Fuzzy Auto-Seed Cluster Means Morphological Segmentation and SVM Classifier
Detection and classification of lung abnormalities by use of convolutional neural network (CNN) and regions with CNN features (R-CNN)
Weakly Supervised Learning for Whole Slide Lung Cancer Image Classification
Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification
Lung cancer diagnosis using CT-scan images based on cellular learning automata
Fully automatic detection of lung nodules in CT images using a hybrid feature set
An Appraisal of Lung Nodules Automatic Classification Algorithms for CT Images
Breast cancer classification using machine learning
Naive Bayes Classifiers: A Probabilistic Detection Model for Breast Cancer
A deep learning approach for the analysis of masses in mammograms with minimal user intervention
A Hybrid Approach to Segmentation of Diseased Lung Lobes
Fast CapsNet for Lung Cancer Screening
comparing two classes of end - to - end machine - learning models in lung nodule detection and classification : mtanns vs . cnns .
Dataflow design for optimal incremental SVM training
Learning Hierarchical Features for Scene Labeling
Strategies for Lean Product Development
Graph-Sparse LDA: A Topic Model with Structured Sparsity
View Synthesis by Appearance Flow