Binary Classification of Pulmonary Nodules using Long Short-Term Memory (LSTM)

Lung cancer is a prominent reason for deaths all over the globe. A large number of cases have been detected in developed as well as developing nations. It is evident that the probability of survival in the patients is higher only if detected at its nascent stages. Thus, systems employing Computer-Aided Detection (CAD) deliver a faster diagnosis and hence can probably save lives. In the present paper, a classification model for lung nodules that uses Computed Tomography (CT) scans which classifies the given nodule into benign and malignant based on Long Short Term Memory (LSTM) is proposed. The architecture analyzes the images of the nodules extracted from LIDC/ IDRI and Luna-16 datasets. The nodule extraction is executed using the python package pylidc and LSTM is implemented using PyTorch. The highest achieved accuracy using the proposed architecture is 86.98%.

• Publication year

  2022

• Venue

  International Joint Conference on the Analysis of Images, Social Networks and Texts

• Publication date

  2022-12-09

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1109/AIST55798.2022.10065049
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Fine-Tuned Framework With Transfer Learning for Pulmonary Nodule Detection

  2022cited by this paper
• Investigating the significance of color space for abnormality detection in wireless capsule endoscopy images

  2022cited by this paper
• Dilated CNN for abnormality detection in wireless capsule endoscopy images

  2022cited by this paper
• Brain tumor grade classification Using LSTM Neural Networks with Domain Pre-Transforms

  2021cited by this paper
• Wrapper-Based Best Feature Selection Approach for Lung Cancer Detection

  2021cited by this paper
• FishResNet: Automatic Fish Classification Approach in Underwater Scenario

  2021cited by this paper
• PyTorch

  2021cited by this paper
• Dual branch convolutional neural network for copy move forgery detection

  2020cited by this paper
• Cancer statistics, 2020

  2020influential reference
• Crosspooled FishNet: transfer learning based fish species classification model

  2020cited by this paper
• A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures

  2019cited by this paper
• Fast and fully-automated detection and segmentation of pulmonary nodules in thoracic CT scans using deep convolutional neural networks

  2019cited by this paper
• NoduleNet: Decoupled False Positive Reductionfor Pulmonary Nodule Detection and Segmentation

  2019cited by this paper
• Automatic Pulmonary Nodule Detection in CT Scans Using Convolutional Neural Networks Based on Maximum Intensity Projection

  2019cited by this paper
• CNN-LSTM: Cascaded Framework For Brain Tumour Classification

  2018cited by this paper
• Diagnostic classification of lung nodules using 3D neural networks

  2018cited by this paper
• Deep learning aided decision support for pulmonary nodules diagnosing: a review.

  2018cited by this paper
• Segmentation of Lung Nodule in CT Images Based on Mask R-CNN

  2018cited by this paper
• Lung Cancer Detection: A Deep Learning Approach

  2018cited by this paper
• Standardized representation of the LIDC annotations using DICOM

  2018cited by this paper
• A fast weak-supervised pulmonary nodule segmentation method based on modified self-adaptive FCM algorithm

  2017cited by this paper
• An Ensemble of Convolutional Neural Networks for Image Classification Based on LSTM

  2017cited by this paper
• On improving recurrent neural network for image classification

  2017cited by this paper
• Automatic differentiation in PyTorch

  2017cited by this paper
• Lung nodule malignancy classification using only radiologist-quantified image features as inputs to statistical learning algorithms: probing the Lung Image Database Consortium dataset with two statistical learning methods

  2016cited by this paper
• What is a Lung Nodule?

  2016cited by this paper
• A Gentle Tutorial of Recurrent Neural Network with Error Backpropagation

  2016influential reference
• Using Deep Learning for Pulmonary Nodule Detection & Diagnosis

  2016cited by this paper
• Automatic detection of subsolid pulmonary nodules in thoracic computed tomography images

  2014cited by this paper
• The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository

  2013cited by this paper
• ImageNet classification with deep convolutional neural networks

  2012cited by this paper
• A REVIEW ON COMPUTER AIDED DETECTION AND DIAGNOSIS OF LUNG CANCER NODULES

  2012cited by this paper
• The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans.

  2011cited by this paper
• A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification

  2009cited by this paper
• Bidirectional LSTM Networks for Improved Phoneme Classification and Recognition

  2005cited by this paper
• Long Short-Term Memory

  1997cited by this paper

• The effect of noise removal filters on classifying different types of medical images

  2024cites this paper
• Tensor-RT-Based Transfer Learning Model for Lung Cancer Classification

  2023cites this paper
• A Robust Predictive Model Using Combined Residual Network and Long Short – Term Memory for Lung Cancer Identification

  2023cites this paper