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early stage cancer.pptx

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early stage cancer.pptx

  1. 1. GUIDE : MR. AMRUTH RAJ ASST.PROF DEPT : CSE PREPARED BY : D.SAI KUMAR (20GE5A0501) SHAIK WAJATH HUSSAIN (19GE1A0512)
  2. 2.  Introduction  Existing system  Disadvantages  Proposed system  Advantages  System requirements
  3. 3.  The goal is to increase the proportion of cancers identified at an early stage, allowing for more effective treatment to be used and based on different Machine learning algorithms.
  4. 4.  Lung cancer is mainly triggered by cigarette smoke. Smoke that penetrates into the lungs causes damage to the lung tissue.  In nonsmokers, lung cancer may be induced by radon radiation, second hand smoking, air contamination or other causes.  Heredity is another source of lung cancer, as well. While lung cancer (malignant growth) is hard to diagnose and cure, it may be avoided or treated in the early stages.
  5. 5.  ML techniques handle the data and find the right model. The main category of ML methods is supervised learning (SL), unsupervised learning (USL), and reinforcement learning (RL).  All SL is a form of classification or Regression.  USL is valuable when the information is uncertain but it needs to be investigated.  RL can be model-free or model-based reinforcement learning.
  6. 6.  No security for user’s data. No authentication or security provided  High resource costs needed for the implementation.  Medical Resonance images contain a noise caused by operator performance which can lead to serious inaccuracies classification.
  7. 7.  To construct an efficient and accurate ML model for early LC, the model can be developed with the following parameters.  Data should be collected from large and highly qualified authorized centers. “e.g.” www.cancerimagingarchive.net  Data collected should be preprocessed by a powerful technique such that no important data is lost. Highly correlated Features with the output should be identified for best results.  Using the Hybrid ML model, early prediction of LC can produce accurate results.  Several ML tools and various platforms can be made available for researchers to provide good results.
  8. 8.  High accuracy, fastest prediction, and consistency of results. .  It can segment the lung, heart, and diabetes regions from the data accurately.  It is useful to classify the lung Tumor from trained data set for accurate detection.
  9. 9.  Hardware:  1.Windows 8,10 64 bit  2. 4GB ram  Software:  1. Python [Latest version]  2. Anaconda Navigator

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