Journal article Open Access

Brain Tumor Detection using Deep Learning

Rutuja Gugale; Pratiksha Sonar; Anagha Mandekar; Sonali Ubale; Vaishali Latke

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  "DOI": "10.35940/ijrte.C4644.099320", 
  "container_title": "International Journal of Recent Technology and Engineering (IJRTE)", 
  "language": "eng", 
  "title": "Brain Tumor Detection using Deep Learning", 
  "issued": {
    "date-parts": [
  "abstract": "<p>Nowadays the leading techniques for diagnosing and revealing the different diseases are image processing. And there is an increase in the cases of cancer these days. The unrestricted development of cells cause&rsquo;s lumps which leads to brain tumor also called glioblastoma. There are mainly two types of tumor benign which has covering over the tumor and malignant is the one which spreads throughout the places. Earlier the development of unrestricted cells used to be diagnosed by doctors physically through monitoring the image by which the results were not used to be precise sometimes. But time along boarding of medical fields lead to different medical facilities by which the results could be precise. The broadly approach method of imaging that scrutinizes the internal structure of the human race is Magnetic resonance Imaging. This approach of imaging techniques is also used for detecting brain tumors. The detection of glioblastoma processes has machine vision methods such as Image pre-processing, Segmentation in Image, Feature extraction and classification. Several image segmentation and image classification techniques are available for detecting tumor of the brain. Convolution neural networks (CNN) based classifiers are proposed to prevail the limitations. This CNN is such a classifier which is used to differentiate between the competent data and the trail data, from which the results could be obtained.</p>", 
  "author": [
      "family": "Rutuja Gugale"
      "family": "Pratiksha Sonar"
      "family": "Anagha Mandekar"
      "family": "Sonali Ubale"
      "family": "Vaishali Latke"
  "page": "714-717", 
  "volume": "9", 
  "type": "article-journal", 
  "issue": "3", 
  "id": "5851912"
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