Lung Cancer Detection Research Topics

With the help of Artificial intelligence (AI) and Machine learning (ML), the automatic detection of lung cancer is done. To know more about this process continue reading this study based on it, to get a clear picture of lung cancer detection.

  1. Define Automated Lung Cancer Diagnosis

The lung cancer is diagnosed automatically using image processing technique and artificial intelligence. They will detect the cancer by analyzing the medical images like CT scans with maximum accuracy rate it will produce efficient and timely detection.

  1. What is Automated Lung Cancer Diagnosis?

This technique uses computer systems and algorithms of machine learning to detect the lung cancer in early stage by analyzing data’s of medical images to provide effective diagnosis and results.

  1. Where Automated Lung Cancer Diagnosis is used?

This technique is basically used in medical settings for diagnosing lung cancer mainly in the fields of healthcare institutions and radiology department. This technique is applicable in the area where there is usage of CT scans which do analyzing based on the algorithms of Artificial Intelligence for detecting lung cancer.

  1. Why Automated Lung Cancer Diagnosis is proposed? Previous technology issues

Detection of lung cancer using the method computer tomography with images uses deep learning techniques to analyze those images. But this method has some inaccurate detection of results because of the following reasons:

Lack of image quality and Resolution: The CT scans used for detection should have high resolution and quality. If the scan result is of low quality then it will have noise and minimizing the effectiveness of model.

Feature selection and Algorithmic impact: The feature’s accuracy depends on the algorithm you choose and the amount of feature selected. The accuracy of result will not depend on the assumptions you make about the characteristics of lung cancer.

Low image generation and Resolution in biomedical domain: The technology followed previously for image generation has issues in accuracy and processing time. So it has problem in generating images of high resolution.

Sensitivity and Model evaluation: Sensitivity fluctuations occurring on Negative Predictive Value and F-1 score at the high and low False Positive rates along with False Negative rates will reduce the performance of system.

  1. Algorithms / Protocols

Moving on to the next section, here we are going to discuss about the algorithms used for this technology. The algorithms provided for diagnosing lung cancer automatically to overcome the previous issues faced by it are: “Chaotic Crow Search Algorithm and Random Forest (CCSA-RF)”, “Sparse Convolutional Neural Network with Probabilistic Neural Network” (SCNN-PNN) and “Multi-space Image Reconstruction with Grey Level Co-occurrence Matrix” (MIR-GLCM).

  1. Simulation results / Parameters

The approaches which were proposed to overcome the issues faced by diagnosing lung cancer automatically in the above section are tested using different methodologies to analyze its performance. The comparison is done by using metrics like Accuracy, Precision, F1-Score, Sensitivity and Specificity.

  1. Dataset LINKS / Important URL

You can use the link provided below to gain extra knowledge about this topic based on Lung cancer detection.

  1. Automated Lung Cancer Diagnosis Applications

In this application section we are going to learn about the systems which are making use of this automatic lung cancer detection technique using machine learning algorithm. This system follows early detection of lung cancer by analyzing image by getting the data of cancer patterns from radiologists. This technique makes the diagnosis process easier, minimize human error also helps produce effective and timely treatment plan for the patients affected with lung cancer. It can also be used in large scale screening which helps in improving the healthcare efforts of fighting lung cancer.

  1. Topology

Here you are going to learn about the different choices of topologies which can be used in automatic lung cancer detection technique based on Artificial Intelligence. This technique basically has neural architecture like Convolutional Neural Network (CNN) which is used in image recognition, classification and feature extraction.

  1. Environment

Environment is the circumstances in which the system functions properly. Here in this case of lung cancer detection it is majorly operated in the environment of medical imaging. This system is used in healthcare sector. They analyze the medical data of lung cancer image with the help of computing infrastructure.

  1. Simulation Tools

Here we provide some simulation software for previous works, which is established with the usage of python software of version 3.11.4

  1. Results

When you complete reading this paper you are now more familiar with the technology of Automatic Lung Cancer Detection using Machine learning, what it actually does, algorithms used in this paper to enhance is operation, the places in which it is used also about the issues faced by this system.

Lung Cancer Detection Research Topics

  1. Multimodal Biosensor System for Exhaled Breath Based Lung Cancer Diagnosis
  2. Deep Learning-Assisted Lung Cancer Diagnosis from Histopathology Images
  3. A Hybrid Feature Based Model Development for Computer Aided Diagnosis of Lung Cancer
  4. A Review on Diagnosis of Lung Cancer and Lung Nodules in Histopathological Images using Deep Convolutional Neural Network
  5. Genome Sequence Identification using Deep Learning for Lung Cancer Diagnosis
  6. Non-Small Cell Lung Cancer Diagnosis Using kNN and Logistic Regression
  7. Comparison of CNN Models in Non-small Lung Cancer Diagnosis
  8. Enhancing Lung Cancer Diagnosis with Machine Learning Methods and Systematic Review Synthesis
  9. Lung Cancer Diagnosis and Classification Using Hybrid Neural Network Techniques
  10. Smart Health Care Management System for Diagnosis of Lungs Cancer
  11. An Efficient Deep Learning Model based Diagnosis System for Lung Cancer Disease
  12. LCDctCNN: Lung Cancer Diagnosis of CT scans Images Using CNN Based Model
  13. A comparative study for lung, colon and breast cancer diagnosis using different convolutional neural networks
  14. Lung Cancer Subtype Diagnosis by Fusing Image-Genomics Data and Hybrid Deep Networks
  15. Diagnosis of Lung Cancer Nodules in CT scans Images using Fuzzy Neural Network
  16. An Empirical Investigation of the Use of Artificial Neural Networks (ANN) for Lung Cancer Disease Diagnosis
  17. Diagnosis of Brain tumors, Lung tumors, and Breast Cancers by a Patch Fractal Antenna for Wireless Sensor
  18. HViT4Lung: Hybrid Vision Transformers Augmented by Transfer Learning to Enhance Lung Cancer Diagnosis
  19. Harnessing Continual Learning in Deep Neural Networks for Improved Lung Cancer Diagnosis
  20. MedNet: A Segmentation Algorithm for Effective Lung cancer Diagnosis
  21. Deep Fuzzy Cognitive Map methodology for Non-Small Cell Lung Cancer diagnosis based on Positron Emission Tomography imaging
  22. A whole-slide pathology segmentation framework for the diagnosis of non-small cell lung cancer
  23. Big Data Analytics on Lung Cancer Diagnosis Framework with Deep Learning
  24. Healthcare As a Service (HAAS): CNN-based cloud computing model for ubiquitous access to lung cancer diagnosis
  25. Artificial intelligence in lung cancer diagnosis and prognosis: Current application and future perspective
  26. Conducting polymer composite-based bio-sensing materials for the diagnosis of lung cancer: A review
  27. Chinese expert consensus on the diagnosis and treatment of bone metastasis in lung cancer (2022 edition)
  28. Factors associated with late-stage diagnosis and overall survival for lung cancer: An analysis of patients treated in a Brazilian hospital and a US-hospital from 2009 to 2019
  29. Evaluation of pre-diagnostic blood protein measurements for predicting survival after lung cancer diagnosis
  30. Comparative study of deep learning models on the images of biopsy specimens for diagnosis of lung cancer treatment
  31. WS-LungNet: A two-stage weakly-supervised lung cancer detection and diagnosis network
  32. Brief Report: A Multidisciplinary Initial Workup for Suspected Lung Cancer as Fast-Track Intervention to Histopathologic Diagnosis
  33. Intraoperative Versus Preoperative Diagnosis of Lung Cancer: Differences in Treatments and Patient Outcomes
  34. Exploring the micro biome: Uncovering the link with lung cancer and implications for diagnosis and treatment
  35. Emergency presentation prior to lung cancer diagnosis: A national-level examination of disparities and survival outcomes
  36. An intelligent algorithm for lung cancer diagnosis using extracted features from Computerized Tomography images
  37. Lobectomy for Suspected Lung Cancer without Prior Diagnosis
  38. New bidirectional recurrent neural network optimized by improved Ebola search optimization algorithm for lung cancer diagnosis
  39. Association between duration of smoking abstinence before non-small-cell lung cancer diagnosis and survival: a retrospective, pooled analysis of cohort studies
  40. A Patient-Centered Model of Fast-Track Lung Cancer Diagnosis
  41. Diagnosis of lung cancer following emergency admission: Examining care pathways, clinical outcomes, and advanced NSCLC treatment in an Italian cancer Center
  42. Stage at Diagnosis Following Delay to Interval Scans for Indeterminate Nodules in Lung Cancer Screening: An Observational Study Examining the Outcomes of CHEST Expert Panel Recommendations
  43. Quitting smoking improves two-year survival after a diagnosis of non-small cell lung cancer
  44. Evaluating the Optimal Time between Diagnosis and Surgical Intervention for Early-Stage Lung Cancer
  45. Diagnosis and treatment of lung cancer in Denmark during the COVID-19 pandemic
  46. The Economic Potential of Smoking Cessation Interventions at the Point of Diagnosis of Non–Small Cell Lung Cancer
  47. Association between Quality of Life Questionnaire at Diagnosis and Survival in Patients with Lung Cancer
  48. Performance of EUS-FNA and EUS-B-FNA for the diagnosis of left adrenal glands metastases in patients with lung cancer: A systematic review and meta-analysis
  49. Combinations of plasma cfDNA concentration, integrity and tumor markers are promising biomarkers for early diagnosis of non-small cell lung cancer
  50. Peripheral but not axial muscle mass is associated with early mortality in bone metastatic lung cancer patients at diagnosis